18 MediumAccessControl,Resource Management ...static.tongtianta.site/paper_pdf/b6e5325c-78a3-11e9-b777-00163e08bb86.pdf · Mbps, and the peak data rate of IEEE 802.11n can be 600

18 Medium Access Control, ResourceManagement, and Congestion Controlfor M2M Systems

Shao-Yu Lien and Hsiang Hsu

18.1 Introduction

The current mobile networks provide seamless and reliable streaming (voice/video)services to an increasing number of mobile users. From Global System for MobileCommunications (GSM) General Packet Radio Service (GPRS) and Universal MobileTelecommunication System (UMTS) to Long Term Evolution (LTE) and LTE-Advanced(LTE-A), the transmission data rates have increased tremendously. Relying on thedeployment of a heterogeneous network (HetNet) [1–5] involving macrocells, smallcells (femtocells and picocells), and/or relay nodes, ubiquitous support for basicmultimedia and Internet browsing applications has been tractable. In addition to mobilenetworks, wireless local area networks (WLANs) such as IEEE 802.11a/b/g/e/n/ac/axalso support moderate distance data exchange with best effort services or a certain levelof quality of service (QoS). As a result, it seems that the needs of human-to-human(H2H) communication applications can be satisfied using the existing network archi-tectures and technologies. However, to substantially facilitate human daily activities,providing only basic voice/video and Internet access services may be insufficient.

Recently, a novel communication paradigm known as machine-to-machine (M2M)communications has had a significant impact on the development of the next generationof wireless applications. Achieving “full automation” and “everything-to-everything”(X2X) connection facilitated by M2M communications has been regarded as two urgentand ultimate targets not only in the industry but also in the context of economics,social communities/activities, transportation, agriculture, and energy allocation [6].“Full automation” implies a significant enhancement of human beings’ sensory andprocessing capabilities, which embraces unmanned or remotely controlled vehi-cles/robots/offices/factories/stores, augmented/virtual/kinetic reality, and immersivesensory experiences. On the other hand, X2X connection implies that diverse entities,including human and machines, will be able to form new types of communitiesin addition to those based on H2H communication, such as social networks usinghuman-to-machine (H2M) and M2M communication [7–12]. The applications includeintelligent transportation systems (ITSs) [13], volunteer information networks (in whicheach entity in the network shares information with other entities to obtain a global view

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MAC, Resource Management, and Congestion Control 403

of the environment and the community) [14], the Internet of Things (IoT) [15–17], andsmart buildings/cities/grids [18–20], to name but a few.

To support these emerging applications, boosting the transmission data rate is onlyone of the diverse requirements for providing some of the necessary services. In addi-tion, the performance in terms of end-to-end transmission latency [6], energy efficiency,reliability, scalability, cost efficiency, and stability must be fundamentally enhanced.For example, ultra-low latency and reliable data exchange are particularly requiredfor unmanned or remotely controlled vehicles/robots, augmented/virtual/kinetic real-ity, immersive sensory experiences, and ITSs. On the other hand, as a largenumber of sensors may be involved in the IoT and smart buildings/cities/grids,scalability, cost efficiency, and energy efficiency are a primary concern. Unfortu-nately, the state-of-the-art radio access schemes (e.g., UMTS/LTE/LTE-A and IEEE802.11a/b/g/n/ac/ax) designed solely to optimize data rates may face an unprecedentedchallenge in meeting all the requirements for the two targets above. As a result,envisioning effective designs is of crucial importance. To satisfy the diverse applicationrequirements of M2M communications, novel designs of system architectures, mediumaccess control (MAC) schemes, and resource management techniques are needed.

18.2 Architectures for M2M Communications

18.2.1 WLAN Architecture for M2M Communications

In past decades, developments of WLANs such as IEEE 802.11 primarily emphasizeddata rate enhancement. For example, the peak data rate of IEEE 802.11b/g/n is 150Mbps, and the peak data rate of IEEE 802.11n can be 600 Mbps using four antennas.IEEE 802.11ac can achieve a peak data rate of 3.46 Gbps using eight antennas. A similardevelopment trend has also occurred in short-range communication networks such asnext-generation Bluetooth. However, if they are to be deployed in the 2.4 GHz and5 GHz unlicensed bands, the communication ranges of these systems are limited. Inaddition, the power consumption and the cost of the devices may also be unaffordablein M2M scenarios. The above concerns have consequently driven the development ofa new WLAN particularly designed for M2M/IoT communications, known as IEEE802.11ah [21–25].

In 2013, the Wi-Fi Alliance launched a system called “HaLow” based on IEEE802.11ah. “HaLow” is named from a combination of “ah” and “low” for “low-power.”The target of IEEE 802.11ah and thus HaLow is that it will be deployed in frequencybands under 1 GHz, to achieve a data rate of more than 100 kbps even if the communi-cation range exceeds 1 km, as illustrated in Figure 18.1. IEEE 802.11ah is designedto have a very low power consumption, which makes this system competitive withBluetooth and ZigBee. Layer 1 of IEEE 802.11ah partially adopts the physical layerfunctions of IEEE 802.11ac, including 256 quadrature amplitude modulation (QAM),beamforming, and multiuser multiple-input multiple-output (MIMO). However, the



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404 Shao-Yu Lien and Hsiang Hsu

IEEE 802.11ah (< 1 GHz, 100 kbps)

IEEE 802.11b/g /n (2.4 GHz, 150–600 Mbps)

IEEE 802.11ac/ax (5 GHz, 3466 Mbps)

IEEE 802.11ad (60 GHz)

IEEE 802.11ah (< 1 GHz, 100 kbps)

IEEE 802.11b/g /n (2.4 GHz, 150–600 Mbps)

IEEE 802.11ac/ax (5 GHz, 3466 Mbps)

IEEE 802.11ad (60 GHz)

Figure 18.1 The deployment frequency bands of IEEE 802.11ah for M2M applications are lessthan 1 GHz to achieve a data rate of 100 kbps.

bandwidth of IEEE 802.11ah is very limited as compared with 802.11ac (only 1, 2,4, 8, 16 MHz are supported).

18.2.2 Cellular Radio Access Network for M2M Communications

To support mobility, ubiquitous wireless services, QoS guarantees, and robustconnections, data exchange among machines may rely on the existing cellularinfrastructure of mobile networks. For this purpose, the standards for cellular-basedM2M communications were launched with the Third Generation Partnership Project(3GPP) Release 11 for machine-type communications (MTC) in 2012. The goal ofRelease 11 MTC was to utilize the 3GPP infrastructure, which provides (wired)connections between all stations. In Release 11 (i.e., LTE-A), these stations can beEvolved Universal Terrestrial Radio Access (E-UTRA) node Bs (eNB) in macrocells orpicocells, relay nodes (RNs), or home eNBs (HeNBs) in small cells. These stationsthus provide ubiquitous wireless access in both outdoor and indoor environments.By attaching to these stations, higher-layer connections between all MTC devicescan be provided. To fully leverage the 3GPP infrastructure, in Release 11, the Uuinterface (i.e., the air interface between an eNB/HeNB and a user equipment (UE))is reused as the air interface to connect an MTC device with the network, as shown inFigure 18.2. As a result, an MTC device is regarded as a special type of UE. Althoughthis paradigm quickly boosted the development of Release 11 MTC, new challengeshave consequently emerged.

Since 2011, 3GPP has been conducting a series of studies to investigate the charac-teristics of MTC traffic. The results show that the characteristics of traffic to supportMTC could be greatly different from those of conventional traffic flowing throughUMTS/LTE-A. The differences between MTC traffic and human communication trafficinclude infrequent transmissions (MTC devices may send or receive data at a low dutycycle), small data transmissions (MTC devices may only send/receive a small amountof data), time control (MTC devices can send or receive data only during an accessgrant time interval and the network must reject access requests, sending/receiving data,



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MTCdevice

MTCdevice

MTC

MTC

HeNB

HeNB

eNB

MME/S-GW/P-GW

The air interfacebetween E-UTRAN andMTC devices reused Uu

interface

HeNB GW

Operator domain

eNB

HeNB

eNBS1

S1

S1

X2S1

S1

X2

X2

RNRN

RN

MTCdevice

MTCdevice

MTCdevice

MTCdevice

MTCdevice

MTC

MTCdevice

MTCdevice

MTCMTCd

de

Figure 18.2 In 3GPP Release 11 MTC, the Uu interface (between a UE and an eNB) is reused asthe interface between an MTC device and an eNB. In Release 12 MTC, the Uu interface isfurther simplified. In this figure, X2 is the interface between the eNBs, while S1 is the interfacebetween an eNB/HeNB and a mobility management entity (MME), serving gateway (S-GW),packet data network gateway (P-GW), or HeNB gateway (HeNB GW).

and signaling of MTC devices within a forbidden time interval), and group-based MTC(for certain management or resource allocation purposes, the system must provide amechanism to associate one MTC device with one or more MTC groups).

To further support the above new traffic characteristics, the enhanced MTC (eMTC)standard was launched in Release 12 (see [26]). In eMTC, since an MTC device maytransmit/receive very small amount of data, link adaptation schemes such as adaptivemodulation and coding schemes, beamforming, and spatial multiplexing to supporthigh data rates are not utilized, to simplify the design of MTC devices. In addition,to support infrequent transmissions, the cell search and synchronization procedures forMTC devices are also simplified.

18.2.3 Heterogeneous Cloud Radio Access Network for M2M Communications

The heterogeneous cloud radio access network (H-CRAN) architecture is suitable forsupporting M2M communications which require high mobility and link reliability,



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such as ITS-based M2M applications. The concept of H-CRAN originates from thehierarchical network architecture of UMTS, in which each radio network controller(RNC) coordinates a number of NodeBs [27]. In UMTS, radio resource management(RRM) is conducted by each RNC, while NodeBs only perform physical signaltransmissions/receptions. Although RRM was subsequently implemented so that itcould be performed by an eNB in LTE/LTE-A/Evolved Packet Core (EPC), this conceptopens up the design of integrating the radio resource optimization in individual eNBsinto a joint optimization. Coordinated multipoint (CoMP) transmissions/receptions arethus a practical paradigm for joint resource scheduling/optimization among multipleeNBs [28–31]. In [32], I et al. proposed a novel evolution of CoMP to separate theradio resource optimization and front-end/baseband signal processing, in which theresource optimization is efficiently executed via cloud computing technology. Basedon this design, the baseband unit (BBU) and the radio head do not have to be collocatedwithin an eNB. Instead, a number of BBUs and remote radio heads (RRHs) can beseparated from an eNB and massively deployed. Using fiber-optic cables to connecteNBs and BBUs/RRHs, the coverage of eNBs can be extended. This architecture is thecloud radio access network (CRAN) [33–36]. Subsequently, Peng et al. [37] and Leiet al. [38] introduced the concept of H-CRAN. Using the wired or wireless interfacesprovided (i.e., S1, X2, and Un), not only BBUs/RRHs but also multiple HeNBs, RNs,and eNBs are able to exchange information for joint resource scheduling and allocation[6], as depicted in Figure 18.3.

By fully exploiting the technical merits of HetNets to densely deploy differentkinds of cells with different coverage, potential coverage holes can be effectivelyeliminated to adapt to all kinds of environment with H-CRAN. Furthermore, via cloudcomputing, a universal resource scheduling/allocation optimization can be achievedto mitigate potential (intercell or intracell) interference. Therefore, in the H-CRANarchitecture, high-mobility machines are able to improve their QoS. However, if it isdeliver enhanced wireless service experiences, the cost of H-CRAN can be high. Owingto the large number of machines involved in M2M communications, the complexity ofresource optimization in H-CRAN may be a growing and inevitable issue, even with thefacilitation provided by cloud computing technology. Second, along with the growingnumber of machines, the amount of traffic may increase by more than a thousandfold. Inthe H-CRAN architecture, all the traffic relies on the infrastructure for data transmissionamong machines. Such a star-like topology centered on the cloud for traffic flows mayeventually be a burden on both the fronthaul and the backhaul links. In H-CRAN, threefacts are generally ignored:

1. Traffic may be exchanged socially or locally among a group of machines. It isassumed that each packet from each machine may be delivered to any other machinein the world in H-CRAN. However, this assumption may not be generally true,as more and more social applications only require data exchange in close physicalproximity.



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BBU pools/MME/S-GW/P-GW

RN

Opticalfiber

Opticalfiber

Machine

Un

X2Sl

HeNB

eNB

RRH RRH

Figure 18.3 In H-CRAN, using the wired or wireless interfaces provided (i.e., S1, X2, and Un),not only BBUs/RRHs but also multiple HeNBs, RNs, and eNBs are able to exchangeinformation for joint resource scheduling and allocation. In this figure, a Un is the interfacebetween an eNB and an RN.

2. Each machine may exchange information with machines within its social networkmore frequently than with other machines. Such a social network may be a set ofservers and machines.

3. The downlink traffic to different machines or the uplink traffic from differentmachines may have strong correlations. For example, a group of densely deployedsensors measuring a common physical quantity may obtain similar results, and thusthe uplink data to the cloud may also be correlated.

As a result, the technical merits of H-CRAN also cause these engineering challengesto limit the performance of H-CRAN. This predicament thus motivates us to revisit thefog computing network (FogNet) architecture for M2M communications.

18.2.4 FogNet Architecture for M2M Communications

The concept of FogNet emerged from fog computing technology [39]. In contrastto cloud computing, fog computing facilitates processing and computing capabilitiesat edge entities, where not all information for performance optimization needs to be



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delivered to the cloud. Instead, those tasks (and the corresponding information foroptimization) that cannot be processed well by edge entities are handled by the cloud.Fog computing therefore may significantly alleviate the computing burdens in cloudcomputing and thereby achieve scalability (which is especially important for M2Mcommunications involving a large number of machines). Aryafar et al. applied theconcept of fog computing to form a new type of network architecture known as FogNet[40]. Under FogNet, a machine that has social messages to be exchanged with othermachines does not rely on traffic relaying via the cloud, and machines in close physicalproximity are able to share messages locally. This design leads to the concept ofsocially aware traffic management, where the aim is to significantly decrease the amountof traffic to be supported by the cloud. Recently, radio access technologies such asdevice-to-device (D2D) communications [41–46] using small cells as smart data/trafficrouting gateways, Bluetooth, and ZigBee have been successful examples where theuse of FogNet is possible. Subsequently, Peng et al. took the correlation among trafficto and from different edge entities into consideration further [47]. When a group ofmachines have highly correlated traffic to be delivered to the cloud, each entity does nothave to upload traffic individually. Instead, the common part of the traffic is deliveredonce by a single machine. This concept is also known as the swarm architecture forM2M networks. On the other hand, when the cloud has highly correlated traffic to beforwarded to multiple machines, it does not forward the traffic individually to eachmachine. Instead, the cloud selects only one machine to forward the traffic, and then theselected machine autonomously shares the traffic with other machines. Consequently,the amount of traffic supported by both the fronthaul and the backhaul links can bereduced.

Although the FogNet architecture has considerable technical virtues that allow oneto potentially tackle the issues of complexity, scalability, and heavy traffic burdens inH-CRAN, new challenges are also created. First, although data can be shared sociallyamong machines, there is no guarantee that all machines that require this service will beable to successfully receive the data. Therefore, reliability of data delivery becomesa primary concern. Second, mobility management and service continuity may notbe sufficiently supported in FogNet. Third, owing to the lack of adequate resourcecoordination among machines, interference may drastically impact the performance ofFogNet.

18.3 MAC Design for M2M Communications

The major features of M2M communications lie in (i) the very large number ofmachines, and (ii) infrequent transmissions of a large number of small amountsof data. Thus, an effective MAC is of crucial importance to eliminate interferenceand congestion among machines. Without an effective design, machines may suffercontinuous collision when a large number of machines are involved. MAC design canbe divided into two categories: scheduling-based MAC and random-access-based MAC.



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In this section, various MAC schemes for different M2M architectures to support theabove features of M2M communications are presented.

18.3.1 Grouping-Based M2M MAC in H-CRAN

In M2M communications, some applications (e.g., reports of measured data frommeters in smart grids and navigation signal transmissions in ITSs) require strict timingconstraints. Therefore, in M2M communications, providing hard QoS guarantees is animportant requirement. Although a lot of schemes and methods have been proposedfor providing QoS guarantees in H2H communications, these schemes can be difficultto apply directly to M2M communications. The reason is two-fold. First, the packetarrival periods in M2M communications can range from 10 ms to several minutesor hours owing to the infrequent-transmission feature. Such enormously diverse QoSrequirements cause difficulties in the design of radio resource allocation algorithmsfor M2M communications for providing guaranteed jitter performance, as the jitter(defined by the difference between the time of two successive packet departures andthe time of two successive packet arrivals) fully captures the timing performance ofperiodic traffic. Secondly, unlike the case of multimedia traffic, typically with burstypacket arrivals, each machine may only occupy a small amount of radio resource ineach transmission owing to the small amounts of data transmitted. Furthermore, a largenumber of machines may need to be supported by H-CRAN. As a consequence, thecomplexity of radio resource allocation for M2M communications can be high.

To support (hard) QoS guarantees in M2M communications, a scheduling-basedMAC may provide better performance owing to its capability to optimize resourceallocation. However, the major concern for a scheduling-based MAC originates fromcomplexity and scalability, which becomes extremely severe when it is requiredto support a large number of machines. The key for this to be applied to M2Mcommunications is to reduce the complexity. This idea thus motivates a grouping-basedM2M MAC. This grouping-based M2M MAC is a scheduling-based MAC. The mainidea is to group machines with similar QoS requirements and traffic characteristicsinto a cluster. Then, the network schedules radio resources for each cluster, instead ofallocating resources to individual machines, as shown in Figure 18.4. Within a cluster,all machines are associated with similar QoS requirements and traffic characteristics.Thus, resource sharing within a cluster may not be a challenging issue. In this case, thenumber of entities involved in the optimization is substantially reduced, and thereforethe complexity of scheduling optimization can be substantially reduced.

To implement a grouping-based M2M MAC, the network may need knowledgeregarding the QoS requirements and traffic characteristics of all machines. Thisknowledge is usually available in the H-CRAN architecture, while it may not beavailable in the FogNet and WLAN M2M architectures. For a cellular RAN architecture(i.e., MTC in Release 11/12), this knowledge may be available within a small numberof eNBs supporting CoMP. As a result, a grouping-based M2M MAC is favorable forH-CRAN and can be partially supported by cellular CRAN.



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Cluster 1 Cluster 2

H-CRAN

Time

Freq

uenc

y

Control channel

Radio resources no being used

Radio resources allocated to cluster 1

Radio resources allocated to cluster 2

Figure 18.4 In a grouping-based M2M MAC, machines with similar QoS requirements and trafficarrival patterns are grouped into a cluster. Radio resources are then allocated on a cluster basis.

18.3.2 Access Class Barring Based M2M MAC in FogNet/WLAN

MACs based on access class barring (ACB) were originally designed for access controlin the random access channel (RACH) of LTE/LTE-A [48, 49]. An ACB-based MACprovides different levels of access latency performance to support access requests fromusers in different priority classes. In LTE/LTE-A ACB, there are 16 access classes(ACs): AC 0−9 represent normal UEs, AC 10 represents an emergency call, andAC 11−15 represent specific high-priority services. In LTE/LTE-A ACB, an eNBbroadcasts access probabilities q and AC barring times for UEs corresponding to ACs0−9. The UE randomly draws a value p, 0 ≤ p ≤ 1. If p < q, then the UE proceeds tothe random access procedure; otherwise, the UE is barred for the duration of the ACbarring time. In this case, the channel access probability of each UE is basically underthe control of q, announced by the eNB. When the channel is severely congested, theeNB may announce a small value of q to prevent a large number of UEs trying to accessthe channel.

The ACB-based MAC is regarded as an effective random access MAC for theRACH of MTC devices. Figure 18.5 shows the performance in terms of access delayusing ACB, where 30000 MTC devices were involved in channel contention. In thissimulation, a Beta distribution was used as the traffic arrival model for each MTCdevice [48], and the AC barring time was 4 s. Figure 18.5 shows that when the barring



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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Barring factor q

2

4

6

8

10

12

14

16

18

20

90th

per

cent

ile d

elay

(s)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Barring factor q

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Suc

cess

rat

e

(a) (b)

Figure 18.5 (a) Access delay of MTC devices for different barring parameters q. (b) Success rateof MTC devices for different barring parameters q.

parameter q is less than 0.3, the 90th percentile delay exceeds 20 s. In other words,only when the number of MTC devices exceeds 30000 does the access delay becomeunacceptable (i.e., exceeding 20 s). These results demonstrate the effectiveness of ACBfor dealing with massive accesses by MTC devices.

Because of this performance, ACB is not limited to access schemes for the RACH ofLTE/LTE-A. ACB can be extended to a general M2M MAC scheme to prevent channelstarvation, especially in FogNet and LAN M2M architectures. However, if the numberof machines continues to increase, a very small q may be inevitable, which may lead tounacceptably large latency during contention. As a result, severe congestion may occuron a channel, and how to alleviate such channel congestion may be a crucial issue. Inthe next section, congestion control schemes for an ACB-based M2M MAC will bediscussed further.

18.3.3 Random-Backoff-Based M2M MAC

In a random-access-based MAC such as carrier sense multiple access (CSMA), (slotted)ALOHA or ACB, devices may suffer from continuous collisions when the number ofdevices is extremely large. Random backoff is a possible collision resolution scheme. Ina random-backoff-based M2M MAC, when a collision occurs, each machine randomlyselects a value that specifies the size of a backoff window. This backoff value is stored ina counter, and this counter counts down when a particular event occurs (e.g., at an idleslot or at each slot time). The machine can access the channel when its counter reacheszero. It is expected that a large backoff window size can alleviate the problem collisionsand thus facilitate the collision resolution. However, although a backoff-based MACcan provide performance improvements for a low congestion level, it cannot resolvea high congestion level when the channel is overloaded. A MAC scheme similar tothe backoff-based MAC is dynamic resource allocation. In this scheme, the networkdynamically allocates additional resources (in the time, frequency, or code domain) forrandom access based on the congestion levels and overall traffic load [50]. Although



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the dynamic allocation of random access resources can be effective in most casesin supporting M2M communications, the performance improvement is limited by theavailability of additional resources.

18.3.4 Harmonized M2M MAC for Low-Power/Low-Complexity Machines

It is well known that a scheduling-based MAC may provide better QoS guarantees,while a random-access-based MAC may have the engineering merit of simplicity inimplementation. However, the design of an effective MAC scheme generally doesnot take channel conditions into consideration. This may be reasonable in designs forH2H communications, in which a device is capable of implementing link adaptationtechnologies. Nevertheless, for M2M communications, low power and low complexitymay be critical design requirements in the implementation of the machines. Under thisconstraint, a machine may not be able to increase the transmission power to enhancethe received signal-to-noise ratio at the receiver side. A machine may also not be ableto combat severe channel fading through sophisticated channel estimation/equalization.In addition, for those M2M applications which require long-distance communication,the channel quality may have a crucial impact on the design.

In the literature, the integration of a scheduling-based MAC and a random-access-based MAC has been widely discussed to strike an engineering trade-off between QoSand simplicity. A harmonized MAC with scheduling and random access may providebenefits in terms of throughput enhancement for M2M communications that lack thecapability to combat channel fading. If a scheduling-based MAC is adopted for M2Mcommunications, when a machine requires a radio resource to upload/download datafrom/to the network, it needs to send a request to the network. If this resource request isgranted by the network, then the network may allocate a radio resource to the machine.However, when the network allocates a radio resource to a machine, there are twoobstacles obstructing the attempt of the network and machine to combat channel fadingon this allocated resource:

1. On the network side, although the network is able to perform channel estimationto avoid allocating a radio resource suffering from a channel fading condition, suchchannel estimation typically requires the machine to provide and transmit referencesignals to the network. However, transmitting reference signals may be a heavyburden for a low-power machine. As a result, the performance of channel estimationon the network side may be very limited.

2. Without effective channel estimation, a channel fading condition can be eliminatedon the machine side via channel equalization. However, as mentioned above, channelequalization may not be affordable for a low-complexity machine. As a consequence,when the network allocates a radio resource to a machine, both the network andthe machine are uncertain whether fading conditions may occur on this allocatedresource. If channel fading unfortunately occurs on the allocated resource, thentransmissions/receptions on this radio resource may not be successful. In other words,if the probability of occurrence of a fading channel on a radio resource is g, then the



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probability that a machine can successfully utilize this radio resource is 1− g, whichis completely subject to g.

To improve the probability that a machine will successfully utilize an allocated radioresource, one possible scheme is that the network allocates two radio resources to amachine which needs only one radio resource. In this case, the probability that themachine can successfully utilize at least one radio resource is 1 − g2, which is largerthan 1 − g. Nevertheless, the average resource utilization of each of these two radioresources is

(1− g2

)/2, which may be less than 1 − g. As a result, if the network

allocates more radio resources to a machine needing only one radio resource, then theprobability that a machine can successfully utilize at least one radio resource can besignificantly boosted. In this case, these radio resources allocated to a machine can beregarded as a resource pool. However, the average resource utilization of each resourcemay decrease drastically, which is especially unfavorable for M2M applications.

To enhance the average utilization of each resource, we observe that differentmachines at different geographic locations may suffer from different levels of channelfading conditions with the same resource. The network may thus allow other machinesto share the resource pool. In other words, the pool of radio resources is allocatedto more than one machine, and each machine can randomly select a radio resourcefrom within the pool to perform transmission. In this case, if multiple machines selectdifferent resources from each other, then this pool of radio resources can be fullyutilized. Nevertheless, if some machines select the same radio resource, then collisionsmay occur on that radio resource and the average utilization of each resource maydecrease. We note, particularly, that a pool of radio resources randomly shared bymultiple machines is exactly the concept of a random-access-based MAC.

The above discussions reveal a trade-off between a scheduling-based MAC anda random-access-based MAC for M2M communications. This trade-off depends onthe probability of deep channel fading. As a result, it is difficult to know whethera scheduling-based MAC or a random-access-based MAC is able to provide betteraverage utilization of each radio resource. In this case, there needs to be a harmonizationbetween these two sorts of MAC scheme. In other words, if g is smaller thana certain value, then a scheduling-based MAC should be adopted; otherwise, arandom-access-based MAC should be adopted, to optimize the average utilization ofeach radio resource for all values of q. Such a scheme where the radio access switchesbetween a scheduling-based MAC and a random-access-based MAC is known as aharmonized MAC. To derive the switching condition g analytically, we need to analyzethe average of each radio resource when a random-access-based MAC is adopted.

Given a resource pool composed of M radio resources indexed by m = 1, . . . ,M, to beshared by N machines indexed by n = 1, . . . ,N, let

Im,i ={

1, ith machine selects mth resource,

0, otherwise



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be an indicator function. The probability that the ith machine has one radio resourcethat allows it to transmit data without suffering deep channel fading is

Pr

{M∑

m=1

Im,i = 1

}= 1− gM ,

while the probability that the ith machine has no radio resource that allows it to transmitdata without suffering deep channel fading is

Pr

{M∑

m=1

Im,i = 0

}= gM .

The mean of∑M

m=1 Im,i is E[∑M

m=1 Im,i

]= ∑M

m=1 E[Im,i] = ME

[Im,i] = 1 − qM .

Thus, E[Im,i] = Pr

{Im,i = 1

} = (1− gM

)/M can be obtained. For each machine, the

probability that the mth radio resource is selected and no collision occurs on this radioresource is given by

Pr

{M∑

m=1

Im,i

}= N

(1− gM

M

)(1− 1− gM

M

)N−1

.

Assuming that the probability of deep channel fading on every radio resource isindependently and identically distributed (i.i.d.), the average utilization of each radioresource can be expressed as

ρ = N

(1− gM

M

)(1− 1− gM

M

)N−1

.

The optimum number of machines N∗ to share the pool of radio resources can be foundfrom

N∗ = arg maxN

{N

(1− gM

M

)(1− 1− gM

M

)N−1}

and N∗ can be obtained as

N∗ =

⎧⎪⎨⎪⎩

M

1− gM, if (1− gM)/M is an integer,⌊

M

1− gM

⌋, otherwise,

which leads to the optimum average utilization of each radio resource as follows:

ρ∗ =

⎧⎪⎪⎪⎨⎪⎪⎪⎩

(1− 1− gM

M

)(1−gM)/M−1

, if (1− gM)/M is an integer,⌊M

1− gM

⌋(1− gM

M

)(1− 1− gM

M

)�M/(1−gM) −1

, otherwise.



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Probability g of occurrence of deep channel fading

Ave

rage

util

izat

ion

of e

ach

radi

o re

sour

ce

Random-access-based MACScheduling-based MACHarmonized MAC

g = 0.63

Figure 18.6 Average resource utilization of a harmonized M2M MAC compared with two otherschemes.

On the other hand, when a scheduling-based MAC is adopted, the average utilizationof each radio resource is 1− g. Consequently, a random-access-based MAC should beadopted if ⌊

M

1− gM

⌋(1− gM

M

)(1− 1− gM

M

)�M/(1−gM) −1

≥ 1− g.

Since⌊M

1− gM

⌋(1− gM

M

)(1− 1− gM

M

)�M/(1−gM) −1

≈(

1− 1− gM

M

)M/(1−gM)−1

,

the above inequality can be rewritten as(1− 1− gM

M

)M/(1−gM)−1

≥ 1− g ⇒ g ≥ 1−(

1− 1

M

)M−1

.

When radio resources are abundant, we may use the approximation

limM→∞

(1− 1

M

)M−1

≈ 0.63.

From the above analysis, it can be seen that a harmonized M2M MAC shouldswitch to a random-access-based MAC if q ≥ 0.63; otherwise, it should switch to ascheduling-based MAC. Such a harmonized M2M MAC is illustrated in Figure 18.6.When the probability of channel fading is less than 0.63, a scheduling-based MACprovides better performance. In contrast, if the probability of channel fading is largerthan 0.63, then a random-access-based MAC can fully exploit “statistical multiplexing”arising from the random access of multiple machines. These results demonstrate that



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AP AP

APAP

AP

Machine

Machine under coverageof multiple networks

Machine under coverageof a single networks

Figure 18.7 Each machine may be located within overlapping coverage areas of multiple M2Mnetworks.

a harmonized M2M MAC is a promising scheme for low-power, low-complexitymachines.

18.4 Congestion Control and Low-Complexity/Low-Throughput MassiveM2M Communications

Owing to the feature of a large number of machines, traffic congestion may be aninevitable issue. In this section, a promising mechanism to alleviate traffic congestionin a Layer 2 ACB-based M2M MAC is consequently discussed.

18.4.1 Congestion Control in ACB-Based M2M MAC

As mentioned above, an ACB-based M2M MAC is effective for reducing the accesslatency when a large number of machines are involved in channel contention. However,when the number of machines continue to increase, a very small q may be inevitable,which may lead to unacceptably large latency during contention. As a result, severecongestion may occur on a channel. To alleviate access congestion on a channelfurther, we observe that, in practical deployments of M2M networks, each machinemay be located in overlapping coverage areas of multiple M2M networks, as shown inFigure 18.7. Each M2M network may support a different number of machines and thussuffer from different levels of congestion. If a machine suffering from severe congestioncan possibly access an M2M network with a lower congestion level, then the accessdelay (and thus the congestion) experienced by the machine can be reduced. The mainidea is to achieve balancing of the access load from machines between networks.

In an ACB-based M2M MAC, the congestion control of a network can be controlledvia deciding on an appropriate ACB parameter q. If the network is highly congested,then q may be set to a small value. On the other hand, if the network does not suffer



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from congestion, q may be set to a large value. For a machine located in overlappingareas of multiple networks, a simple strategy for a machine is to choose a networkannouncing a large q for access. However, such a simple strategy may not be optimal forachieving the optimum access balance. When each machine always selects the networkwith the largest q, then all machines may access that network simultaneously, and thismay lead to severe congestion on that network. As a result, the optimum networkselection strategy for each machine may be one in which the probability of selectinga network is proportional to the value of q announced by the network. In other words,if a network announces a larger g, then each machine may select that network with ahigher probability.

With our knowledge regarding the network selection strategy of machines, we knowthat the value of q announced by each access point (AP) of a network cannot be decidedindividually by each AP. Instead, all values of q announced by the APs must be decidedjointly. Specifically, suppose that there are |Nm| machines attempting to access the mthAP. In a conventional ACB-based M2M MAC, the ACB parameter qm of the mth APshould be qm = 1/ |Nm|, such that the expected number of machine access is qm |Nm| = 1.However, given the optimum network selection strategy of the machines, if |Nm| is large,then qm < 1/ |Nm| to force machines to access other networks. On the other hand, if|Nm| is small, then qm > 1/ |Nm| to attract more machines to access that network. As aconsequence, the joint optimization of all q values can be formulated as an optimizationproblem.

18.4.2 Massive MTC and Low-Complexity/Low-Throughput IoT Communications

In 2015, the International Telecommunication Union Recommendation Sector (ITU-R)launched its vision of International Mobile Telecommunications for 2020 and beyond,termed IMT-2020. Along with the trend of “full automation” and “X2X connection,”the targets of IMT-2020 and beyond include supporting very low-latency andhigh-reliability human-centric and machine-centric communications, supporting highuser densities, maintaining high quality at high mobility, enhanced multimedia services,the IoT, convergence of applications, and ultra-accurate positioning applications.Among these targets, the use cases of ultra-reliable/low-latency M2M communicationsand massive MTC (mMTC) are receiving significant attention. The use cases ofultra-reliable/low-latency M2M communications include wireless control of industrialmanufacturing and production processes, remote medical surgery, distributed automa-tion in smart grids, and transportation safety. The use cases of mMTC include sensorsand detectors. To support these use cases, relying solely on MTC/eMTC may notbe sufficient. As mentioned above, 3GPP MTC/eMTC reuses the Uu interface tosupport general use cases of M2M applications. Although MTC/eMTC may supportconnection reliability and mobility management, this paradigm may lead to highdeployment and implementation costs, which are particularly unfavorable for mMTC.These concerns are thus driving 3GPP to develop an alternative system supportingan ultra-low-complexity and low-throughput IoT [51]. LTE and LTE-A (and thusMTC/eMTC) are regarded as wideband solutions for M2M. Besides, MTC/eMTC,



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two narrowband solutions for low-complexity mMTC have been developed: ExtendedCoverage GSM (EC-GSM) and narrowband IoT (NB-IoT).

Owing to the engineering merits of simplicity, GSM may be feasible for low-complexitymachines. In addition, since GSM is an open-loop system (i.e., feedback channelsfor sophisticated link adaptation schemes such as hybrid automatic repeat requests,beamforming, MIMO, dynamic modulation, and coding schemes are avoided), it issuitable for low-throughput and low-power M2M applications. To be deployed in the800−900 MHz frequency bands (close to the frequency bands of HaLow), EC-GSMhas an extended communication range, and the evolution from GSM to EC-GSM canbe undertaken without significant system impacts.

In October 2015, 3GPP agreed to study the integration of EC-GSM and NB-IoT intoa single NB-IoT solution, and the first release of NB-IoT standardization was includedin 3GPP Releases 13 and 14. NB-IoT is also known as the clean-slate technology, andin Releases 13 and 14, three different operation modes are supported:

1. Standard-alone operation utilizes the spectrum currently being used by GSM and byEnhanced Data Rates for GSM Evolution (EDGE) Radio Access Networks (GERAN)systems as a replacement for one or more GSM carriers.

2. Guard-band operation utilizes the unused resource blocks within an LTE carrier’sguard band.

3. In-band operation utilizes resource blocks within a normal LTE carrier.

In particular, the following communication schemes are supported. The bandwidthfor both uplink and downlink transmissions is 180 kHz. For downlink transmissions,orthogonal frequency division multiple access (OFDMA) is adopted, and twonumerology options are being considered for inclusion: 15 kHz subcarrier spacing(with a normal or extended cyclic prefix) and 3.75 kHz subcarrier spacing. For uplinktransmissions, two options are being considered: frequency division multiple access(FDMA) with Gaussian minimum-shift keying (GMSK) modulation (see Section 7.3in [51]) and single-carrier FDMA (SC-FDMA) with quadrature phase-shift keying(QPSK) as a baseline modulation. In both downlink and uplink transmissions,single-tone (where one single 15 kHz or 3.75 kHz subcarrier is utilized as a carrier) andmultitone (multiple 15 kHz or 3.75 kHz subcarriers are utilized as a carrier) schemesmay be used. Table 18.1 summarizes the differences between MTC/eMTC, EC-GSM,and NB-IoT.

Similarly to the development of MTC/eMTC and EC-GSM, to support low-complexity,low-power, and low-throughput machines, a number of features supported byLTE/LTE-A are no longer supported by NB-IoT, including dual connectivity, carrieraggregation, real-time services, handover, relay, measurement reports, D2D, WLANinterworking, etc. Compared with LTE/LTE-A, since the bandwidth of NB-IoT isgreatly reduced, the duty cycle of NB-IoT is extended. In LTE/LTE-A, the lengthof a transmission time interval is 1 ms, which is also the basic time unit for radioresource scheduling. For single-tone transmissions with 15 kHz subcarrier spacing, oneschedulable time unit is 8 ms. For single-tone transmissions with 3.75 kHz subcarrierspacing, one schedulable time unit is 32 ms. For three-tone transmissions, one



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Table 18.1 MTC/eMTC, EC-GSM, and NB-IoT.

MTC/eMTC (Rel-11/12) NB-IoT (Rel-13) EC-GSM (Rel-13)Range ≤ 11 km ≤ 15 km ≤ 15 kmMaximum coupling loss 156 dB 164 dB 164 dBSpectrum Licensed (700–900 MHz) Licensed (700–900 MHz) Licensed (800–900 MHz)Bandwidth 1.4 MHz or shared 200 kHz

(180 kHz +20 kHz guard band) 2.4 MHz or shared

Data rate ≤ 1 Mbps ≤ 150 kbps 10 kbpsBattery life ∼ 10 years ∼ 10 years ∼ 10 yearsAvailability 2015 2016 2016

schedulable time unit is 4 ms. For six-tone transmissions, one schedulable time unit is2 ms. In Layer 2, the duty cycle for broadcasting network information is also extendedas compared with LTE/LTE-A. For example, in LTE/LTE-A, the master informationblock (MIB) is broadcast via the broadcast channel every 40 ms. In NB-IoT, the MIBis broadcast every 640 ms. In LTE/LTE-A, the system supports 20 system informationblocks (SIBs) carrying different sorts of network information, while in NB-IoT, onlyseven SIBs are used.

For link adaptation, stop-and-wait-based HARQ is supported in NB-IoT in both theuplink and the downlink. In the uplink, asynchronous adaptive HARQ is supported(i.e., there is no fixed time period between a data transmission and the correspondingfeedback acknowledgment (ACK) or negative ACK (NACK) messages). In thedownlink, ACKs/NACKs in response to uplink (re)transmissions are sent on thedownlink control channel. This design is very different from that in LTE/LTE-A, inwhich ACKs/NACKs are sent on the physical HARQ indication channel (PHICH).In other words, in NB-IoT, PHICH is avoided. The main idea of the above designis to simplify the implementation of low-complexity, low-power, and low-throughputmachines. As the required bandwidth of each NB-IoT carrier is limited to 200 kHz,it is expected that the design will accommodate more machines to support massivedeployment.

18.5 Conclusion

In this chapter, MAC and congestion control designs for some promising M2Marchitectures to support the next generation of M2M applications were discussed.These discussions demonstrate a top-down design approach, originating from M2Mapplications, to M2M networks and specific MAC and resource management designs.Considering that M2M applications may dominate wireless services in the comingdecades, the insights provided in this chapter may help provide an engineeringfoundation for practice in 5G mobile networks.



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