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AbstractCognitive radio sensing the spectrum to fully utilize
radio spectrum has been considered as a key technology toward
future wireless communications. We generalize this concept toward
communication and networking environment sensing to leverage
co-existing systems/networks in addition to opportunity
transmission like original cognitive radio, to create the
self-organized cognitive radio networking architecture for terminal
devices to fully utilize spectrum and co-existing systems/networks.
We use an example to demonstrate the advantages through cooperating
cognitive radio networks.
I. INTRODUCTION UE to the diverse application scenarios such as
different data rates and different propagation distances, a
good
number of international wireless communication standards have
been widely deployed in past years, in addition to 3G and popular
legacy 2G systems. Multiple standards may co-exist such as
well-known Bluetooth and WiFi at global available 2.4G Hz ISM band,
which results in co-existence standard like Bluetooth 2.0 and IEEE
802.15.2. With more applications into attention, Universal Mobile
Access (UMA) to combine both GSM/GPRS at 1.8-2G Hz band and
Wireless LAN at 2.4G Hz bands first introduces an international
effort to allow multiple-standard multiple-band system into
realistic ubiquitous wireless applications, while software defined
radio (SDR) is considered as a mean to facilitate such a concept.
Since the pioneer research on cognitive radio by J. Mitola [2-3]
and FCCs regulations to facilitate cognitive radio, it has been
considered as one key technology for future wireless communications
and ubiquitous networking to fully utilize spectrum efficiency. In
other words, instead of developing a universal wireless
communication system governing all kinds of applications that
requires tremendous and revolution efforts in establishing
infrastructure and replacement of billions mobile terminals, an
intelligent terminal device who can learn communication
environments (available frequency spectrum, available
infrastructure and/or systems at license
This research was supported in-part by the National Science
Council and
National Telecommunication Program under the contracts NSC
96-2219-E-002-008 and NSC 95-2923-I-002-001-MY2.
Kwang-Cheng Chen, Ling-Hung Kung, and David Shiung are with the
Institute of Communication Engineering, National Taiwan University,
Taipei, Taiwan 106 e-mail: [email protected]
Ramjee Prasad is with the Center for TeleInFrastruktur, Aalborg
University, Aalborg, Denmark. e-mail: [email protected].
Shihi Chen is with the Institute for Information Industry,
email: [email protected].
band or unlicensed band, etc.) and can adapt communication to
meet own networking purpose of quality of services (delay, jitter,
cost, etc.) shall be desirable for future wireless communications.
In this paper, we would like to propose complete terminal device
architecture to realize cognitive radio networking and general
convergence of Internet [1] applications, to connect various
consumer devices, such as PC, mobile phone, game station, TV, etc.
under various systems.
To distinguish between software radio and cognitive radio, we
adopt a simple concept by defining software radio to adjust system
parameters over a processor-based platform (that is usually
facilitated by digital signal processor(s) to execute physical
layer transmission receiver functions), so that one platform can
serve multiple system specifications. A cognitive radio shall be
able to sense the communication environments, including spectrum
sensing, so that the device is able to self-organize appropriate
communication and networking functions through re-configurable
communication/network processor(s). By this point of view, we
consider the entire communication/networking as multiple-standard
systems co-existing in time, frequency, and spatial (geographical
location or distance) domains. It is a generalization from
traditional cognitive radio definition that the secondary system is
allowed by leveraging idle radio resources (in time and/or
frequency domain) of the primary user system. We use a popular
scenario to demonstrate advantages of such cognitive radio
networking architecture.
II. RATE-DISTANCE NATURE OF WIRELESS COMMUNICATIONS Assuming a
primary communication system (or pair(s) of
transmitter and receiver) is functioning; a cognitive radio that
is the secondary user for the spectrum explores channel status and
seeks possibility to utilize such spectrum for communication. The
channel can be commonly modeled as an Elliot-Gilbert channel [6,14]
with two possible states: existence of primary user(s) (a state not
allowing any secondary user to transmit), and non-existence of
primary user (a state allowing secondary user(s) to transmit). In
addition to many exciting research earlier, such as [7-8], we would
like to exploit a critical and practical nature in wireless and
thus cognitive radio systems. The rate-distance relationship, which
has not been drawn a lot of attention but is critical in
state-of-the-art wireless communication systems. Let us illustrate
this observation from a realistic IEEE 802.11 a/g OFDM PHY and MAC
[4] as Figure 1. Due to the received power level, the system will
automatically adjust PHY transmission rate accordingly, and
Self-Organizing Terminal Architecture for Cognitive Radio
Networks
Kwang-Cheng Chen, Ling-Hung Kung, David Shiung, Ramjee Prasad,
Shihi Chen
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thus throughput via MAC. It is common in state-of-the-art
wireless communication systems.
If we consider the propagation distance between transmitter and
receiver to have correspondence received power, we may create a new
model for such a feature of wireless communications, and we may
call it as rate-distance feature of wireless communications. We may
further consider such a distance as a measure of signal received
power, rather than Euclidean distance nor propagation distance, to
characterize propagation factors for networking operation like
[17]. Consequently, distance measure D means any possible location
point with received signal power as propagation Euclidean distance
D under certain long-term fading. Figure 2(a) illustrates
rate-distance feature and the system having two transmission rates
as an example. Figure 2(b) shows maximum allowable interference
caused by the secondary user(s) to primary users system at the
origin. It may be generally considered that lower rate transmission
is more vulnerable to such interference. Consequently, as Figure
2(c) shows, a secondary user transmission rate/power can be
scheduled without affecting primary user(s). Therefore, in case a
cognitive radio senses possible opportunity to transmit, its
transmission rate (and thus power) is determined by the following
rate-distance conditions: (a) Channel capacity in fading channel in
terms of
rate-power allocation (b) Interference level by co-existing
operating system(s) (c) Maximal tolerable interference to active
primary
system user(s) (d) Effective distance relationship among primary
and
secondary user devices
Figure 2 actually depicts the worst case scenario of
cognitive
radio communication, and more rate-distance nature can be
leveraged such as Figure 3. The base station and mobile station in
the primary system are communicating. Due to their effective
distance, the low-rate is selected. Near the boundary of the cell
(according to base station), there are two cognitive radio devices
wishing to establish communication under the low-level of
interference from primary system. As the Figure showing, high-rate
communication might be possible between these two cognitive radios
without affecting primary system, and the interference from active
primary system nodes to cognitive radios can be tolerated.
As a matter of fact, multiuser detection (MUD) can be applied
here to alleviate co-channel interference for cognitive radios, as
cognitive radios know communication status of primary system users.
From initial synchronization to user identification, all can be
jointly determined [21-22]. It is not limited to CDMA
communications. In [18], it is shown that OFDM communications can
utilize MUD to cancel co-channel interference, without precise
knowing primary users. Based on the development of adaptive
modulation [11-13], we may summarize a mathematical condition to
determine rate-power for secondary cognitive radios.
III. DEVICE ARCHITECTURE AND COGNITIVE RADIO DESIGN Figure 4
depicts the device architecture of our proposed
self-organized cognitive radio, which consists of several major
functional blocks: n Cognitive Radio: It recognizes wireless
communication
environments and co-existing systems/networks. n
Software-Defined Radio: Based on the decision of
self-coordinator, SDR configures to appropriate transceiver
parameters for communication of mobile device. [24] provided an
example to fully program SDR for OFDM to CDMA.
n Re-configurable MAC: Self-coordinator also determines best
possible routing among available systems/networks, and
re-configurable MAC adjusts to proper subroutines in a universal
access protocol machine.
n Network-layer procedures: Self-organized coordinator instructs
right network layer functions such as radio resource allocation,
mobility management, etc. to complete wireless network
operation.
n Self-organized communication/networking coordinator: The brain
of terminal device determines (1) decent access
BaseStation
MobileStation
PrimaryCommunicationLink
SecondaryCommunicationLink
High-rate region forprimary communication
Low-rate region forprimary communication
High-rate region forsecondary communication
Low-rate region forsecondary communicationwithout affecting
primaryhigh-rate communication
Figure 3: Rate-Distance Nature of Co-existing
Primary/Secondary
Communications.
ReceivedPower
(a)
Distance betweenTransmitter & Receiver
ReceivedPower
(b)
Distance betweenTransmitter & Receiver
Channel Capacity
High Transmission RateLow Transmission Rate
Maximum AllowableInterference to Primary System
Distance betweenTransmitter & Receiver
Channel Capacity
Maximum Allowable Interference to Priamry System
Secondary User Transmission Power
ReceivedPower
(c)
Figure 2: ate-Distance Feature of Cognitive Radio.
n IEEE 802.11a/g OFDM transmission with RF factorsn Payload=1000
bytes; DATA rate =ACK raten IEEE 802 100 nsec delay spread fading
channel
Figure 1: Received Power versus IEEE 802.11a/g OFDM PHY Data
Rate (and thus MAC Throughput)
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network route based on cognitive radio information (2)
configuration of proper hardware and software of cognitive radio
(3) maintenance of users communication need for terminal
device.
n RF: It might consist of several sub-band RFs to cover right
frequency range, with capability of adjustable RF filtering to fit
selected system parameters.
The cognitive radio along with interaction of self-organized
coordinator and SDR can be summarized in Figure 5. We temporarily
do not consider the circuit reuse situation under this scheme, in
this paper.
It is well known that cognitive radio is centered around
spectrum sensing [7-8]. However, as Figure 5 shows, there are a
lot more information needed to practically make a good sensing, not
only in spectrum, but also in some networking functions, as a
generalized sensing (or cognition). We categorize such
spectrum/network sensing features into: n RF Signal Processing
includes (carrier) frequency, signal
bandwidth, signal strength (RSSI), SINR estimation n BB
Pre-detection Signal Processing includes, symbol rate,
carrier and timing, pilot signal, channel fading n BB
Post-detection Processing (some might be done
pre-detection stage) includes system/user identification,
modulation parameters, FEC type and rate, MIMO parameters,
transmission power control
Networking Processing information includes multiple access
protocol or MAC, radio resource allocation (such as time slot,
sub-carrier, code, allocation), ARQ & traffic pattern (ABR,
CBR, or VBR), routing or mobility information. The purpose for
above list is to execute spectrum sensing, identification of
co-existing systems/networks, and then operation of such
co-existing systems/networks. [11,19,22-23] are some
examples to facilitate partial functions in the list. The
cognitive radio cycle to show working flow is therefore summarized
as Figure 6. [3,7] had developed cognitive cycle concepts. Since we
generalize to cognitive radio network along with rate-distance
concept, it primarily distinguishes new novel features here.
Cognitive radio functions not only sensing spectrum and fitting
spectrum resource, but also sensing networking environment and
adapting into cognitive routing in the network level.
IV. RE-CONFIGURABLE MAC Medium access control (MAC) of wireless
networking for
mobile/ubiquitous computing has been another fundamental element
in addition to radio transceiver. [20] described some fundamental
challenges for wireless networks in fading channels and principles
to resolve them. After a series of efforts, a unified MAC algorithm
is presented in [16] to execute most well-known access protocols,
via leveraging the concept that multiple access conducts either
carrier sensing (generalized as collision avoidance) or collision
detection, to form CATE and CRTE (collision avoidance/resolution
tree structures to generally represent all protocols) [27].
Re-configurable MAC Algorithm RP_1 if(access method = blocked)
{
allow new arrivals during previous cycle DN;} if
(memoryless_after_lost is set) { have all noted in DN call
CATE(type_CATE;)} else{ unmarked nodes in DN call CATE(type_CATE);
associate marked nodes in DN to group number #(original group
number -g);} unmarked nodes in CN call CRTE(type_CRTE); associate
marked nodes in CN to group number # (original group numbers -g);
if (report grouping result is set) { all nodes report the grouping
result back;} set g=1; // start to process each group RP_2
if(access method=free){ nodes with new arrival packets during the
processing of group #(g-1) TX(g);} nodes in group #g TX(g); process
group #g with GP(gp_scheme); if (there is no transmission){ g++; if
(G is set ){ // G is the maximum TE size
Radio/WirelessMedium
RF Analysis BB-PHYAnalysis
System/UserAnalysis
NetworkAnalysis
Channel State &Rate-Distance
Self-OrganizedCoordinator &
SpectrumUtilizationDecision
RadioResource
Re-configurableMAC
SDR
AD/DA &Filtering
RF
Packets/Traffic
Figure 6: Self-Organized Cognitive Radio Cycle
RF AD BB-DSP MAC & NetworkFeatures
FrequencyBandwidthRSSISINR Estimation
Symbol RateCarrier & TimingPilot SignalChannel Fading
System/User IdentificationModulation ParametersFEC Type &
RateMIMO ParametersPower Control
Multiple Access ProtocolRadio Resource AllocationARQ &
Traffic PatternRouting/Mobility Information
Sensing/Cognitionof Cognitive Radio
Self-OrganizedCoordinator
Decision of SpectrumOptimization & Utilization
RF AD/DA &Filtering BB-SDRRe-configurable
MACNetwork
Functions
Packets
Assisted Information for MUD
Channel State &Rate-Distance
Figure 5: Self-Organized Cognitive Radio Hardware Design
CognitiveRadio
SoftwareDefinedRadioADC
DAC
Re-configurableMAC & Networking Protocol
RF-1 RF-N
Radio Resource AllocationBandwidth/Channel Allocation
Handoff/RoamingRouting, QoS, Security
Coordinatorof Self-
OrganizedCommunication& Networking
.
PHY
Data Link& MAC
Network
..
Applications & Services
Figure 4: Self-Organized Cognitive Radio Device Architecture
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if(g>G){ goto RP_1;} else{goto RP_2;}} else{goto RP_2;}}
elseif(there is transmission){ if(access method=freee){ nodes in
group #(g+1) to group #(g+t) DN;} //t is the duration of the
transmission If(the transmission is a success){ the successful node
removes the transmitted pracket from buffer; if (completeness is
set){ g++; if(g>G){goto RP_1;} else{goto RP_2;}} else{ if
memoryless_after_lost is set){ mark the loser in CN;} else{mark the
loser in CN and DN;} current cycle ends and goto RP_1;}} else{
collided nodes CN; if(completeness is set){ g++; if(g>G){goto
RP_1;} else{goto RP_2;}} else{ if(memoryless_after_lost is set){
mark the loser in CN;} else{mark the loser in CN and DN;} current
cycle ends and goto RP_1;}}}
ALOHA
with Random backoff
Basic Q-ary CRA
p-persistent CSMA
CSMA/CA
GRAP
Slot time One transmission + One feedback
One transmission + One feedback
One Propagation Delay
Defined in Spec.
One propagation delay
Access method
Free Free Free Free Blocked
Completeness
No No No No Yes
Memory-less after loss
No Yes No Yes No
Report grouping result
No No No No Yes
Group process scheme
Two way handshaking
Two way handshaking
Two way handshaking
Four way handshaking
Polling
Type of CATE
None None Geometric.CATE
BEB.CATE
Uniform.CATE
Type of CRTE
Geometric.CRTE or BEB.CRTE
Q-ary CRA.CRTE
Geometric.CRTE
BEB.CRTE
Uniform.CRTE
Table 1: Re-configuration of MAC parameters
V. COOPERATIVE NETWORKING Following above architecture,
self-organized coordinator
schedules right networking functions in routing to control QoS,
and decides appropriate configuration of MAC, software radio
communication parameters, and RF parameters. Typical approach [10,
26] toward self-organized wireless communications looks into
topology control of the entire possible networks/systems, and
optimizes based on different criterion. UMA even considers the
update of infrastructure. Toward a practical realization, we
consider the problem from a different angle, that is, a terminal
determines routing based solely on information available to radio
access networks. The radio access network can be a part of digital
cellular like UTRAN, an access point of Wireless LANs connected to
Internet, or a base station (or a subscriber station in mesh
network) in WiMAX. We therefore generally assume users from K
systems that are operating within certain geographical area, and
devices can access all operating frequency bands. Traditionally one
mobile device capable of operating in one system cannot operate in
another system, and resources within these K systems may not be
evenly distributed in that some of the systems may be crowded
whereas others may have no or little traffic. Through cognitive
radio, we shall leverage possible cooperation among these systems
to improve the individual and overall performance. The primary
challenge is to determine right cooperation among various
combinations of systems to enhance performance or QoS. Please note
that the users may want cost as a performance measure in practical
applications. Without loss of generality, we consider a circuit
switching (CS) network (such as 2G/3G cellular) with n1 users and a
packet switching (PS) network (such as WiFi) with n2 users. These N
= n1 + n2 users to operate cognitive mode between such systems, we
want to demonstrate effective routing to enhance overall cognitive
radio network performance as the following figure. The packet loss
is due to collision with retransmission, and the number of users in
the network is also assumed to be relatively steady.
1n
2n
Figure 7: Queuing Model of Cooperative Access Networks Access
Network 1: CS network, modeled as a multi-server
queue with N1 servers. The service rates of N1 servers are
all
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deterministic and equal to 1. A certain user is served by only
one server and no more than N1 users can be admitted into this
network.
Access Network 2: PS network, modeled as a single-server queue
with a multiple access device in front of the server to decide
which user has the right to access media. The service rate of this
server is deterministic with rate 2, and the multiple access scheme
is assumed to be slotted ALOHA with retransmission probability
q.
We consider two types of traffic in services: CBR and ABR
(representing voice and data), while arrival rates of the same
service type are assumed to be equal. CBR: Deterministic arrival
rate c, number of users in the PS network Nc, delay bound c. ABR:
Poisson arrival rate with mean a, number of users in the PS network
Na. We measure the average delay in the wireless end. The overall
performance is to calculate the average delay of all users:
1
1[ ]N
ii
E D DN =
=
and compare whether this value is smaller with cooperation. The
individual performance is to calculate the average delay for users
within one specific network:
,1
1[ ] 1, 2kn
k i kik
E D D kn =
= =
We further embed cost model into our analysis. For a user to use
system k, she/he needs to pay Pk dollars to access it, k = 1, 2. We
can calculate the average price of all users
1
1[ ]N
ii
E P PN =
=
The cognitive radio shall route traffic based on the selection
criterion: small enough delay to satisfy delay bound for CBR
service and to lower average price. We then derive mathematical
equations for average delay of a single user.
(i) CBR via AN1: For services with arrival rate c < 1, this
is a D / D / 1 queue, and the delay would be a constant
DC,1 = 1 / 1 (ii) ABR via AN1: For services with arrival rate a
< 1, this is modeled as a M / D / 1 queue and the average delay
would be
1,1
1 1
1[ ]2( )
aA
a
E D l mm m l
= +-
(iii) CBR via AN2: Assume in AN2 there are Nc 1 other CBR users
with the same arrival rate. There is no ABR user. The average delay
can be found in [1] to be:
,, 2
2 ,
11 1[ ]2
s cC
s c
p qE D
p qm - +
= +
where q is the retransmission probability and ps is the
probability of successful transmission. For Nc = 1, ps,c = 1, while
for Nc > 1, ps,c = (1 c / 2)(Nc 1).
(iv) ABR via AN2: Assume in AN2 there are Na 1 other ABR users
with the same arrival rate. There is no CBR user. The average delay
can be seen from [26] to be:
,,2
2 ,
11 1[ ]2
s aA
s a
p qE D
p qm - +
= +
where q is the retransmission probability and ps is the
probability of successful transmission. For Na = 1, ps,a = 1, while
for Na > 1, ps,a = exp(-a(Na 1) ). We evaluate the following
scenario with the relationship between different rates as a = c =
0.11 = 0.022. The average delay for one CBR or ABR service is shown
in Fig. 8 and the unit slot time for delay is equal to 1 / 2. We
also assume that P1 > P2.
If we set the delay bound c to be 8 time slots, then AN2 can
take up to 15 CBR services while not maintaining appropriate
quality. Say n1 = n2 = 8, N1 = 10, we then have 1n = 1, 2n = 15,
and
4 .1 6 w ith o u t c o o p e ra tio n[ ]
7 .3 4 w ith c o o p e ra tio nE D =
2
3 .3 1 w ith o u t c o o p e ra tio n[ ]
7 .4 9 w ith co o p eratio nE D =
1 2 1 21 1 1 15[ ] [ ]2 2 16 16
E P P P P P E P = + > + =
In this case, some of the old CBR users in AN1 would switch to
AN2 due lower price and acceptable delay. For ABR users, since
there is no delay limit, users in AN1 would switch to AN2 until the
PS network can no longer support such that its throughput starts to
decrease. In the above numerical example, the effectiveness of
cooperative self-organized (by mobile device only) cognitive radio
networking is consequently successfully verified.
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2 4 6 8 10 12 14 160
2
4
6
8
10
12
14
Number of users in the network
Ave
rage
del
ayCBR delay in scenario 1
AN1 AnaSim w/o bufSim w/ buf
2 4 6 8 10 12 14 160
2
4
6
8
10
12
14
Number of users in the network
Ave
rage
del
ay
ABR delay in scenario 1
AN1 AnaSim w/o bufSim w/ buf
Figure 8 Performance of Self-organized Cognitive Radio in
Cooperative Access Networks
VI. CONCLUSION We demonstrated a practical architecture design
of
self-organized cognitive radio based on the newly found
rate-distance nature, to meet future wireless communication need,
which consists of precise/novel cognitive radio structure,
corresponding cognitive cycle, re-configurable MAC design, and
finally self-organized coordinator to determine appropriate routing
traffic to enhance overall utilization and efficiency of wireless
networks. Such cognitive radio achieves not only spectrum
efficiency but also more importantly the networking efficiency of
entire wireless networks in picture, which facilitate the cognitive
radio networks to support better networking efficiency at a given
bandwidth.
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