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FP7-ICT-GA 619732
`SPATIAL-SPECTRAL FLEXIBLE OPTICAL NETWORKING ENABLING SOLUTIONS FOR A SIMPLIFIED AND EFFICIENT
SDM
SPECIFIC TARGETED RESEARCH PROJECT (STREP) INFORMATION & COMMUNICATION TECHNOLOGIES (ICT)
Evaluation of the INSPACE system benefits
D2.3
Document Type : Deliverable
Dissemination Level : PU
Lead Beneficiary : AIT
Contact Person : Ioannis Tomkos
Delivery Due Date : 31/01/2017
Submission date : 21/03/2017
Contributing institutes : AIT
Authors : Behnam Shariati (AIT)
This deliverable reports on the benefits of introducing of the INSPACE solutions in an optical network. In this
deliverable we provide extensive studies and analysis with the purpose to: a) identify the system performance in
different scenarios, b) calculate the expected cost benefits from the adoption of the INSPACE solutions, and c)
evaluate the energy consumption of the nodes in networks. In all cases, comparisons are made with legacy
technologies with the main purpose to identify the expected economic benefits and the improvements in power
consumption.
Ref. Ares(2017)3331579 - 03/07/2017
INSPACE D2.3 Evaluation of the INSPACE system benefits Version 3.0
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Revision History
No. Version Author(s) Date
1 1.0 Behnam Shariati (AIT) 16/02/2017
Comments: Initial Release, ToC
2 2.0 Behnam Shariati (AIT) 28/02/2017
Comments: Submitted for internal review
3 3.0 Behnam Shariati (AIT) 21/03/2017
Comments: Final submitted version
4 4.0
Comments:
5 5.0
Comments:
6 6.0
Comments:
7 7.0
Comments:
Comments:
Participants
The INSPACE Project Consortium groups the following Organizations:
No Partner Name Short Name Country
1 Optronics Technologies S.A. OPT Greece
2 Telefonica Investigation y Desarrollo TID Spain
3 The Hebrew University of Jerusalem HUJI Israel
4 Research and Education Laboratory in Information Technologies AIT Greece
5 Optoscribe Ltd. OPTOSCRIBE United Kingdom
6 Center for Research and Telecommunication Experimentation for
Networked Communities CN Italy
7 Aston University ASTON United Kingdom
8 Opsys Tech Ltd. OPSYS Israel
10 W-Onesys, S.L. WONE Spain
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List of Tables
Table 2.1 Values of selected ChBW with the amount of corresponding spectral contents supported by two
WSS technologies ............................................................................................................................................................. 12
Table 2.2 Maximum point-to-point transmission reach (km) with the indicated baud rate, ChBW, and
modulation format ........................................................................................................................................................... 12
Table 3.1 Estimated cost of HPC WSSs ....................................................................................................................... 18
Table 3.2 Relationship between maximum number of ports and number of LCoS SLM pixels. N_px is the
total number of pixels in the steering direction. Px_port is the number of pixels per port................................. 19
Table 3.3 Loss of splitters and switches with different splitting ratios to be used in realizing MCS with various
port counts ........................................................................................................................................................................ 20
Table 3.4 The required number of WSSs and their port count ................................................................................ 23
Table 3.5 Cost of the A/D modules presented in Figure 3.9 to Figure 3.11 ......................................................... 24
Table 3.6 CMOS power dissipation based on Fig. 2 of [7] ....................................................................................... 26
Table 3.7 Power-limited reach based on total consumption per 600 Gb/s module ............................................. 27
Table 3.8 Total power consumption per 600 Gb/s module. .................................................................................... 27
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List of Figures
Figure 2.1 Cumulative density function (CDFs) of the assumed traffic profiles with (a) fixed σ=200 Gbps and
µ=700, 1150, and 1600 Gbps, (b) fixed µ=1248 Gbps, and σ=96, 192, 384, 768 Gbps. ..................................... 10
Figure 2.2 Blocking probability in terms of average number of live connections per A/D node for three
profiles of traffic forming of small, large and medium size demands which their distributions are plotted in
Fig. 13-5: a) µ=1600, b) µ=1150, and c) µ=700 Gbps. .............................................................................................. 11
Figure 2.3 BP in terms of standard deviation when µ=1248 Gbps and average number connection per A/D
node is 112. ........................................................................................................................................................................ 11
Figure 2.4 Average spectrum utilization per link per fiber under different spatial Sp-Ch switching paradigms
considering current WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of
32, 19.5 and 7 Gbaud, respectively in terms of load. .................................................................................................. 13
Figure 2.5 Average data occupancy in percentage considering current WSS technology for (a) 50 GHz, (b)
37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 32, 19.5 and 7 Gbaud, respectively in terms of load. .... 13
Figure 2.6 Average spectrum utilization per link per fiber under different spatial Sp-Ch switching paradigms
considering improved resolution WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a
baud rate of 41, 28.5 and 16 Gbaud, respectively in terms of load. ......................................................................... 15
Figure 2.7 Average data occupancy in percentage considering finer resolution WSS technology for (a) 50
GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 41, 28.5 and 16 Gbaud, respectively in terms of
load. .................................................................................................................................................................................... 15
Figure 2.8 Average spectrum utilization per link per fiber for J-Sw considering: (a) the current WSS
technology which requires 18 GHz for guard band and (b) finer resolution WSS which requires 9 GHz for
guard band. Five values of ChBWs are assumed for the simulations. The amount of spectral contents that can
be utilized considering different WSS technologies is provided in Table I. ........................................................... 16
Figure 3.1 single-carrier transceiver ............................................................................................................................... 17
Figure 3.2 Integrated spatial super-channel transceiver with seven integrated sub-channels .............................. 17
Figure 3.3 Relative cost/price of WSS required for a given number of ports........................................................ 18
Figure 3.4 Port-reconfigurable MN WSS ................................................................................................................... 19
Figure 3.5 A possible design of MC-EDFA ................................................................................................................ 20
Figure 3.6 Average spectrum utilization per link per fiber in terms of total offered load to the network in
Pbps .................................................................................................................................................................................... 21
Figure 3.7 Total relative cost of the network considering 4 different offered load for 5 SDM deployments
compare to an equivalent parallel system using 100G TRx. ...................................................................................... 21
Figure 3.8 Route and Select ROADM architectures for (a) Ind-Sw and (b) J-Sw in an SDM scenario with S=2.
In (a) we show all internal connectivity from the ‘East’ ingress WSS. LCs are supported if all solid, dashed and
dotted connections exist. Ind-Sw without LCs requires solid and dashed lines. ................................................... 23
Figure 3.9 Two A/D module architectures for ROADM CD operation. Only ‘Drop’ WSS are shown. ......... 23
Figure 3.10 Two M×N WSS-based A/D module architectures for ROADM CDC operation. Only ‘Drop’
WSS are shown. ................................................................................................................................................................ 23
Figure 3.11 Three MCS-based A/D module architectures for ROADM CDC operation. Only ‘Drop’ MCS
are shown. .......................................................................................................................................................................... 24
Figure 3.12 Relative cost of the components wrt 1×9 WSS ..................................................................................... 24
Figure 3.13 Total relative cost (TRC) of different ROADM implementations. .................................................... 25
Figure 3.14 MIMO-DSP complexity of FDE ............................................................................................................. 26
Figure 3.15 Predicted (a) power consumption per mode and (b) total consumption per 6×100Gb/s module,
for 15 nm CMOS.............................................................................................................................................................. 27
Figure 3.16 Predicted (a) power consumption per mode and (b) total consumption per 6×100Gb/s module,
for 15 nm CMOS.............................................................................................................................................................. 27
Figure 3.17 Power consumption of control unit of WSSs needed for realizing SDM nodes .............................. 28
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Table of contents
Revision History ................................................................................................................................................ 2
Participants ......................................................................................................................................................... 2
List of Tables ..................................................................................................................................................... 3
List of Figures .................................................................................................................................................... 4
Table of contents ............................................................................................................................................... 6
Executive summary ........................................................................................................................................... 7
1. Introduction.......................................................................................................................................... 8
2. Networking level performance evaluations ................................................................................... 10
2.1. Impact of traffic profile on the performance of SDM switching schemes ............................... 10
Simulation environment and assumptions ........................................................................................ 10
Results and discussions ......................................................................................................................... 10
2.2. Impact of spectral and spatial granularity on the performance of INSPACE proposed
switching schemes ............................................................................................................................................. 12
Simulation environment and assumptions ........................................................................................ 12
Results and discussions ......................................................................................................................... 13
3. Cost analysis and power consumption evaluations ...................................................................... 17
3.1. Cost benefit quantification of INSPACE proposed solutions ................................................... 17
Cost model .............................................................................................................................................. 17
Network-level cost analysis .................................................................................................................. 20
3.2. Comparison of CD(C) ROADM architectures for SDM networks........................................... 22
Motivation ............................................................................................................................................... 22
ROADM architectures and cost analysis ........................................................................................... 22
Discussion on the total relative cost of ROADM implementations ............................................ 25
3.3. Power consumption of MIMO processing and its impact on the performance of SDM
networks .............................................................................................................................................................. 25
Motivation ............................................................................................................................................... 25
Power consumption of MIMO processing for SDM ...................................................................... 26
Network-wide performance evaluation of integrated transceiver utilizing different MIMO-
DSP modules ......................................................................................................................................................... 28
3.4. Control unit power consumption of WSSs used for realizing SDM nodes .............................. 28
Conclusions ...................................................................................................................................................... 29
Annex – abbreviations and acronyms .......................................................................................................... 30
References ........................................................................................................................................................ 32
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Executive summary
The INSPACE project aims at extending the established spectral flexibility concept to the space domain, by
proposing novel switching solutions that simplify the Super-Channel (Sp-Ch) allocation mechanisms and route them
transparently in an optical network. Three switching paradigms have been identified and developed in INSPACE,
which guarantee various levels of spatial granularity (i.e. grouping of the spatial channels) exploiting wavelength
selective switches (WSSs) of different port counts. The core novelty is the development of a high port count (HPC)
WSS capable of switching all spatial dimensions jointly with the expected benefit of reducing the complexity/cost as
well as providing appropriate switching element when highly coupled transmission media (i.e. few mode fibers
(FMFs)) are in place. The proposed switching element trades off some level of flexibility for the sake of
architectural simplicity and satisfying, at the same time the major switching requirements. Therefore, it is very
important to investigate it in different scenarios showcasing its expected benefits in terms of performance, cost, and
power consumption as is promised in T2.3 of INSPACE.
In this deliverable we thoroughly investigate SDM networks utilizing the proposed switching schemes and
compare it with the conventional schemes with the purpose of showing their benefits/drawbacks in different
networks and under various traffic profiles. We show that the performance of Joint Switching (J-Sw) paradigm
converges to that of Independent Switching (Ind-Sw) as traffic increases. In other words, we show that the J-Sw is a
favorable switching scheme for networks with large demands, where the size of the demands is comparable with the
capacity of spatial Sp-Chs, while Ind-Sw and Fractional Joint Switching (FrJ-Sw) cases are favorable options for
networks with high levels of traffic diversity. We further show that the performance of J-Sw can be improved when
WSSs capable of switching the spectral content with finer spectral granularity are in place. For this reason, we report
extensive computer simulations comparing SDM proposed switching schemes with conventional approaches. We
take into account WSS capable of switching at various spectral granularity as well as WSSs with two levels of
spectral resolution (i.e. the current technology and an improved resolution WSS technology).
In addition, we perform a network wide cost analysis considering spatially integrated elements to showcase the
cost benefit of SDM solutions. For this purpose, we develop a cost model based on the current available technology
and complementary estimations provided by INSPACE industrial partners. We show that the INSPACE switching
solution can bring up to 50% cost savings in the development of reconfigurable optical add drop multiplexer
(ROADM) line side. We further show that utilizing J-Sw case, cost benefits can be expected from other components
like spatially integrated TRx and amplifiers. We also conclude that exploiting J-Sw scheme can bring huge cost
savings for the add/drop unit of ROADMs. We propose several colorless directionless (CD) and CD contention-less
(CDC) architectures where J-Sw case can contribute to extra cost savings. Finally, we present results on the power
consumption benefits of SDM proposed nodes, more pronounced for the realization of the control unit of the HPC
WSSs.
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1. Introduction
Traffic in telecommunication networks is growing at an annual rate of 20-60% and is approaching the capacity
limits of the single-mode fiber (SMF) [1]. Space division multiplexing (SDM) over multi-core fibers (MCF), multi-
mode fibers (MMF), few-mode multi-core fibers (FM-MCF), or even bundles of SMFs would allow the network
capacity to scale to orders of magnitude higher than what can be achieved with an SMF-based network
infrastructure.
SDM systems would also reduce the cost per bit delivered to the end users, compared with parallel-fiber systems, by
sharing network elements, taking advantage of dense optical integration (SDM transceivers (TRx), switching
elements and in-line amplifiers) among different spatial dimensions. However, the denser the integration of the
spatial channels it becomes, the more significant is the crosstalk (XT) interactions among them, as in the case for
strongly-coupled MCFs and FMFs. Such spatial XT is expected to be mitigated by multiple-input multiple-output
(MIMO) digital signal processing (DSP) at the TRx side at the expense of higher complexity and power
consumption.
SDM TRx integration allows generating spatial super-channels (Sp-Ch), which can be defined, similar to
spectral Sp-Chs, as the aggregation of signals modulated on a certain optical carrier across a number or all of the
spatial dimensions of an SDM transmission medium [2]. Spectral and spatial Sp-Ch allocation policies were
compared in [2] for single and multiple modulation format transmission, showing that even though spectral Sp-Ch
transmission generally leads to superior network performance, resulting from increased flexibility, networks based
on spatial Sp-Ch allocation can benefit from cost savings related to the possibility of sharing switching elements
among spatial dimensions.
Three switching strategies were proposed in INSPACE: (a) independent switching (Ind-Sw): all spectral slices
and spatial dimensions can be independently directed to any output port; (b) joint switching (J-Sw): all spatial
dimensions are treated as a single entity, while spectral slices can be freely switched by the WSS; and (c) fractional
joint switching (FrJ-Sw): a hybrid approach in which a number of subgroups of G spatial dimensions, as well as all
spectral slices, can be independently switched to all output ports. The last two paradigms can be categorized as
spatial group switching (SG-Sw) solutions since the spatial resources are switched in groups rather than
independently, as in the case of Ind-Sw. Note that several spatial switching granularities result from different levels
of grouping of the spatial dimensions: from G=1, which assumes individual fibers, thus corresponding to the Ind-Sw
case and offering the finest spatial granularity, all the way through to G = S, which considers all spatial dimensions
as one spatial group, thus corresponding to the J-Sw case and offering the coarsest spatial granularity. In planning an
SDM network, the choice of one of the above SDM switching paradigms has a considerable impact on both the
flexibility of the implemented resource allocation (RA) policies and the switching infrastructure deployment cost.
SDM amplifier integration has also been investigated. Various schemes for pumping of Erbium Doped Fibers
(EDF) have been explored: i) core pumping using individual single-mode pump diodes, ii) shared pump, and iii)
cladding-pumping using a single high power (over 1 W) multimode pump laser diode [3]. Cladding pumping
requires fewer optical components and has the potential to allow the use of low-cost, energy efficient multimode
diodes. Bundled EDFs, consisting of bundles of identical single-core EDFs, are one candidate amplification medium
for SDM EDF amplifiers (EDFA). Few-mode and multi-core EDFAs enable cladding pumping with a single laser
diode. The non-uniformity of the modal gain and the noise figure (NF) between modes/cores are issues that need to
be further improved, but FM- and MC-EDFAs are indeed promising solutions for cost-effective SDM network
deployments since mode mixing can be made negligible [3].
In this deliverable, we present a comparative performance evaluations of SDM networks based on INSPACE
proposed switching solutions and the legacy approaches. In particular, we investigate the impact of spatial switching
granularity and spectral switching granularity on the performance and the implementation cost of SDM switching
schemes. The spectral switching granularity is related to the capabilities of wavelength selective switches (WSSs).
In order to reflect the impact of different technologies on the performance of INSPACE proposed switching
schemes, we consider two WSS technologies for handling of the SDM switching paradigms: 1) the current WSS
realization, 2) WSS technology with a factor-two resolution improvement. Moreover, with the aim of exploring the
performance of SDM switching schemes in different parts of networks (i.e. national, regional, or inter-data center
networks), we investigate the impact of different traffic profiles on their performance. Then, after deriving a cost
model for all network elements, we present cost analysis of SDM networks with the main purpose of showcasing the
benefits brought about by INSPACE proposed switching nodes. For the cost analysis, we consider spatial and
spectral Sp-Ch integrated TRx, amplifier and node implementations for Ind-Sw (spectral and spatial Sp-Ch), J-Sw
and FJ-Sw (spatial Sp-Ch). We also compare them to a parallel-fiber system based on 100G transmission. We
quantify the values of cost savings, under given assumptions, for several cases of parallel-fiber system deployments,
and discuss how the cost of the system can be further reduced. Furthermore, some efforts are made to estimate the
power consumption of different SDM approaches as well as the power consumption due to the control unit of
INSPACE proposed WSSs.
The rest of the deliverable is structured as follows:
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In chapter 2, we present extensive computer simulation results evaluating the performance of INSPACE
proposed switching paradigms under different traffic profiles and considering various spectral and
spatial granularity as well as two different WSS technologies.
In chapter 3, we present cost and power consumption analysis showcasing the benefits of INSPACE
proposed schemes in comparison with the conventional approaches.
Finally, we present the conclusions of the deliverable.
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2. Networking level performance evaluations
2.1. Impact of traffic profile on the performance of SDM switching schemes
A detailed comparison of SDM switching schemes was reported in D5.2 of INSPACE for an offline network
planning scenario. The study considered a specific traffic matrix with a limited number of demands. The comparison
revealed that under high traffic load the networking performance in terms of spectrum utilization for the case of FrJ-
Sw and J-Sw (i.e. spatial group switching options) almost converges to that of Ind-Sw.
The assumed traffic profile (i.e. large aggregated demands) is the most typical traffic profile in core networks
today, as we have traffic aggregation at the edge of the network. However, due to the introduction of application-
centric services and dependency of traffic increase on i) the type of network (access/metro/core), ii) the kind of
connectivity services offered by the network (e.g. based on 4G/5G radio, FTTX, etc.), and iii) the type of offered
applications (e.g. file sharing, video conferencing), traffic de-aggregation will be a possible networking policy.
Therefore, in this section, we want to study the cases in which lightpaths might be established based on i) a large
number of small demands (typically seen in regional part of networks), ii) a small number of large demands
(applicable for example in inter-datacenter communications), and iii) any combinations of last two options (found
e.g. in national scale networks serving a heterogeneous type of traffic demands).
In this section, we thoroughly evaluate the impact of various traffic profiles and dimensioning approaches on the
performance of SDM switching paradigms, in an online operation scenario. We show that the performance of the
three SDM switching paradigms is highly dependent on the traffic profile. While J-Sw shows similar performance as
Ind-Sw for large demands, it presents a reduced performance when network is fed by a large number of small
demands. However, we show that the performance of the J-Sw can improve substantially when the spectral
granularity of the switching paradigm is reduced.
Simulation environment and assumptions
In this study we use the Spanish backbone model of Telefónica which is the reference network considered for
INSPACE performance evaluations studies. It comprises 30 nodes (average nodal degree 3.7, max. 5), 14 of which
with add/drop capability (A/D), as well as 56 links with an average length of 148 km. In order to have a fair
comparison among the three SDM switching paradigms, regardless of any transmission medium related performance
constraints, bundles of 12 SMFs were considered for all links in the network.
Moreover, based on the network characteristics and the related performance evaluation studies [1], DP-8QAM at
32-Gbaud was chosen as the modulation format offering the best compromise between transmission reach and
spectral efficiency. A 50-GHz channel spacing is used.
According to the above and considering an available spectrum per fibre equal to 4.8 THz (C-band) on the ITU-T
12.5-GHz grid, discrete event simulation studies were carried out for the purpose of performance evaluation
exploiting the online simulator reported in D5.2 of INSPACE. In the simulator, the routing, space, and spectrum
allocation (RSSA) problem is solved with a k-shortest path (k = 3) and using a spatial and spectral resource
allocation algorithm, that follows a first-fit strategy, starting with the shortest computed path. The load generation
followed a Poisson distribution process. Traffic demands for each source-destination pair were generated randomly
following a normal distribution with mean µ and standard deviation σ over the range of study, namely 50 Gbps to
2.25 Tbps. Blocking Probability (BP) was used as a quantitative performance measure.
Figure 2.1 Cumulative density function (CDFs) of the assumed traffic profiles with (a) fixed σ=200 Gbps and µ=700, 1150, and 1600 Gbps, (b) fixed µ=1248 Gbps, and σ=96, 192, 384, 768 Gbps.
Results and discussions
In the first part of the study, we consider three traffic profiles corresponding to the three previously mentioned
cases: small, large and medium-size demands. The distribution of demands for the 3 different mean demand values
and fixed σ is shown in Figure 2.1(a), while the effect of the deviated demands over a fixed mean value is shown in
0%
20%
40%
60%
80%
100%
0 576 1152 1728 2304
Perc
enta
ge o
f C
DF
Average Bit Rate per Connection (Gbps)
σ=96 Gb/s
σ=192 Gb/s
σ=384 Gb/s
σ=768 Gb/s
(b)
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Figure 2.1(b). The lower and upper mean values were chosen according to the following: a) for µ=700 Gbps, 98% of
demands requires less than half of the 12 spatial dimensions (i.e. bundles of SMFs (BuSMFs) in our case study) to
be allocated, b) for µ=1600 Gbps, we have large aggregated demands that result in more than 98% of them requiring
more than half of the 12 spatial dimensions to be allocated. The three traffic profiles of Figure 2.1(a) are used to
obtain the results shown in Figure 2.2. It is noted that in all cases traffic dimensioning is realized by varying the
number of live connections per A/D node.
Figure 2.2 Blocking probability in terms of average number of live connections per A/D node for three profiles of traffic forming of small, large and medium size demands which their distributions are plotted in Fig. 13-5: a) µ=1600, b) µ=1150, and c)
µ=700 Gbps.
For high mean traffic demands (Figure 2.2(a)), the three switching paradigms show the same performance. Since
most demands require more than half of the spatial resources, the unutilized resources of FrJ-Sw and Ind-Sw cases
cannot be allocated thus leading to the same results as the J-Sw case. For a traffic profile with diverse and relatively
medium-size demands (Figure 2.2(b)), FrJ-Sw and Ind-Sw start performing better than J-Sw in terms of BP, since
now part of the incoming demands that require less than 6 spatial dimensions to be allocated can fit within the free
spatial resources that FrJ-Sw and Ind-Sw enable them to use. For small mean traffic demands (Figure 2.2(c)) the
performance difference between J-Sw and FrJ-/Ind-Sw is more pronounced, since the allocation options in space
dimension are increased and small demands can fit in available spatial slots.
Previous discussion on Figure 2.2 strongly suggests that the optimal switching paradigm for an SDM network, in
fact, depends on the nature of its traffic, specifically whether there is a prevalence of relatively small or large
demands. However, since most of the demands in the core networks are aggregated traffics, J-Sw would be a
suitable choice, considering its cost benefit which will be shown in the next chapters.
In order to see the impact of traffic diversity on the performance of SDM switching paradigms, a complementary
set of simulations is carried out, where the traffic dimensioning is done by varying σ and keeping µ and the number
of live connections fixed (Figure 2.1(b)). Note, in this study, the total offered load to the network during the whole
range of the simulation is fixed to 14*112*1248 Gbps = ~1.95 Pbps.
Figure 2.3 BP in terms of standard deviation when µ=1248 Gbps and average number connection per A/D node is 112.
Results plotted in Figure 2.3 show that at the beginning the performance of three SDM switching is the same
(similar to Figure 2.2(a)), and by increasing σ which is equivalent to the diversity level of the traffic profile, Ind-Sw
and FrJ-Sw show a remarkable improvement which justifies their suitability for networks with high level of
diversity in their traffic profile. In conclusion, the performance of the three SDM switching paradigms is highly
dependent on the traffic profile. While J-Sw shows similar performance as Ind-Sw for large demands, it presents a
reduced performance when network is fed by a large number of small demands. J-Sw shows better performance for
large demands, because when the load increases the capacity of a spatial Sp-Ch becomes comparable to the demand
that has to be served. Therefore, if we can form spatial Sp-Chs with lower capacity spreading across all spatial
dimensions, smaller demands can fill most of Sp-Ch container and, thus, reduce the unutilized spectral and spatial
resources compared to the spatial Sp-Chs with higher capacity. One of the required technology to realize narrower
spatial Sp-Chs is the WSSs with finer spatial granularity capable of switching narrower spectral bands. In next
0.0001
0.001
0.01
0.1
1
75 100 125 150 175 200 225 250 275
BP
Average Number of Live Connections per A/D Node
J-Sw
FrJ-Sw
Ind-Sw(a)0.0001
0.001
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75 100 125 150 175 200 225 250 275
BP
Average Number of Live Connections per A/D Node
J-Sw FrJ-Sw Ind-Sw(b)
0.0001
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BP
Average Number of Live Connections per A/D Node
J-Sw
FrJ-Sw
Ind-Sw (c)
0.0001
0.001
0.01
0.1
1
24 48 96 192 384 768
BP
Standard Deviation of the Traffic Profile (Gbps)
J-Sw
FrJ-Sw
Ind-Sw
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section, we investigate the impact of spatial and spectral granularity on the performance of SDM networks based on
spatial Sp-Ch switching.
2.2. Impact of spectral and spatial granularity on the performance of INSPACE proposed switching schemes
Assuming bundles of 12 SMFs, we showed in D5.2 of INSPACE that the performance of SG-Sw cases becomes
similar to that of Ind-Sw as the total offered load to the network (hereinafter referred to as load) increases. However,
SG-Sw cases showed a reduced performance for low values of load. In the previous section, we further showed that
SG-Sw paradigms with lower G values perform well for networks with high level of traffic diversity, while J-Sw is
favorable for networks with large demands. Note that in the previous studies, Ind-Sw, J-Sw and FrJ-Sw with G = 3
were only examined with a fixed spectral channel width (ChBW) —defined as the spectrum over each of the spatial
dimensions used to allocate the spatial Sp-Ch constituents— equal to 50 GHz. In this section, in addition to the three
levels of spatial switching granularity studied previously, we examine FrJ-Sw with G equal to 2, 4, and 6. The
spectral switching granularity is related with the capabilities of WSSs. Current WSS technology allows occupying
32-GHz on a 50 GHz grid due to channel pass bandwidth imposed by the WSS resolution. The same WSS resolution
can allocate finer channels, typically according to a 6.25 GHz grid, i.e., 25.75 GHz can be provisioned on 43.75
GHz, 19.5 GHz on 37.5 GHz, 13.25 GHz on 31.25, or 7 GHz on 25 GHz. In this section, we investigate the impact
of the spectral switching granularity on the performance and cost of spatial Sp-Ch switches based on two practical
WSS technologies: 1) the current generation WSS realization, 2) a WSS technology with a factor-two resolution
improvement (i.e. requiring 9 GHz for guard band instead of the 18 GHz considered above). A summary of the
ChBW values selected for this study and the corresponding clear channel bandwidth that can be allocated with data
and switched by current and future (factor-two resolution improvement) WSS realizations is provided in Table 2.1.
Table 2.1 Values of selected ChBW with the amount of corresponding spectral contents supported by two WSS technologies
Simulation environment and assumptions
The network simulations were carried out over the Telefónica Spain backbone network model. Even though
MCFs/MMFs/FM-MCFs are the ultimate transmission media for SDM networks, to ensure a smooth migration from
currently deployed networks to SDM, network operators will seek to leverage their current infrastructure by
exploiting the capacity increase enabled by parallel transmission through BuSMFs. These have the advantage that
the transmission is not affected by XT between spatial dimensions (fibers), and SDM multiplexers/demultiplexers
are not required for component interconnection. Assuming BuSMFs also allows us to make a fair comparison
between different spatial Sp-Ch switching paradigms, due to the fact that BuSMFs are the only type of SDM
transmission medium compatible with all switching paradigms. We therefore limited the network performance study
to the case of BuSMFs and use the offline planning platform presented in D5.2 of INSPACE to perform the
simulations. Regarding the transmission technology, single carrier (SC) multi-channel (MC) multi-line rate (MLR)
systems were considered, in which the MLR behavior is achieved by changing the number of spatial channels and/or
by employing different modulation formats. The choice of modulation format is limited to dual-polarization (DP) –
BPSK, QPSK, 8QAM and 16QAM, with maximum transmission reach calculated by means of the Gaussian Noise
(GN) model of nonlinear interference in coherent (Nyquist) WDM systems proposed in 32. The obtained reach
values are presented in Table 2.2.
Table 2.2 Maximum point-to-point transmission reach (km) with the indicated baud rate, ChBW, and modulation format
Baud rate in GSamp/s ChBW in GHz DP-BPSK DP-QPSK DP-8QAM DP-16QAM
32 50 9800 4900 1900 900
25.75 43.75 10800 5400 2100 1000
19.5 37.5 12300 6100 2400 1200
13.25 31.25 15400 7700 3000 1500
7 25 16650 8300 3250 1650
41 50 7300 3500 1200 600
34.75 43.75 7800 3700 1400 700
28.5 37.5 8300 4000 1500 800
22.25 31.25 9200 4500 1800 900
16 25 10500 5200 2000 1000
Spectral Channel plan [GHz] 50 43.75 37.5 31.25 25
Current generation WSS resolution
(clear channel BW and % utilization) 32 25.75 19.5 13.25 7
64% 59% 52% 42% 28%
Improved resolution WSS technology (clear channel BW and % utilization)
41 34.75 28.5 22.25 16
82% 79% 76% 71% 64%
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Results and discussions
In this section, we first compare the performance of spatial Sp-Ch switching paradigms under different spatial and
spectral granularities in a network planning scenario for the Telefónica Spain backbone network model. We assume
bundles of 12 SMFs with 4.8 THz available spectrum per fiber across all links as a near-term SDM solution. The
performance evaluation is done in terms of load and its growth. Since there is a fixed number of demands in the
traffic matrix (84 demands), traffic growth is achieved by increasing the size of the demands. In order to perform the
studies, we scale the load (i.e. total offered load to the network) up to 1 Pbps, which is equivalent to 10-year total
traffic growth, assuming 45% annual traffic increase. For the spatial switching granularity, we consider groups (G)
of 1, 2, 3, 4, 6 and 12 fibers out of 12 fibers in BuSMFs, where G = 1 and 12 correspond to the cases of Ind-Sw and
J-Sw, respectively, which offer the finest (Ind-Sw) and the coarsest (J-Sw) spatial granularities. Intermediate values
represent FrJ-Sw with spatial groups formed of 2-6 SMFs.
Figure 2.4 Average spectrum utilization per link per fiber under different spatial Sp-Ch switching paradigms considering current WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 32, 19.5 and 7 Gbaud,
respectively in terms of load.
Figure 2.5 Average data occupancy in percentage considering current WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 32, 19.5 and 7 Gbaud, respectively in terms of load.
Figure 2.4 shows the results considering present-day WSS technology requiring 18 GHz guard band. Figure 2.4(a)
presents the results for the case of fixed-grid 50 GHz WDM ChBW, in which 32 Gbaud is selected for the contents
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of each ChBW as it is the maximum baud rate supported by present-day WSS resolution. The average spectrum
utilization per link per fiber is used as a quantitative network performance metric. Thus, for example, J-Sw with the
coarsest granularity in our studies corresponds to G = 12 and ChBW = 50 GHz, allows for 32GHz×12=384 GHz for
data loading, corresponding to 3072 Gb/s assuming DP-16QAM format. Note that, DP-16QAM is the most used
modulation format, as most of the lightpaths in the Telefónica network can be established within its optical reach
limit. Demands smaller than that will simply occupy the whole spatial-spectral slot, resulting in low data occupancy
within the available bandwidth. At the finest granularity of G = 1 and ChBW = 50 GHz, the equivalent minimum
spatial-spectral bandwidth slot amounts to 256 Gb/s.
In Figure 2.4(a), Ind-Sw shows the best performance for all loads, i.e, lowest utilization for the given traffic load,
since it offers the finest granularity. We use it as the benchmark to estimate the unutilized bandwidth due to
grouping of spatial dimensions. The performance of the rest of spatial Sp-Ch switching paradigms is seen to
converge to that of Ind-Sw as load increases. This is due to the fact that when the load increases the spectral-spatial
slot becomes comparable to the demand that has to be served and therefore the amount of unutilized bandwidth due
to SG-Sw reduces. Additionally, we observe that, independently of the load, but more noticeably for smaller loads,
the curves for the SG-Sw cases with lower values of G (i.e. finer spatial granularity) are closer to the Ind-Sw curve.
This is a consequence of the higher flexibility that SG-Sw with low G offers to allocate smaller demands in the
space dimension. However, finer spatial granularity results in higher switching infrastructure cost. Current WSS
technology with 6.25-GHz assignable spectral slots enables the switching of smaller ChBWs, which allows us to
evaluate the impact of spectral switching granularity on the performance of spatial switching paradigms. To carry
out this investigation, we repeated the above simulations for 37.5-GHz ChBW at a baud rate of 19.5 Gbaud (Figure
2.4(b)), and 25-GHz ChBW at 7 Gbaud (Figure 2.4(c)). As observed in Figure 2.4(b) and (c), for small values of
load, all curves show improved performance compared to the 50-GHz ChBW case since finer data loads can be
accommodated per channel. It is noteworthy that the performance of J-Sw (switching paradigm with the coarsest
spatial granularity) converges to that of Ind-Sw for smaller load values, as the ChBW decreases, compared to the
case of 50-GHz ChBW. Yet for high loads the spectrum utilization is higher for the case of finer DWDM channel
grid, as the finite channel guard bands exhibit lower spectral utilization and more channels have to be provisioned to
carry the data.
Another way to quantify the impact of the switching group size is to consider the average ‘data occupancy’
within an allocated wavelength channel. The data occupancy metric is defined by the bandwidth required to support
the data (i.e. equivalent to the baud rate yet measured in GHz) divided by the available bandwidth for data (which is
the clear channel bandwidth multiplied by the group size). Ind-Sw always satisfies 100% data occupancy, whereas J-
Sw will have the lowest data occupancy (as low as 1/G). As shown in Figure 2.5, the amount of data occupancy
increases for switching paradigms with finer spatial and/or spectral granularity. Additionally, inflection points in the
performance of SG-Sw cases are observed at loads of 0.2 and 0.7 Pb/s, respectively, from where the data occupancy
of SG-Sw cases increases significantly. In particular, as observed in Figure 2.5(b), the data occupancy in the case of
J-Sw goes above 90% for loads higher than ~0.65 Pb/s with 37.5 GHz ChBW, instead of for loads above ~1 Pb/s
with 50 GHz ChBW. Finally, Figure 2.5(c) shows a further improvement of the performance of different SG-Sw
paradigms in comparison with the previous two cases (e.g. the data occupancy of J-Sw increases up to 90% at ~0.2
Pb/s).
Therefore, we can conclude that, for small load values, the utilization of WSSs with finer spectral switching
granularity can compensate for the spatial granularity rigidity of SG-Sws. For larger load values, on the other hand,
the performance of all switching paradigms is degraded (i.e. the average spectrum utilization increases) as ChBW is
decreased. This is due to a less efficient utilization of the spectrum arising from a lower amount of occupied
spectrum containing actual traffic compared to the required guard band for the WSSs (i.e. 32/50=64% vs. 7/25=28%
for ChBW equal to 50 and 25 GHz, respectively).
In order to evaluate the improvement of the SG-Sw performance resulting from the utilization of WSSs with
improved resolution, we repeat the above studies for a WSS technology with a factor-two resolution improvement
(i.e. requiring a 9-GHz guard band instead of the 18 GHz considered previously). Figure 2.6 attest that the
performance of spatial Sp-Ch switching paradigms can be improved by using spatial switches with finer spatial
granularity. Likewise, similar to Figure 2.4, the use of smaller ChBWs results in performance improvement. Note
that, comparing Figure 2.6 with Figure 2.4, due to the lower guard band required by the WSS with finer resolution,
the performance of switching paradigms is not degraded for large load values, in contrast to the case of current WSS
technology with coarser resolution. However, by comparing Figure 2.7 with Figure 2.5, we observe that the amount
of data occupancy is less significant when the improved resolution WSSs are in place. This is due to the fact that one
spectral-spatial slot can accommodate more traffic when WSS technology with factor-two resolution improvement
is in place compared to the case of present-day WSS technology. For example, assuming ChBW equal to 50 GHz
and DP-16QAM, the capacity of one spatial-spectral is 72 Gb/s higher (i.e. 328 Gb/s – 256 Gb/s) in the case of
improved resolution WSS compared to the current WSS technology. Note that, even though the data occupancy
(which is a normalized metric) is lower for WSSs with finer spectral resolution, refining the WSS spectral resolution
results in a globally better performance, as is shown in Figure 2.8.
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Figure 2.6 Average spectrum utilization per link per fiber under different spatial Sp-Ch switching paradigms considering improved resolution WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 41, 28.5
and 16 Gbaud, respectively in terms of load.
Figure 2.7 Average data occupancy in percentage considering finer resolution WSS technology for (a) 50 GHz, (b) 37.5 GHz, and (c) 25 GHz ChBWs at a baud rate of 41, 28.5 and 16 Gbaud, respectively in terms of load.
Figure 2.8 presents the results of a more detailed performance evaluation of SG-Sw paradigms for the values of
ChBWs indicated in Table 2.1 and for two WSS technologies with coarser and finer resolutions. For the sake of
clarity, the results are only shown for J-Sw. Figure 2.8(a) shows the average spectrum utilization with the current
WSS technology. For small loads (<80 Tb/s), as shown previously, smaller ChBW values lead to better J-Sw
performance. However, as traffic increases, smaller ChBW values result in significant performance degradation.
Figure 2.8(b) shows the results when the finer resolution WSS is used. Due to the more efficient utilization of the
optical spectrum, smaller ChBW values lead to better performance for loads lower than 800 Tb/s. Even if the
performance of J-Sw with smaller values of ChBW reduces for loads larger than 800 Tb/s, this is remarkably better
than in the case of WSSs with coarser resolution. Another important finding is that, for small and large loads, the
best J-Sw performance is obtained for the lowest and highest values of ChBWs, respectively. Consequently, ChBW
must be adaptable to the load level in order to achieve a globally optimum spectrum utilization in an SDM network.
This highlights the importance of utilizing flex-grid transmission enabled by spectrally flexible ROADMs and
bandwidth variable transceivers when SG-Sw paradigms are considered.
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Figure 2.8 Average spectrum utilization per link per fiber for J-Sw considering: (a) the current WSS technology which requires 18 GHz for guard band and (b) finer resolution WSS which requires 9 GHz for guard band. Five values of ChBWs are assumed for the simulations. The amount of spectral contents that can be utilized considering different WSS technologies is
provided in Table I.
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3. Cost analysis and power consumption evaluations
In this chapter of the deliverable, we present the results showcasing the cost and power consumption benefits of
exploiting INSPACE proposed solutions with respect to the legacy approaches.
3.1. Cost benefit quantification of INSPACE proposed solutions
In this section, we present a cost analysis of SDM networks based on spatial and spectral Sp-Ch allocation
considering integrated TRx, amplifier and node implementations for Ind-Sw (spectral and spatial Sp-Ch), J-Sw and
FJ-Sw (spatial Sp-Ch), compared to a parallel-fiber system based on 100G transmission. We quantify the values of
cost savings, under given assumptions, for several cases of parallel-fiber system deployments, and discuss how the
cost of the system can be further reduced.
Cost model
Super-channel transceiver cost model
The cost of the Sp-Ch TRxs is based on the cost model presented in [5]. In contrast to the case of parallel fiber
systems employing single-carrier 100G OIF MSA TRxs (shown in Figure 3.1), the spectral Sp-Ch TRx employs two
comb generator modulators and drivers, two arrayed waveguide gratings (AWG), and a variable gain dual-stage
amplifier, in order to avoid the use of two lasers per sub-channel (Sb-Ch). The spatial Sp-Ch TRx (shown in Figure
3.2), since all Sb-Chs are transmitted at the same frequency, does not require frequency combs or AWGs, and can
bring the cost down by 5-20% for integrated spatial Sp-Ch TRx with 2-10 Sb-Chs vs. spectral Sp-Ch TRx.
Figure 3.1 single-carrier transceiver
Figure 3.2 Integrated spatial super-channel transceiver with seven integrated sub-channels
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Cost model for switching solutions
The implementation of switching solutions for Ind-Sw, J-Sw and FJ-Sw requires the design of new SDM
switching nodes. A potential realization for OE node architecture based on currently available WSS technology is
composed of a number of WSSs equal to 2·D·ceil(S/G) with port count D·G, where D is the number of nodal
degrees, S is the number of spatial dimensions and G is the number of spatial dimensions (out of the total number S)
that are jointly switched. Therefore, G equals 1 for Ind-Sw, S for J-Sw and any intermediate number for FrJ-Sw.
FrJ-Sw and J-Sw make necessary a redesign of the WSSs. Joint WSSs compatible with BuSMF, FMF or MCFs
were reported in [8]. They are configured to operate as S×(I×O) WSSs, i.e. they direct I input ports, each carrying S
spatial modes/cores, toward O output ports. In the cases of FMFs, MCFs or FM-MCFs, SDM interfaces capable of
converting the MCF/FMF inputs/outputs into BuSMFs, such as photonic lanterns [9] or MCF fan-ins/fan-outs, need
to be interposed between the fiber and the WSS. Alternative WSS implementations for FMF transmission replace
the WSS I/O SMF array with an FMF array14, with a 3×(1×9) configuration having been demonstrated. The
considered cost model for various component are presented in the next sections.
To compare the three SDM switching paradigms, we must take into account how the architectural complexity of
the switching solutions affects, not only the network performance, but also the equipment cost. Cost differences
between node architectures arise from the fact that the required number of WSSs per node and the WSS port count
differ, as indicated above, according to the chosen switching strategy. In the rest of this chapter, we consider route
and select node architectures for BuSMFs, thus focusing on WSS realizations with I/O SMF arrays and obviating the
need of SDM interfaces.
HPC 1N WSS cost model
The HPC 1×N WSS cost model uses the cost of commercial LCoS-based 1×9 WSSs as a reference (cost = 1) and
follows the rule that an increase of 4 in the number of ports results in a 2.5 increase in cost, based on a WSS
design analysis performed in the framework of the EU project INSPACE. Node architectures requiring WSSs with
less than 3, 6, 10 and 21 ports (i.e. 1×2, 1×5, 1×9, and 1×20 WSSs, respectively; all commercially available) have a
cost per WSS of 0.4, 0.63, 1 and 1.58, respectively. The cost of HPC WSSs with up to 40, 80 and 160 ports (1×40,
1×80, 1×160 WSSs), assuming technology maturity and mass production, was estimated to be 2.50, 3.95, and 6.25,
respectively. In Table 3.1 and Figure 3.3, we show the relative cost for WSS with port counts from 2 to 160.
Table 3.1 Estimated cost of HPC WSSs WSS port count Estimated cost of HPC WSS
12 0.40
15 0.63
19 1
120 1.58
140 2.50
160
180 3.95
1160 6.25
Figure 3.3 Relative cost/price of WSS required for a given number of ports
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MN WSS cost
MN WSS are not commercially available. We have estimated the cost of an 824 WSS to be the same as that of a
180 WSS, i.e. 3.95 times the cost of a 19 WSS, as indicated. According to [6], a ballpark estimate for the
maximum number of ports supported by an LCoS SLM is given by the formulas presented in Table 3.2.
Table 3.2 Relationship between maximum number of ports and number of LCoS SLM pixels. N_px is the total number of pixels in the steering direction. Px_port is the number of pixels per port.
WSS port configuration
General formula Formula assuming a number of pixels per
port (Px_port) equal to 5
Maximum number of ports for N_px = 1200
pixels
1N N+1 = 0.7*N_px/Px_port N+1 = 0.7*N_px/5 N = 168
MN M·N = 0.7 N_px/Px_port M·N = 0.7*N_px/5 M·N = 168 (if M = 8, then N = 20)
Twin 1N N+1 = 0.7 N_px/(Px_port·2) N+1 = 0.7*N_px/(5·2) N = 80
Quad 1N N+1 = 0.7 N_px/(Px_port·4) N+1 = 0.7*N_px/(5·4) N = 40
From Table 3.2, we can see that a 19201200 pixel LCoS panel could support a 1160 WSS or an 820 WSS.
An 824 WSS (design based on a two column arrangement, as shown in Figure 3.4, where I/O fibers were arranged
in two columns, thus doubling the addressable port count) can be supported with such a conventional LCOS panel.
Our assumption is that an 824 WSS is just as complex as a 180 WSS (four times a 120 WSS). There is a
secondary switching plane (and technology) required, accounting for a doubling in the cost for the switching
technology. Additionally, a 2D fiber and collimator array is more expensive to realize than the same number of
fibers in a 1D array. Finally, testing time is significantly longer for such a switch, accounting for extra cost.
Figure 3.4 Port-reconfigurable MN WSS
ROADM cost model
For the A/D nodes, we assume colorless, directionless and contentionless (CDC) reconfigurable optical A/D
multiplexer (ROADM) operation based on multicast switches (MCS). The A/D module cost (CA/D) is estimated as
𝐶𝐴/𝐷 = min {2𝑁 (𝐷 · 𝑆 · 𝐶𝑠𝑝 + ⌈𝑇
𝑁⌉ 𝑆 · 𝐶𝑠𝑤) + 𝐶𝑎𝑚𝑝}
where N is the number of Twin MCSs, T is the number of TRxs that add/extract traffic to/from the node; Csp is
the cost of the splitters (with splitting ratio 1:(T·S/N)) and Csw is the cost of the opto-mechanical switches (with
port count (D·S)×1) forming the MCS, obtained by averaging costs from different vendors, and Camp is the cost of
the required amplifiers per MCS, given by 2·D·S·N·CEDFA, where CEDFA is the cost of an SMF EDFA. We
assume that amplifiers are only used if the power at the receiver (without amplification) is below -20 dBm. In the
equation for CA/D, the value of N minimises CA/D.
The A/D nodes are composed of 2·D·ceil(S/G) WSSs with port count (D+N)·G. The total cost of a ROADM is
estimated to be
𝐶𝑅𝑂𝐴𝐷𝑀 = 𝐶𝐴/𝐷 + 2𝐷 ⌈𝑆
𝐺⌉ 𝐶𝑊𝑆𝑆
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where CWSS is the cost of the WSS, which is estimated as indicated above. The cost of an OE node is given by the
above equation with CA/D=0 and N=0.
MCS specifications
MCS use low-cost single stage amplifiers with gain of typically ~17 dB. The losses of an integrated MCS include
coupling in and out of a PLC (-0.75 dB), the splitter intrinsic power loss (1/N) plus some excess loss. If we assume
the split is performed by cascaded 1:2 splitters, then the loss in dB will be approximately -3.25* (log2N). Say for 16
way split, this will be -13dB (instead of intrinsically -12dB). At the output there is a selector switch, which is based
on a cascade of 12 switches. Each switch will have a typical loss of -0.35dB, hence a selector switch of 4 inputs
will have another -0.7 dB of loss. Finally, there will be many waveguide crossings on the PLC. Those will add some
excess loss which scales linearly with the number of inputs and outputs (in dB scale). With 17dB amplifiers you can
support MCS 8×16 at most.
Table 3.3 Loss of splitters and switches with different splitting ratios to be used in realizing MCS with various port counts
Amplifier cost model
Regarding the in-line amplifiers, MC-EDFAs with cladding pumping can be used for SDM systems using
BuSMFs, provided that proper fan-in/fan-out devices are used. Therefore, we consider two scenarios: (a) SMF
EDFAs (one per fiber) and (b) a realization composed of a fan-in (which converts the output of the SMF bundle into
an S-core MCF), an MC-EDFA and a fan-out as shown in Figure 3.5. The MC-EDFA cost was estimated based on
[7], which, for S = 9, is 3.3× the cost of a conventional SMF EDFA. The cost of the fan-in/fan-out was estimated in
the framework of INSPACE and to be proportional to the number of channels plus a fixed cost for packaging,
material, etc.
Figure 3.5 A possible design of MC-EDFA
Network-level cost analysis
Telefónica’s Spanish national network model was considered for the studies. As baseline for comparison we
consider two cases with 9 parallel SMFs, each having a different line system without spatial multiplexing of
networks elements: one considering conventional single-carrier 100 Gbps TRx (100G case) and the other an SDM
network based on spectral Sp-Ch TRx (C1 case). In addition, we consider four SDM deployment cases, with
spatially integrated network elements, based on bundles of 9 SMFs (because it allows us to make a fair comparison
among different cases regardless of any transmission medium related physical constraints): C2) SDM employing
individual EDFAs, Ind-Sw and integrated spatial TRx; and SDM network employing integrated spatial Sp-Ch TRx
and SDM EDFAs using C3) Ind-Sw, C4) FrJ-Sw(G=3), and C5) J-Sw.
To route the demands, we use the offline planning platform presented in D5.2 of INSPACE. Figure 3.6 shows
the average spectrum utilization for scenarios C1-C5. SDM network based on spectral Sp-Ch benefits from higher
spectral efficiency compared to spatial Sp-Ch one, as is also shown in D5.2 of INSPACE. SDM networks based on
Ind-Sw, FrJ-Sw and J-Sw show almost similar performance, particularly when the total offered load to the network
is large.
inputs outputs splitter switch PLC coupling WG crossings Total
4 8 -9.75 -0.7 -0.75 -0.64 -11.84
4 16 -13 -0.7 -0.75 -1.28 -15.73
4 32 -16.25 -0.7 -0.75 -2.56 -20.26
8 8 -9.75 -1.05 -0.75 -1.28 -12.83
8 16 -13 -1.05 -0.75 -2.56 -17.36
8 32 -16.25 -1.05 -0.75 -5.12 -23.17
Losses
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In order to present the total deployment cost of networks, we use the cost model described in the previous
section and, considering the cost of commercial LCoS-based 1×9 WSSs as 1 unit, we report all cost values with
respect to this value. Figure 3.7 shows the total relative cost (TRC) of different cases under study. Total cost is the
summation of the cost of TRxs, A/D nodes, amplifiers (Amp in Figure 3.7), and OE nodes.
Figure 3.6 Average spectrum utilization per link per fiber in terms of total offered load to the network in Pbps
Figure 3.7 Total relative cost of the network considering 4 different offered load for 5 SDM deployments compare to an equivalent parallel system using 100G TRx.
As depicted in Figure 3.7, going from single-carrier (i.e. 100G case) to spectral and spatial Sp-Ch based
approaches results in huge total cost savings (50%-60%) due to the reduction of the TRx cost (enabled by photonic
integration and component sharing), which dominates the total cost of the network.
After the TRx cost, whose contribution to the total cost ranges from 45% to 58%, the second most costly element
in all cases is the A/D nodes (consisting of MCSs and WSSs), with 36%-40% of the total cost of the network. The
four SDM cases (C2-C5) offer a total cost benefit ranging from only 4% to 15% compared to C1. Excluding TRxs
and A/D nodes, the cost of the rest of the elements, with 50%-55% and ~63% savings for OE nodes and EDFAs
respectively, demonstrate the benefits of joint switching and amplifier integration. The relatively small overall cost
savings due to the high cost of TRxs and A/D nodes mean that, for an SDM solution to offer higher cost savings, the
focus should be on the reduction of the cost of TRxs and A/D nodes. To reduce the cost of TRxs, we can consider
the use of common DSP chips (as proposed in [10]). To reduce the cost of A/D units, alternative designs need to be
considered. In the next section, we present a comprehensive cost analysis of A/D units revealing the benefits of
INSPACE proposed J-Sw scheme in reducing the overall cost of ROADMs including both OE and A/D units.
250
500
1000
2000
0.5 1 1.5 2Avera
ge S
pectr
um
Utiliz
ation per
Lin
k p
er
Fib
re (
GH
z)
Total Offered Load to the Network (Pbps)
C1C2, C3C4C5
0
20
40
60
80
To
tal R
ela
tive
Co
st
(in
th
ou
sa
nd
s)
Total Offered Load to the Network (Pbps) 0.5 1 1.5 2
100G C1 C2 C3 C4 C5OE Node
Amp
A/D Node
TRxs
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3.2. Comparison of CD(C) ROADM architectures for SDM networks
Motivation
CDC ROADMs has attracted significant interest from the optical networking industry. CDC ROADMs offer
architectural flexibility and operational efficiencies leading to reduced cost, while they also support enhanced
capabilities for optical layer restoration and re-optimization in the case of need for dynamic capacity re-allocation
[11]. For enhanced performance, colorless-directionless (CD) and CDC ROADMs use a common optical core based
on twin-WSS modules in a route-and-select (R&S) architecture, and only differ in the way that the A/D side is
implemented.
Such ROADMs are becoming a commercial reality for conventional flexible WDM networks. Moreover, the
existing architectures can be upgraded to address the challenges introduced by spatial division multiplexing (SDM),
which will arguably be the next step in the evolution of optical networks. Not only does SDM promise higher
capacities, but it also hints at a reduction of the cost per transmitted/switched bit through the utilization of spatial
integration of networks elements. As we have also seen in the previous section, A/D unit of a ROADM node is one
of the dominant contributor to the cost of an SDM network. Despite the increasing interest in SDM-based optical
networking studies, a detailed study summarizing the architectures of CD(C) ROADMs for SDM spatial super-
channel routing, while comparing their scaling potential and associated implementation costs, is missing. In this
section, we propose CD(C) ROADM architectures enabling Ind-Sw, FrJ-Sw and J-Sw of spatial SChs and we
present a cost model based on the premises presented in the previous section for comparing the proposed
architectures. We find that a CDC ROADM design which maximizes the number of A/D ports, and yet keeps the
port count of pass-through WSSs low, is the most cost-effective solution.
ROADM architectures and cost analysis
We consider the ROADM architectures illustrated in Figure 3.8, as well as the case (not shown) of FrJ-Sw, where
each group of G spatial dimensions is jointly routed by a G×(I×O) WSS. Lane changes (LCs) between groups,
defined as the possibility of routing a spatial SCh from a given set of spatial dimensions to a different one, can also
be supported in the case of FrJ-Sw with the appropriate connectivity and port count. The required number of WSSs
and their port count is shown in Table 3.4 as a function of G (thus providing a common formula valid for all cases)
for routing options with/without LC. The evaluation of the ROADM cost (CROADM) considers the cost of the
WSSs (CWSS) and an A/D module (CA/D) composed of K elements, such that K minimizes the overall cost.
Tx3
‘West’
WS
SW
SS
‘East’
WS
SW
SS
WSSWSS
‘North’
WSS WSS
WS
SW
SS
WS
SW
SS
Rx1
Rx2
Rx3
Rx4
12
2
1
12
1
2
1st spatial
dimension
2nd spatial
dimension
A/D module (Drop)
(a)
Tx1
Tx2 Tx4
12
2
1
12
1
2
A/D module (Add)
To ‘North’
To ‘West’
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Figure 3.8 Route and Select ROADM architectures for (a) Ind-Sw and (b) J-Sw in an SDM scenario with S=2. In (a) we show all internal connectivity from the ‘East’ ingress WSS. LCs are supported if all solid, dashed and dotted
connections exist. Ind-Sw without LCs requires solid and dashed lines.
Table 3.4 The required number of WSSs and their port count
Pass-through WSS Total ROADM cost
(CROADM) # WSS Port count
Without LC
2 · 𝐷 ·
𝑆/G
𝐺 × [1 × (𝐷 + 𝐾 − 1)] min𝐾∈ℤ+
{𝐶𝐴/𝐷 + 2𝐷 · (𝑆/𝐺) · 𝐶𝑊𝑆𝑆𝐺×[1×(𝐷+𝐾−1)]}
With LC 𝐺 × {1 × [(𝑆/𝐺) · (𝐷 − 1) + 𝐾]} min𝐾∈ℤ+
{𝐶𝐴/𝐷 + 2𝐷 · (𝑆/𝐺) · 𝐶𝑊𝑆𝑆𝐺×{1×[(𝑆/𝐺)·(𝐷−1)+𝐾]}}
Figure 3.9 Two A/D module architectures for ROADM CD operation. Only ‘Drop’ WSS are shown.
Figure 3.10 Two M×N WSS-based A/D module architectures for ROADM CDC operation. Only ‘Drop’ WSS are shown.
‘West’
WS
S
‘East’
WS
S
WSS
‘North’
WSS
WS
S
WS
S
1st spatial dim.
2nd spatial dim.
(b)
Tx3Rx1
Rx2
Rx3
Rx4
12
2
1
12
1
2
A/D module (Drop)
Tx1
Tx2 Tx4
12
2
1
12
1
2
A/D module (Add)
Rx1
S (D 1)
S (1 T/K )
Rx2
Rx3
Rx4
12
2
1
121
2
S (1 N) WSS
S (M 1) WSS
(a) 1st spatial dimension 2nd spatial dimension
Rx1
Rx2
Rx3
Rx4
12
2
1
121
2
D 1
1 T/K 1 N WSS
M 1 WSS
1 N WSS
M 1 WSS
(b)
Rx1
S (D T/K )
Rx2
Rx3
Rx4
12
2
1
121
2
S (M N) WSS(a)
1st spatial dimension 2nd spatial dimension
Rx1
Rx2
Rx3
Rx4
12
21
121
2
M N WSS M N WSSD T/K
(b)
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Figure 3.11 Three MCS-based A/D module architectures for ROADM CDC operation. Only ‘Drop’ MCS are shown.
For CWSS we use as a baseline the cost of commercial LCoS-based 1×9 WSSs (cost = 1). Node architectures
requiring WSSs with port counts 1×5, 1×9, and 1×20, all commercially available, have a cost per WSS of 0.63, 1
and 1.58, respectively. The cost of WSSs with port counts 1×40, 1×80 and 1×160 was extrapolated to be 2.50, 3.95,
and 6.25, respectively, according to 58% premium for a fiber port doubling.
Table 3.5 Cost of the A/D modules presented in Figure 3.9 to Figure 3.11
The A/D modules are implemented as shown in Figure 3.9 for CD operation and Figure 3.10 and Figure 3.11 for
CDC operation (only the drop module is shown). In Figure 3.9 to Figure 3.11, T is the number of transceivers (TRx)
connected to the A/D ports. Table 3.5 show our A/D module cost estimation. In the case of CDC operation
implemented with multicast switches (MCS), Figure 3.11, the cost model is based on the estimation of the number
of discrete components (splitters and opto-mechanical switches), whose costs were obtained by averaging costs from
different vendors, and it does not include the cost of packaging and electronics. We consider low-cost single-stage
amplifiers with ~17 dB gain (which limits the splitting ratio to about 1:16) and cost CEDFA ~ 0.17. The total cost
of amplifiers per A/D module is estimated as C_amp=2·K·S·D·C_EDFA. The architecture shown in Figure 3.11(a)
supports full CDC switching between all common ports and A/D ports (case investigated in the previous section)
and is required in the case of single-channel TRxs. In Figure 3.11(b) connectivity is simplified to provide CDC
operation per spatial dimension, which is sufficient for integrated SCh TRxs. In the case of CDC operation with
M×N WSS, Figure 3.10, we assume WSS configurations with complexity similar to that of a 1×80 WSS, e.g. 8×24.
If we decide to use a higher number of common ports M, the number of A/D ports N has to decrease in proportion
so that their product remains constant.
Figure 3.12 Relative cost of the components wrt 1×9 WSS
(S·D) 1 Switches
1: S·T/KSplitters
Rx1
(S·D) S·T/K
Rx2
Rx3
Rx4
12
2
1
121
2
(a)
D 1 Switches
1: T/KSplitters
Rx1
Rx2
Rx3
Rx4
12
2
1
121
2
(S·D) S·T/K
(b)
D 1 Switches
1: T/KSplitters
MCS for 1st spatial dimension MCS for 2nd spatial dimension
Rx1
Rx2
Rx3
Rx4
12
2
1
121
2
D 1 Switches
1: T/KSplitters
D T/K
(c)
0 5 10 15 20 25 30
1×2 WSS1×5 WSS1×9 WSS
1×20 WSS1×40 WSS1×80 WSS
1×160 WSSTwin 3×52 WSSTwin 9×18 WSSTwin 18×9 WSSTwin 36×4 WSSTwin 3×16 MCSTwin 9×16 MCS
Twin 18×16 MCSTwin 36×16 MCS
Twin 9×48 MCSTwin 18×96 MCS
Twin 36×192 MCS
Relative Cost of the Components wrt
1×9 WSS
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Figure 3.13 Total relative cost (TRC) of different ROADM implementations.
Discussion on the total relative cost of ROADM implementations
Based on the cost models described in the previous section, we calculate the total implementation cost of each
ROADM architecture. A degree-3 ROADM for an SDM network with 6 spatial dimensions is considered for the
calculations. For the realizations of the designs, we use the components with port count and relative cost provided in
Figure 3.12 and calculate the total relative cost (TRC) of each design for the three SDM switching paradigms. In
order to evaluate the scaling of their implementation cost with the number of A/D ports, we repeat the calculations
for 20, 40, and 60 spatial SCh TRx, each comprising 6 spatial sub-channels, resulting in 120, 240, and 360 required
add and drop ports. To minimize the A/D module cost, we consider a number K of A/D elements in parallel, whose
impact on the port count of pass-through WSSs is also taken into account. Figure 3.13 shows the resulting TRC
values. C1-C7 represent the ROADM architectures illustrated in Figure 3.9(a-b), Figure 3.10(a-b), and Figure
3.11(a-c), respectively.
It is found that the most cost-effective architecture is the one which i) maximizes the number of available A/D
ports, and ii) does not heavily increase the port count of pass-through WSSs. Design C7, which utilizes 3×16, 9×48,
and 18×96 MCSs (with internal connectivity as shown in Figure 3.11 (c)) for Ind-Sw, FrJ-Sw with G=3, and J-Sw,
respectively, is the one that best meets the above conditions and therefore turns out to be the most cost-effective
solution. Surprisingly, its implementation cost is very similar to the implementation cost of CD ROADMs (design
C2). The study also reveals that J-Sw based ROADMs are more cost effective than those implementing Ind-Sw and
FrJ-Sw, except for design C4, where the Ind-Sw based realization, by taking advantage of the fact that M×N WSSs
with port count 3×52 maximize the available A/D ports with very small increase in the port count of pass-through
WSSs, outperforms the other two switching strategies. Design C3, based on a single M×N WSS with common ports
connected to all spatial dimensions coming from all pass-through WSSs, shows the highest implementation cost,
because of the imposed restriction that the M×N WSS complexity should not be higher than that of a 1×80 WSS.
Therefore, the only possible M×N WSS realization supporting a ROADM of degree 3 in an SDM system with 6
spatial dimensions is using WSSs with port count 18×9. The very low number of A/D ports supported by this
component means that a large number of them is required to support the same number of TRxs that could be
supported with fewer components by other architectures (e.g. C4, which can be implemented with M×N WSSs of
port count 3×52). Additionally, we have seen that when the number of parallel A/D elements increases, some of the
designs have a huge impact on the port count of pass-through WSSs. These designs turn out to be too costly or
impractical to be estimated (e.g. J-Sw based realizations of C3 and C4 when the number of TRxs scales beyond 40)
due to the need for WSSs with extremely large port counts.
3.3. Power consumption of MIMO processing and its impact on the performance of SDM networks
Motivation
SDM has been proposed as a solution for increasing the capacity of fiber-optic networks with a reduced cost-per-bit
through dense optical parallelism. The denser the integration of the spatial channels it becomes, however, the more
significant is the XT interactions among them, e.g. in strongly-coupled MCFs and FMFs. Such spatial XT is
expected to be mitigated by MIMO DSP at the TRx side, with a complexity for real-time implementation
determined by the number of spatial channels and their corresponding delay spread.
Current C-form pluggable (CFP)-based analog and digital coherent optics modules (i.e. CFPx-ACO and CFPx-
DCO) are focusing on decreasing the cost and volume, as well as the power consumption of the DSP by separating
the DSP chip from the module, while utilizing smaller complementary metal-oxide-semiconductor (CMOS)
platforms. While commercial CFP2-ACO DSP modules have been produced utilizing down to 16 nm CMOS
platforms [12], it will be extremely difficult to support further power reduction with sub-10 nm CMOS-based DSP
0
100
200
300
400
500
To
tal
Rel
ati
ve
Co
st w
rt 1
9 W
SS
s
Number of Spatial Superchannel Transceivers with 6 Spatial Channels
(C1) Ind-Sw (C1) FrJ-Sw (G=3) (C1) J-Sw
(C2) Ind-Sw (C2) FrJ-Sw (G=3) (C2) J-Sw
(C3) Ind-Sw (C3) FrJ-Sw (G=3) (C3) J-Sw
(C4) Ind-Sw (C4) FrJ-Sw (G=3) (C4) J-Sw
(C5) Ind-Sw (C5) FrJ-Sw (G=3) (C5) J-Sw
(C6) Ind-Sw (C6) FrJ-Sw (G=3) (C6) J-Sw
(C7) Ind-Sw (C7) FrJ-Sw (G=3) (C7) J-Sw
C1 C2 C3 C4 C5 C6 C7 C1 C2 C3 C4 C5 C6 C7 C1 C2 C3 C4 C5 C6 C7
20 40 60
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modules due to heat dissipation issues [13]. Therefore, even though MIMO-DSP can ideally compensate all the
linear impairments in SDM systems, the power consumption of the MIMO-DSP and of the overall SDM-TRx can be
a limited factor in the maximum capacity and achievable reach of SDM networks.
In this section, an estimation for the power consumption of real-time MIMO-DSP considering the current CMOS
technology is presented based on the computation complexity of frequency-domain equalizers (FDEs). In addition, a
reach/power consumption tradeoff for MIMO-DSP modules is investigated for FMFs with up to 6 spatial modes.
Finally, the performance of FMF-based SDM networks with 3 and 6 modes is compared with single-mode fiber
(SMF)-based SDM networks, revealing the impact of power-limited reach on the overall network performance.
Power consumption of MIMO processing for SDM
Considering MIMO-FDE with 2S spatial and polarization channels, based on training sequence and without any
cyclic-prefix, the computational complexity in terms of complex multiplications/bit can be given by [14][15]:
𝐶𝐶𝐹𝐷𝐸 = 𝑟𝑠
2𝑆 𝑁𝑓𝑓𝑡 log2 𝑁𝑓𝑓𝑡 + 2(2𝑆)2𝑁𝑓𝑓𝑡 + (2𝑆)3𝑁𝑓𝑓𝑡
2𝑆 (𝑁𝑓𝑓𝑡 − ⌈𝑇𝑇𝑠
⌉ + 1) log2 𝑀= 𝑟𝑠
𝑁𝑓𝑓𝑡 log2 𝑁𝑓𝑓𝑡 + 4𝑆 𝑁𝑓𝑓𝑡 + (2𝑆)2𝑁𝑓𝑓𝑡
(𝑁𝑓𝑓𝑡 − 𝑁 + 1) log2 𝑀
where, r_s is the oversampling ratio, M the modulation order, T_s the symbol duration, T the channel delay in
[sec], N=⌈T/T_s ⌉ the total channel delay in [symbols], and N_fft=2^d≥N the fast-Fourier transform (FFT) size with
d an integer number. In the above equation, the first term of the numerator accounts for the complexity of 2×FFTs,
the second term for the calculation the MIMO channel matrix and the multiplication of its inverse with the received
frame, and the third term for the calculation of the inverse of the MIMO channel matrix. Note, that even though the
MIMO matrix estimation and inversion is expected to take place every several frames [15], since here we are
focusing on the total power consumption, the worst case was assumed in which all the functions of channel
estimation and equalization are performed together. The resulting complexities for 32 GBaud, 100 Gb/s signals over
linear polarized (LP) FMFs with 3- and 6-spatial modes are compared to the SMF case in Figure 3.14 for different
channel delay spread values. This figure depicts the complexity increment based on the number of spatial channels
and the delay spread of the MIMO channel and how for larger delays, larger FFT sizes and number of taps are
required for equalization.
Figure 3.14 MIMO-DSP complexity of FDE
Table 3.6 CMOS power dissipation based on Fig. 2 of [7]
Moore’ law
(norm.)
Actual
(norm.)
Deviation from
Moore’s law
90 nm 0.129 0.153 15.7 %
45 nm 0.03 0.09 66.7 %
22 nm 0.008 0.07 88.6 %
15 nm 0.004 0.065 93.9 %
In [16], using a reversed-engineering approach, the energy dissipation per real multiplication and per real
addition for 90 nm CMOS platforms, was calculated to be 1.5 pJ and 0.5 pJ, respectively. Therefore, considering
that a single complex multiplication can be described by 4 real multiplications and 2 real additions, an estimation of
the power consumption from the computational complexity can be drawn. In addition, to account for current CFP2-
ACO DSP implementations based on sub-20 nm CMOS [13], a power reduction from the 90 nm platforms can be
drawn based on dissipation values for different CMOS sizes. Here, we based our analysis on the published results of
[13], summarized in Figure 3.6. In the first column, the theoretical predicted values based on the Moore’s law are
shown, while the second column provides values for actual deployed systems. Since the deviation of the actual
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values from the Moore’s law is increasing for smaller sizes, here only the actual values were considered with a
deviation of ±5 %.
The resulting power consumptions for 15 nm CMOS, based on the computational complexities of Figure 3.14,
are depicted in Figure 3.15. Here an FFT size of 1024 was considered. The x-axis, which describes the reach, was
converted from the delay spread of Figure 3.14 considering a chromatic dispersion coefficient of 20 ps/nm/km and a
differential mode group delay (DMGD) of 0 ps/km, 1 ps/km, and 6 ps/km for the SMF, 2-LP FMF, and 4-LP FMF
case, respectively. Particularly, the values for the FMF cases were considered based on recent fabricated fiber links
incorporating DMGD compensation [17][18].
(a) (b)
Figure 3.15 Predicted (a) power consumption per mode and (b) total consumption per 6×100Gb/s module, for 15 nm CMOS.
Table 3.7 Power-limited reach based on total consumption per 600 Gb/s module
100 W 200 W 300 W
(50 W/mode)
600 W
(100 W/mode)
SMF 2104 km 2609 km 2780 km 2950 km
2-LP FMF 1699 km 2152 km 2306 km 2459 km
4-LP FMF 666 km 1050 km 1180 km 1309 km
Table 3.8 Total power consumption per 600 Gb/s module. 750 km 1000 km 1500 km fibers modes
SMF 42.8 W 47.9 W 62.7 W 6 × 1
2-LP FMF 49.1 W 56.7 W 82.2 W 2 × 3
4-LP FMF 112.2 W 176.4 W - 1 × 6
Particularly, in Figure 3.15(a) the calculated power consumption per mode is depicted, while in Figure 3.15(b)
the total consumption per 600 Gb/s is depicted based on 6-spatial/parallel channels carrying 100 Gb/s each (i.e.
6×SMFs, 2×3-mode FMFs, and 1×6-mode FMF). Considering that current real-time DSP for long haul applications
consumes 50~100 W per 400~500 Gb/s [19], and that current Intel’s processors consume powers around 200 W
[20], a limit on the maximum reach can be drawn. The resulting power-limited reaches for different power
thresholds and fibers are summarized in Table 3.7 for clarity. In addition, the total power consumption for 750-km,
1000-km, and 1500-km are summarized in Table 3.8. Such estimated distances are of great interest since they are
covering most of the deployed European-scale core networks. From this we can conclude that by shifting from
parallel SMFs implementations to FMFs with 3- and 6-modes, the realistic power-limited reaches are expected to be
reduced by 18~19 % and 60~68 %, respectively, for total consumptions of 100~200 W. Alternatively, the overall
consumption increases by 15~18 % and 168~268 %, respectively for the 3- and 6-mode upgrade, between 750-km
and 1000-km.
Figure 3.16 Predicted (a) power consumption per mode and (b) total consumption per 6×100Gb/s module, for 15 nm CMOS.
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
612.5 735 857.5 980 1102.5 1225
Blo
ckin
g P
rob
abil
ity
Total Offered Load to the Network (in Tb/s)
SMF, 100W
2LP-FMF, 100W
4LP-FMF, 100W
(a)
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
612.5 735 857.5 980 1102.5 1225
Blo
ckin
g P
rob
abil
ity
Total Offered Load to the Network (in Tb/s)
SMF, 200W
2LP-FMF, 200W
4LP-FMF, 200W
(b)
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Network-wide performance evaluation of integrated transceiver utilizing different MIMO-DSP modules
The impact of power consumption on the achievable reach affects the successful establishment of incoming
connections. Therefore, to investigate the overall performance reduction due to the power-limited reaches of
different MIMO-DSP modules, a networking-wide simulation is performed. We use the Spanish national network of
Telefónica. For the SDM network evaluations, 6 spatial dimensions are assumed per link, which can be realized by
utilizing either bundles of 6×SMFs, bundles of 2×FMFs with 3 spatial modes, or FMFs with 6 spatial modes.
Available spectrum equal to 4.8 THz (C-band) on the ITU-T WDM 50-GHz grid per spatial dimension is assumed.
Regarding transmission, single-carrier spatial superchannel TRxs with 6 spatial channels, each carrying 32 GBaud
DP-QPSK signals have been considered. TRxs with different MIMO-DSP modules for the different fiber types are
assumed, resulting in various achievable distances as provided by Table 3.7. The blocking probability (BP) was used
as a qualitative performance measure. Note that in our simulator, a blocked connection occurs due to i) transmission
distance longer than the optical reach of the signal and ii) unavailability of resources (spectrum, fiber) to establish
the connection. The results are summarized in Figure 3.16. Each data point was obtained by simulating 3×105
connection requests and has a confidence interval of 95%. As shown in Figure 3.16 (a), for a consumption of 100 W
per TRx module, BuSMFs and 2LP-FMF show similar performance due to the medium-size topology considered, in
which most of the connections can be established by the achievable reaches. However, 4LP-FMF shows
unacceptable performance resulting in rejecting more than 30% of the incoming connection requests. By increasing
the power per module, as shown in Figure 3.16 (b), the performance of 4LP-FMF improves significantly and gets
closer to the other cases. The performance for BuSMFs and 2LP-FMFs do not improve much, because the blocking
occurs due to the lack of available resource (even when considering 100 W per module). Therefore, it can be
concluded that, for a MIMO-DSP module for a 4LP-FMF based SDM network to perform close enough to SDM
networks utilizing BuSMFs or 2LP-FMFs, more than 2 times the power is required.
3.4. Control unit power consumption of WSSs used for realizing SDM nodes
As we have seen in the previous sections, the three SDM switching paradigms require different number of WSSs of
various port counts for the realization of a node. However, regardless of the port count of WSSs, each WSS is
equipped with a control unit which consumes, according to internal partners data, a fixed 10 watts of power to
operate properly. Therefore, as the number of required WSSs for realizing the three switching schemes scales
differently, the overall power consumption of the node due to the control unit scales in a different way. Figure 3.17
(a) shows the power consumption of a single degree of a ROADM for the three switching schemes in terms of the
number of spatial dimensions. As is shown, while the power consumption increase slightly in the case of J-Sw, it
increases sharply when Ind-Sw (i.e. the switching schemes which requires lots of WSSs) is in place. Figure 3.17 (b)
and (c) show the results in terms of number of ROADM degrees when 6 and 12 spatial dimensions are considered,
respectively. The power consumption of Ind-Sw case has a significant different compared to FrJ-Sw and J-Sw case.
This amount of power consumption, which is just due to control unit of WSSs, will be more pronounced and an
important contributor to the operational expenditure of a network owning many nodes. Therefore, utilizing the
INSPACE proposed switching scheme (i.e. J-Sw) will lead to significant power savings compared to the Ind-Sw.
Figure 3.17 Power consumption of control unit of WSSs needed for realizing SDM nodes
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Conclusions
In this deliverable, we evaluated the benefits of INSPACE proposed switching solutions in terms of networking
level performance, deployment cost, and power consumption. The main findings are listed below.
We investigated the suitability of three SDM switching paradigms for different traffic profiles in various parts of
a network (i.e. core segment, regional segment, or datacenter interconnection). We found that, for the case of
BuSMFs, Ind-Sw and FrJ-Sw perform well for networks with high level of traffic diversity, while J-Sw is a
favourable option for networks with large demands, considering also its cost benefit. However, J-Sw can perform
significantly better in high diverse traffic scenarios, if spatial Sp-Chs occupy smaller spectral width which can be
switched by WSSs with finer granularity. Therefore, we further investigate the performance of different switching
schemes under various spectral and spatial granularity.
We compared the performance of different SDM switching paradigms, in terms of spectral utilization and data
occupancy under various practically feasible spatial and spectral switching granularities considering the current
WSS realization and an improved resolution WSS technology over a network based on BuSMFs. The network-level
simulation results showed that the performance of all switching paradigms converges as traffic increases while the
switching-related infrastructure cost can be reduced up to 52% in the SG-Sw cases. Additionally, it was shown that,
considering the current WSS technology, the utilization of finer spectral switching granularity significantly
improves the performance of SG-Sw paradigms for small values of traffic, which correspond to small demands in
the traffic matrix. However, as the load increases, the performance of all switching paradigms reduces due to the
less efficient utilization of spectrum arising from a lower amount of occupied spectrum containing actual traffic
compared to the required guard band for the WSSs. We also showed that, by utilizing WSSs with improved
resolution which require 50% less guard band, the performance of switching paradigms in the case of large values of
traffic can be improved by a factor of two. Having said that, it results from our study that, irrespective of the WSS
resolution, large values of ChBW are more beneficial for large values of traffic, and consequently spectral switching
granularity must be adaptable to the traffic size in order to achieve a globally optimum spectrum utilization in an
SDM network, for which spectrally flex-grid ROADMs and bandwidth-variable transceivers are a requirement.
Additionally, we examine the cost benefits of an SDM networks utilizing spatially integrated components. We
showed that going from single-carrier (i.e. 100G case) to spectral and spatial Sp-Ch based approaches results in
huge total cost savings (50%-60%) due to the reduction of the TRx cost (enabled by photonic integration and
component sharing), which dominates the total cost of the network. After the TRx cost, whose contribution to the
total cost ranges from 45% to 58%, the second most costly element is the A/D nodes (consisting of MCSs and
WSSs), with 36%-40% of the total cost of the network. The SDM cases under study exploiting spatial Sp-Ch TRx
offer a total cost benefit ranging from only 4% to 15% compared to the case based on spectral Sp-Ch TRx.
Excluding TRxs and A/D nodes, the cost of the rest of the elements, with 50%-55% and ~63% savings for OE nodes
and EDFAs respectively, demonstrate the benefits of joint switching and amplifier integration. All in all, the
relatively small overall cost savings due to the high cost of TRxs and A/D nodes mean that, for an SDM solution to
offer higher cost savings, the focus should be on the reduction of the cost of TRxs and A/D nodes. In order to find a
cost-efficient A/D architecture we further investigate various CD(C) ROADM architectures. It is shown that an
architecture that maximizes the number of A/D ports, while keeping the pass-through WSSs port count low,
achieves the best cost performance. The study also reveals that J-Sw based ROADMs are more cost effective than
those implementing Ind-Sw and FrJ-Sw, except for one of the designs, where the Ind-Sw based realization, by
taking advantage of the fact that M×N WSSs with port count 3×52 maximize the available A/D ports with very
small increase in the port count of pass-through WSSs, outperforms the other two switching strategies.
Finally, we made some efforts to estimate the power consumption of SDM network based on alternative
transmission media which requires MIMO-DSP modules with different levels of complexity. In particular, the
reach/power consumption tradeoff of SDM networks based on the power consumption of MIMO-DSP has been
investigated considering up to 6-spatial channels. It has been shown that the MIMO-DSP for a 4LP-FMF network
requires more than twice the power required for SMF and 2LP-FMF based networks to achieve similar performance.
Furthermore, we estimate the power consumption scaling of the control unit of WSSs required for the realization of
SDM nodes. We showed that utilizing the INSPACE proposed switching scheme (i.e. J-Sw) will lead to significant
power savings compared to the Ind-Sw.
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Annex – abbreviations and acronyms
A/D: add/drop
AWG: arrayed waveguide grating
BP: blocking probability
BuSMF: bundle of single mode fibers
CD: colorless/directionless
CDC: colorless/directionless/contention-less
CDF: cumulative density function
ChBW: spectral channel width
DMGD: differential mode group delay
DSPaF: degenerate space-first
EDF: erbium-doped fiber
EDFA: erbium-doped fiber amplifier
FFT: fast Fourier transform
FMF: few-mode fibre
FM-MCF: few mode multi core fiber
FrJ-Sw: fractional-joint switching
GN: Gaussian noise
HPC: high port count
Ind-Sw: independent switching
J-Sw: joint switching
KSP: k-shortest path
LC: lane change
LP: linearly polarized
MC: multi-carrier
MCF: multi-core fibre
MC-FMF: multi-core few-mode fibre
MIMO: multiple-input multiple-output
MLR: multi line rate
MMF: multi-mode fibre
RA: resource allocation
ROADM: reconfigurable optical add drop multiplexer
RSMSA: routing, space, modulation level, and spectrum allocation
RSSA: routing, spectrum and space allocation
SA: spectrum assignment
SC: single-carrier
SDM: space division multiplexing
SE: spectral efficiency
SMF: single-mode fiber
SP: spectral penalty
Sp-Ch: super-channel
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TRC: total relative cost
TRx: transceiver
WSS: wavelength selective switch
XT: crosstalk
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