Cognitive Ubiquitous Computing

Kwang-Cheng Chen, Fellow, IEEE, Feng-Seng Chu, Shin-Ming Cheng, Sheng-

Yuan Tu, Shih-Chun Lin,Yu-Yu Lin, Po-Yao Huang, Ao Weng Chon

Institute of Communication Engineering

National Taiwan University

No. 1, Sec. 4, Roosevelt Road.

chenkc@cc.ee.ntu.edu.tw

Abstract

1. Introduction

Accessing information services anytime/anywhere is a beautiful scenario of

human technology [16] while it is still a dream until now. One of the main

challenges prevents us from it is how to make the pocket computing devices

adapting to environments. In general, such adaptation means connection with

outside instruments. For example, to provide internet roaming on or data

retrieving, seamless wireless network connectivity should be guaranteed. And if

GPS and mobile TV are wanted, information delivery from dedicated server is

necessary.

However, such capability is not so easy to be achieved as mobile users

change their locations among buildings and streets, or stand at shadowed places.

Under those conditions, maintaining the wireless connection via a specific radio

access technology (RAT) may not be possible due to its limited coverage.

Therefore, cognitive radio which lets devices dynamically re-configure

themselves to connect to varying RATs or transmit over unlicensed bands [3]

[20], and cooperative relay which allow devices relay data of other devices [21]

are both considered as expecting technologies to achieve anywhere wireless

connections.

Under this scenario, all users communicate with each other via cognitive

radio forming a cognitive relay network to realize the ubiquitous computing.

However, there are some technical challenges before we can buildup such

systems. Firstly, in initialization phase the device should aware the environment

that it is operating and takes corresponding actions. It is critically depending on

the capability of the devices to sense existing and available RATs, and to judge

the network condition accordingly. To complete this work, the general sensing

and tomography are included in our discussion.

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Secondly, all the decisions about utilizing available frequency bands is

depending on the results of sensing and tomography. However, due to the

heterogeneity of the network, fluctuant of wireless channel and mobility of relay

nodes. This results is varying with time, to utilize such all the available resource

in an optimal way, we then study the MAC and spectrum sharing in the

following.

Thirdly, the ultimate goal of the system is networking to provide

anytime/anywhere services. Therefore, based on the completion of resource

management, routing and QoS control become the next issues. Furthermore, in

such a dynamic network, any erroneous transmission may not be recovery by

traditional ARQ/HARQ because the original path may become unavailable. To

deal with all the challenges as a whole, both routing and HARQ in this scenario

are considered.

Finally, as we said, one of the most important feature of this systems is, it can

leverage different RATs to complete its transmission. However, such

mechanism needed be designed carefully since each RAT provides different

quality of links, and it should be considered jointly with the requirements of

application. To finalize the target, we integrate network selection into this paper

in the last part.

2. CRN General Sensing and Tomography [杜勝元]A. General Spectrum Sensing

Traditional spectrum sensing mechanisms, focusing on physical layer

detection or estimation at CR transmitter (i.e. CR-Tx), ignore the spectrum

availability at CR receiver. Due to existence of fading channels and noise

uncertainty along with limited sensing duration, even when there is no

detectable transmission of PS during this venerable period, the receiver of this

opportunistic transmission (i.e. CR-Rx) may still suffer from collisions from

simultaneous transmission(s), as Fig. 1 shows. The CR-Rx locates in the middle

of CR-Tx and PS-Tx and PS activities are hidden to CR-Tx, which induces a

challenge to spectrum sensing. We can either develop more powerful sensing

techniques such as cooperative sensing to alleviate hidden terminal problem, or

a more realistic mathematical model. Traditional spectrum sensing mechanisms

could be explained by a mathematical structure of defining link availability.

Definition 1: CR link availability, between CR-Tx and CR-Rx, is specified by

an indicator function

1link={1 CRlink is available for opportunistic transmission0 otherwise (1)

Definition 2: CR-Tx senses the spectrum and determines link availability based

on its observation as

1Tx={1 CRlink is available for transmissionat CR−Tx0 otherwise (2)

Lemma 1: Traditional spectrum sensing for CR link suggests 1link=1Tx

As we explain in Figure 1 and/or take interference into testing scenario, we

may note that Lemma 1 is not generally true. To generally model spectrum

sensing, including hidden terminal scenarios, we have to reach two

simultaneous conditions: (1) CR-Tx senses the link available to transmit (2)

CR-Rx can successfully receive packets, which means no PS signal at CR-Rx

side, nor significant interference to prohibit successful CR packet reception (i.e.

beyond a target SINR). Therefore, CR link availability should be composed of

localized spectrum availability at CR-Tx and CR-Rx, which may not be

identical in general and is rarely noted in current literatures. [23] developed a

brilliant two-switch model to capture distributed and dynamic spectrum

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availability. However, [23] focused on capacity from information theory and it

is hard to directly extend the model in studying network operation of CRN.

Actually, two switching functions can be generalized as indicator functions to

indicate the activities of PS based on the sensing by CR-Tx and CR-Rx

respectively [11]. Generalizing the concept of [23] to facilitate our study in

spectrum sensing and further impacts on network operation, we represent the

spectrum availability at CR-Rx by an another indicator function.

Collisions at CR-Rx!

CR-Tx Range

CR-Tx CR-Rx PS-Tx

PS-Tx Range

PS-Rx

Fig 1. Hidden terminal problem. CR-Rx lies in the middle of CR-Tx and PS-Tx and PS-Tx is hidden to CR-Tx.

Definition 3: The true availability for CR-Rx can be indicated by

1Rx={1 CRlink is available for reception at CR−Rx0 otherwise (3)

To satisfy two simultaneous conditions for CR link availability, CR link

availability can be represented as multiplication (i.e. AND operation) of the

indicator functions of spectrum availability at CR-Tx and CR-Rx.

Proposition 1: 1link=1Tx 1Rx

The next challenge would be that 1Rx cannot be known a priori at CR-Tx,

due to no centralized coordination nor information exchange in advance among

CRs when CR-Tx wants to transmit. As a result, general spectrum sensing turns

out to be a composite hypothesis testing. In this paper, we introduce statistical

inference that is seldom applied in traditional spectrum sensing to

predict/estimate spectrum availability at CR-Rx.

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Further examining Proposition 1, we see that prediction of 1Rx is necessary

when 1Tx=1, which is equivalent to prediction of 1link. In this paper, we model

1Rx when 1Tx=1 as a Bernoulli process with the probability of spectrum

availability at CR-Rx Pr (1Rx=1∨1¿¿Tx=1)=α ¿. The value of α

exhibits spatial behavior of CR-Tx and CR-Rx and thus impacts of hidden terminal problem. If α is large, CR-Rx is expected to be close to CR-Tx and

hidden terminal problem rarely occurs (and vise versa). The prediction of 1Rx at CR-Tx can be modeled as a hypothesis testing, that

is, detecting 1Rx with a priori probability α but no observation. To design

optimum detection, we consider minimum Bayesian risk criterion, where

Bayesian risk is defined by

R=w Pr (1link=0∨1¿¿Tx=1)PF+Pr (1link=1∨1¿¿Tx=1) PM ¿¿(4)

In (4),PF=Pr (1̂link=1∨1link=0 ,1¿¿Tx=1)¿,

PM=Pr (1̂link=0∨1link=1 , 1¿¿Tx=1)¿, and w ≥ 0 denotes the

normalized weighting factor to evaluate costs of PF and PM, where 1̂link

represents prediction of 1link=0.

Since 1Rx is unavailable at CR-Tx, we have to develop techniques to

"obtain" some information of spectrum availability at CR-Rx. Inspired by the

CRN tomography [4], we may want to derive the statistical inference of 1Rx

based on earlier observation. It is reasonable to assume that CR-Tx can learn the

status of 1Rx at previous times when 1Tx=1, which is indexed by n. That is, at

time n, CR-Tx can learn the value of 1Rx [n−1 ] ,1Rx[n−2] ,⋯ . In other

words, we can statistically infer 1Rx [n] from

1Rx [n−1 ] ,1Rx [ n−2 ] ,⋯ ,1Rx [ n−L ], where L is the observation depth. This

leads to a classical problem from Bayesian inference.

Lemma 2: Through the Laplace formula, the estimated probability of spectrum

availability at CR-Rx is

α̂= N+1L+2

(5)

where N=∑l=1

L

1Rx [ n−l ].

Proposition 2: Inference-based spectrum sensing at CR-Tx thus becomes

1̂link={1Tx α̂ ≥ w /(w+1)0 otherwise

(6)

Remark: CR-Tx believes CR link is available and forwards packets to CR-Rx if the probability of spectrum available at CR-Rx α is high enough. Otherwise,

CR-Tx is prohibited from using the link even when CR-Tx feels free for

transmission because it can generate unaffordable cost, that is, intolerable

interference to PS or collisions at CR-Rx.

B. General Cooperative Spectrum Sensing

Spectrum sensing at cooperative node, which can be represented 1Co, is to

explore more information about 1Rx and therefore alleviates hidden terminal

problem. From above observation, we only care about correlation of 1Rx and

1Co when 1Tx=1 and assume

Pr (1Co=1∨1Rx=1 , 1¿¿Tx=1)=β¿Pr (1Co=0∨1Rx=0 ,1¿¿Tx=1)=γ ¿

Thus the correlation between 1Co and 1Rx , ρ, and corresponding properties

become

ρ= √α (α +1 ) ( β+γ−1 )

√ (αβ+ (1−α ) (1−γ ) )(α (1−β )+(1−α )γ )(7)

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Lemma 3: ρis a strictly concave function with respect to α∈(0,1) if

1<β+γ <2 but a strictly convex function if 0<β+γ<1. In addition, 1Co and

1Rx are independent if and only if ρ=0, i.e, β+γ=1.

By statistical inference, CR-Tx can learn statistical characteristic of 1Rx and

1Co, i.e., {α , β , γ }, by previous observations. From a viewpoint of hypothesis

testing, we would like to detect 1Rx with a priori probability α and one

observation 1Co, which is the detection result at the cooperative node.

Proposition 3: Spectrum sensing with one cooperative node becomes

1̂link={1Tx α ≥ max {α1 , α2 }1Tx1Co α2<α <α 1

1Tx1Co α1<α <α 2

0 otherwise

(8)

where 1Co is the complement of 1Co, α 1=wγ /(1−β+wγ) and

α 1=w (1−γ)/(β+w (1−γ )).

It is interesting to note that cooperative spectrum sensing is not always

helpful. In the following, we adopt minimum error probability criterion (i.e.,

w=1) and give an insight into the condition that cooperative sensing is helpful.

Applying Lemma 3, we can reach the following corollary.

Corollary 1: If we adopt minimum error probability criterion, cooperative

spectrum sensing becomes

1̂link=¿ (9)where 1[s ]

is an indicator function, which is equal to 1 if the statement s is true

else equal to 0, and

Ψ =| β+γ−1√2(βγ +(1−β)(1−γ ))|

Remark: The effectiveness of a cooperative node only depends on the

correlation of spectrum availability at CR-Rx and the cooperative node. If the

correlation is low, information provided by the cooperative node is irrelevant to

the spectrum sensing which degenerates to (5).

C. Link property in CRN

If we consider a path loss model between CR-TX and CR-Rx, transmission

region of CR-Tx should be a circularly shaped region without the existence of

PS. We call this region coverage of CR-Tx. However, due to hidden terminal

problem as in Fig. 1, where PS is either apart from CR-Tx or is blocked by

obstacles, the probability of collision at CR-Rx could increase and CR-Tx may

be prohibited from forwarding packets to CR-Rx. Therefore, CR-Tx may be

prohibited from using the link even when CR-Tx feels free for transmission.

Therefore, allowable transmission region of CR-Tx, defined as neighborhood

of CR-Tx, shrinks from its coverage and is no longer circular shape. In addition,

hidden terminal problem is location dependent, that is, PS is hidden to CR-Tx

but not to CR-Rx in Fig. 1. Thus, CR-Rx is possibly allowed forwarding packets

to CR-Tx. From such observations, links in CRN are generally asymmetric and

even unidirectional as the argument in [13]. Therefore, traditional feedback

mechanism such as acknowledgement and automatic repeat request (ARQ) in

data link layer may not be supported in general. This challenge can be alleviated

via cooperative schemes. Roles of a cooperative node in CR network operation

thus include

Extend neighborhood of CR-Tx to its coverage

Ensure bidirectional links in CRN (i.e. enhance probability to maintain

bidirectional)

Enable feedback mechanism for the purpose of upper layers

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Therefore, spectrum sensing capability mathematically determines CRN

topology. It also suggests the functionality of cooperative nodes in topology

control and network routing, which is critical in CRN due to asymmetric links

and heterogeneous network architecture [13].

We illustrate a numerical example in Fig. 2, where neighborhood of CR-Tx

("+" in the figure) with/without a cooperative node ("o" in the figure) is

depicted by a thick and a thin line respectively. In Fig. 2(a), PS ("*" in the

figure) is placed near to CR-Tx (0.7,0). We observe that CR-Tx almost perfectly

detects the state of PS and neighborhood of CR-Tx approaches to its coverage

and the cooperative node is not necessary in this case. However, when PS is

apart from CR-Tx (1.7,0) as in Fig. 1, the neighborhood at PS side shrinks and

is no longer circularly shaped because PS is hidden to CR-Tx and hence

probability of collision at CR-Rx increases. Fig. 2(b)~(d) illustrate the

neighborhood under different locations of the cooperative node. We observe

that neighborhood area decreases when the cooperative node moves away from

PS and there even exists a region where cooperative sensing can not help.

We present an example of existence of unidirectional link in CRN. In Fig.

2(b), assume one CR is located at (1,0). Obviously, the CR-Tx is not connective

to the CR and therefore is prohibited from forwarding packets to the CR.

However, by Fig. 2(a), the CR is connective to CR-Tx, which makes the link

unidirectional (only from the CR to CR-Tx). With the aid of a cooperative node

located at (0.4,0.3), the link returns to a bidirectional link. D. Network Level Tomography in CRN

Successful CRN operations generally relies on cooperative and opportunistic

relays through neighboring (CR) nodes, which always requires a prior

knowledge of such cooperative relay node-to-node availability to implement

routing and flow control [12], etc. Such node-to-node availability on top of the

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Without Cooperation With Cooperation CR-Tx Primary System Cooperative Node

(d)(c)

(a) (b)Neighborhood of CR-Tx, Without Obstacles,w=4

link availability among one-hop neighboring nodes relates to radio resource, CR

mechanism, and trust [13]. Due to the opportunistic transmission nature of

CRN, the guaranteed quality of service (QoS) control provides an intellectual

challenge. Provided the statistics of the node-to-node availability, the statistical

quality of service (QoS) control is a practical alternative way for end-to-end

services in CRN operations. To infer such a prior knowledge or estimation of

node-to-node availability associated with cooperative relay(s), we may observe

the history and statistics of successful packet transportation over a specific

cooperative relay path. Since there involves packet transmissions (either

implicit traffic packets or explicit probing packets) over multiple links, we

consider this challenge as active CRN tomography at network level.

Considering a scenario with a set of possible cooperative relay paths among

coexisting systems, the source node estimates the success probability of packet

transmission according to the historical record from the reception of destination

node. Both traffic types of the deterministic packet arrival and the Poisson

packet arrival from the source node will be studied.

In Figure 3, a source node nS transmits packets to a destination node nD

through K possible relay paths G j , j=1,… K . Let the successful packet

transmission probability of the routing path G j be PSj . Suppose that

PSj , j=1 ,... , K are selected beforehand independently from the uniform

distribution on the interval [0,1] and unchanged in thereafter packets

transmissions. Assume that the packets are slotted transmitted and received by

the source node and the destination node, respectively, with time interval ∆ t j in

one time slot for the relay path G j, and the propagation delay of G j, denoted as

D j, is constant. The destination node observes the packet reception in M time

slots (and thus M ∆ t j observation time for the relay paths G j) and feedbacks

this historical information to the source node in a reliable way (or the existence

of reliable observation with delay). As the destination node knows the relay

path(s) of the received packets, we may suppose the probabilities

PSj , j=1 ,... , K to be independently determined for all n.

1) Deterministic Packet Arrival: Within each time slot, the source node

transmits one packet by the routing path G j to the destination node (and thus

the packet rate is fixed in 1/∆ t j). We use an indicator function to represent the

transmission result of the i-th transmission by the relay path G j

1 j [ i ]={1, i -th transmission is successful0 , i -th transmission is failed

i=1 , …, M (10)

and 1 j [ i ] is thus Bernoulli-distributed with expected value PSj, i.e. with

probability PSj equal to 1 and probability (1−PS

j ) equal to 0. The a posteriori

probability density function of PSj , f (PS

j ), can be straightforward derived as

f (PSj )= ( M+1 )!

q j ! ( M−q j ) !( PS

j )q j (1−PSj )M−q j

(11)

where q j=∑i=1

M

1 j [i ].

Proposition 4: With mean-square error cost function, the Bayes estimator

becomes P̂S , MSj =(q j+1)/( M+2). On the other hand, with uniform cost

function, the Bayes estimator becomes P̂S ,UNFj =q j / M .

2) Poisson Packet Arrival: We now consider that the packets arrive on G j as a

Poisson process having rate λ j. Consequently, in each time slot, the probability

of no packet to be transmitted is PNj =e−λ j ∆t j. Assuming that λ j ∆ t j is small

enough, it results in negligible probability for more than one packet arrive

within one time slot. Hence we can only consider the probability that one packet

arrives as PYj ≅ 1−e− λ j ∆t j and no packet arrives as PN

j . We define an indicator

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Without Cooperation With Cooperation CR-Tx Primary System Cooperative Node

(d)(c)

(a) (b)Neighborhood of CR-Tx, Without Obstacles,w=4

function to represent the reception result of the i-th observation by G j at the

destination node

1 jR [ i ]={1 , s uccessful reception from j - t h relay pat h in i- th observation

0 , no reception from j - t h relay pat h in i- th observation

i=1 ,…, M(12)

Let s j=∑i=1

M

1 jR [i ].

Proposition 5:

If the propagation delay D j is known, which means the source node can

know whether no reception is due to no transmission from the source node or

transmission but failed, according to the historical observations of the

destination node, P̂S , MSj becomes (s j+1)/( M j+2) where M j is the number

of actual transmissions and P̂S ,UNFj =s j /M j.

Proposition 6:

If the propagation delay is unknown, which means the source node can

only know the statistics in certain M observations of the destination node,

P̂S , MSj becomes

P̂S , MSj =

s j+1M+2

1PY

j

I PYj (s j+2, M−s j+1)

I PYj (s j+1 , M−s j+1)

(13)

where PYj =1−e−λ j ∆ t j and I x (a , b) is the regularized incomplete beta

function, and

P̂S ,UNFj =s j /(M PY

j ) (14)

When PYj =1, (13) and (14) are degenerated to the results of Proposition 4.

Proposition 6 also suggests the estimators according to the traffic mode of the

source node with the parameter λ j of the Poisson process.

3) Applications: Propositions 4 and 5 offer simple estimators for the inference

of success probability P̂S=¿ in different traffic modes (deterministic and

Poisson packet arrival) of the source node, which are accomplished according to

the historical observations of receptions at the destination node. They can be

easily extended to many tomography cases for upper-layer CRN functions. For

example:

Corollary 2: (Opportunistic Routing) In the traditional reactive routing

protocols such as AODV or DSR, the source node spreads packets containing

routing information to get feedback from other nodes and so as to determine a

reliable routing path to the destination node. In CRN, the opportunistic routing

becomes a promising routing concept for the unreliably links with general none

zero packet-loss probability, in which all nodes involving in the route discovery

phase may be applied the proposed model to determine the best one of

neighboring nodes for data forwarding. Suppose that the source node has to

select one relay node from a set of $K$ candidate numbered neighboring relay

nodes to route packets. A straightforward selection with high reliability is to

select the k -th relay path where k=arg j max P̂Sj for j=1 to K .

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Source node nS

. . .

. . .

. . .

.

.

.

Path G1

Destination node nD

Path G2

Path GK

Fig 3. Cooperative opportunistic relay network.

3. Multi-channel MAC [黃柏堯]A. Multichannel MAC

Cognitive Radio (CR) has been considered as a promising technology to

enhance spectrum/channel efficiency while primary systems (PSs) are with

relatively low utilization. It is feasible to allow CR (i.e. unlicensed users) to

exploit the spectrum/channels when PS is idle. Theoretically speaking, the

overall spectrum utilization will be improved by the media access of CRs. From

CR’s point of view, the inherent characteristic of CR media access control

(MAC) is a multichannel environment where channels are preoccupied some

primary system with no precaution of CRs. The MAC under CR paradigm is

depicted as Fig. 4. For reliable CR media access, CR must properly select an

accessing channel, avoid interference with PS transmission, and resolve

contention between CRs. As a result, the multichannel MAC problem under CR

paradigm can further been divided into the two components 1) Channel

Selection and 2) Contention Avoidance/Resolution.

Fig. 4 Multichannel Media Access Problem under CR Paradigm

Collision avoidance/resolution [14] is inherited from the conventional MAC.

However, CR, categorized as secondary users, must perform spectrum sensing

before accessing the channel in avoidance of interference with PS. Moreover,

the potential CR competitor for channel access is no more static in consideration

of dynamic CR access. On the other hand, channel selection which considers

distributed selection of communication channel is now a new challenging issue

of multichannel MAC for CR which recently attracts most research efforts [22],

[9], [10]. The design goal of multichannel MAC aims at distributedly ignition of

parallel transmission over multichannel [10].

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Fig. 5 General multichannel MAC under CR paradigm and a CSMA-based

multichannel MAC for CRs

B. General MAC Protocol Framework

The multichannel MAC of CR is characterized by: {X ,Y , C , qx}. X is the number of channels and channel x is supposed to be independently occupied by some PS with probability qk. Let Y and denote the number of CRs and let C denote the average channel capacity. For multichannel under CR paradigm, we evaluate 1) the aggregated throughput T defined as the aggregated physical layer throughput over all channels, and the 2) average channel utilization U , defined as the average number of channels being successfully utilized for data transmission, normalized by the number of channel. Note that U is equivalent to traditional MAC ‘’throughput” defined as the

number of successful transmission per channel per slot.Consider a slotted and synchronous MAC with a perfect

physical layer, the generalized multichannel MAC problem is depicted in Fig. 5. Multiple CRs seek channel opportunity to access. After selecting channel at the beginning of a frame, the operation frame is further divided into three phases: 1) Spectrum sensing, 2) (CSMA) Contention 3) Data Transmission. The generalized channel selection algorithm is defined as Γ={ p1 , p2 , …, pX }={px }, where px is the probability of selecting channelx The contention avoidance/resolution consists of two parts: spectrum sensing for inter system (CR-PS) contention avoidance/ resolution, and intra system (CR-CR) contention avoidance/resolution mechanism. In this paper, we give and analyze a protocol with a slotted non-persistent CSMA.C. Performance Evaluation

We analyze the general multichannel MAC using a discrete time Markov chain (DTMC) model. CR is assumed to have single buffer and a traffic pattern follows a Poisson arrival with parameter λ. The retransmission policy is geometric with parameter qR. Let X t denotes the number of backlogged CRs at the beginning of a given frame t . Then {X t } forms a DTMC. The state space of X t is defined as:

S={0,1 , …,Y }. Let

QA(a , i) be the probability

that a un-backlogged CRs attempt to transmit packets in a given frame, and that QR(b , i)be the probability that b backlogged node attempt to retransmit. We have:

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QA (a , i )=(Y−ia )(1−qA )Y −i−a qA

a ;QR (b ,i )=( ib)(1−qR)

i−bqRb (15)

Where q A=1−e− λl is the probability of packet arrival for un-backlogged CR within a frame with duration l. The number of CR attempting channel access is therefore a+b with probabilityQA (a , i )QR (b , i ). Define a multichannel contention resolution/avoidance functionΩ(s , ω , x ) where its value represents the probability that there are s successful transmission CRs out of ω attempting CRs over channel 1, 2, …,x . For contention avoidance/resolution on a specific channel x,

define gx (n ) as the probability that the channel is successfully utilized by CR when there is n CR attempters. We have:

Ω (s ,ω, x )=∑n=0

ω

Ω (s , ω−n ,x−1 ) pxn[1−gx (n)]+Ω (s−1 ,ω−n , x−1 ) px

n gx (n)(16)

Note thatΩ (1 ,n , 0 )=g0(n) andΩ (0 , n , 0 )=1−g0(n); Ω(s ,ω , x ) is

recursively solved where pxis related to the channel selection algorithm Γ and

gx (n ), take non-persistent CSMA random back-off and with perfect spectrum

sensing as an example:

gx (n )=ϕx ∑i=0

N cw−1

(n1)( 1N cw

)(1− i+1N cw

)n−1

(17)

where N cw is the contention window size; ϕ xis the channel availability estimated by physical layer, note qx=1−ϕxwith perfect spectrum sensing. The transition probability of such Markov Chain can further be derived as:

Pij= ∑i+a−s= j

0 ≤ a≤ Y −i ;0 ≤ b ≤i ;s ≤ a+b

Ω (s , a+b , X )QA (a ,i ) QR (b , i)(18)

With the transition probability matrix P={Pij}, we can solve the limiting probability π={π i} of the Markov chain. With the limiting probability, the CR normalized (U ) /aggregated (T ) throughput can further be derived. Define the protocol efficiency η as the portion that a multichannel MAC protocol utilizes channel capacity in a frame. The throughput can be derived as:

U= 1X ∑

i=0

Y

∑s ,a , b

0 ≤a ≤ Y−i ;0 ≤ b ≤i ;s ≤ a+b

sΩ (s , a+b , X ) QA (a , i )QR(b , i) π i(19)

T=∑i=0

Y

∑s , a ,b

0≤ a ≤Y −i; 0≤ b≤i ; s≤ a+b

Cη∙ sΩ (s , a+b , X ) QA (a , i )QR(b , i) π i(20)

D. Simulation Results

With the general multichannel MAC framework, Fig. 6 illustrates the analytic and the simulation results of multichannel MAC for

CR. The simulation parameters are with X={10,20,30 }; pk=1X

;

N cw=5; qR=0.2; qx=0.05. The simulation results validate the Markov Chain based analytic model, providing a basic understanding and parameter dependence about the performance of multichannel MAC. With the parameter setting, the optimal normalized throughput of the multichannel MAC is about 0.7 in the presence of PSs. From the simulation results, with higher arrival rate λ, CRs approach to the maximum

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throughput provided by the multichannel faster, then, it saturates and drops quickly and vice versa. Further stabilization mechanisms are promising approaches to improve the multichannel MAC performance.

Fig. 6 Normalized Throughput U via attempt rate G with different number

of channels

4. Distributed Spectrum Sharing [林祐瑜]Since distributed spectrum sharing fully exploits the autonomic property of

CR nodes, the distributed scenario, which is classified by cooperative and non-

cooperative spectrum access, has received considerable research interests. In

cooperative distributed spectrum sharing, coalitions consist of several CR nodes

are formed in which spectrum sharing is performed via information exchange or

bargaining among the group [15]. On the other hand, in the non-cooperative

scheme, CRs make decision individually based on locally defined payoff

function and are thus considered selfish. When we consider a set of selfishly

behaved users embedded with cognition capability that results in the increment

of a priori knowledge and rationality, game theory provides a well-suited model

that describes the behavior of such intelligent nodes. Several spectrum sharing

mechanisms based on game theoretical view have been developed. In [2], power

allocation over shared bands is considered. A game theoretical version of water-

filling algorithm is proposed for both static and repeated games. An economic

view of spectrum sharing is modeled as a market competition process developed

in [5]. However, in most cases, only partial spectrum information is obtainable

due to limited sensing capability of CRs. The available channels are assumed to

be known for all CRs in [2][5], which is hard to achieve in practical operation.

In [7], a mixed strategy equilibrium that solely depends on the probability of

spectrum availability for spectrum access is derived. Nevertheless, since CRs

are able to fetch spectrum information in sensing period, the sensing result can

be regarded as side information in decision making of spectrum access, and is

not considered in spectrum access strategy [7].

In the distributed spectrum sharing scenario to be introduced, we practically

deal with the case in which only partial spectrum information is available for

individual CR and the spectrum sensing result as side information is utilized to

design the spectrum access strategy. This is a joint consideration of the

scenarios of previous efforts in [2][5][7]. The model also includes the individual

sensing capability of CRs by which CRs capture spectrum information in

absence of public spectrum information. Maximin criterion is applied to design

12/15

with respect to the worst channel access strategy of the opponents. We propose

algorithms to determine spectrum access strategy and demonstrate that the

algorithms prevent the system from collision in large network. Numerical

results are presented in comparison with random and proportional channel

selection and the proposed algorithms show superiority in system throughput

over the other strategies in both scenarios.

A. System Model

We consider a wireless network consisting of Primary Mobile Stations (PR-

MSs), Cognitive Radio Mobile Stations (CR-MSs), and Spectrum Agent (SA) :

PR-MSs is a set of licensed users each of which transmits on a fixed‧

licensed band.

Spectrum agent accumulates spectrum access requests from PR-MSs.‧

‧CR-MSs is a set of unlicensed users. A CR-MS is composed of a CR-MS

transmitter (CR-MS-Tx) and a CR-MS receiver (CR-MS-Rx).

PR-MSs and CR-MSs follow a perfectly synchronized time-slotted mechanism

defined by the superframe as shown Fig. 7. A superframe is defined in time

interval [ t d , td+1 ], where d∈Z+¿¿ is the discrete time index. The superframe

begins with the time slot in which SA gathers spectrum access requests from

PR-MSs for t req. The spectrum requisition period is followed by the PR-MS

data transmission slot lasting for t pac. At the time PR-MS transmission is

activated, CR-MS-Txs listen for the spectrum information via CR-MS-Tx

sensing. Spectrum sensing period takes tme t s. As the spectrum access strategy

is determined by the CR-MS-Tx, CR-MS-Rx listens for the preamble from the

CR-MS-Tx in which the decision on the channel to be used is carried. This way,

the agreement on the channel usage between CR-MS-Tx and CR-MS-Rx is

accomplished and the link between CR-MS pairs is established for data

transmission with period t sac .

Fig. 7. Superframe structure of slotted operation for spectrum accessThe wireless network is composed of X PR-MSs, each of which

corresponding to a licensed band, forming a set of numbered licensed channels

{1 … X }. A number of Y unlicensed CR-MSs coexist in the cell and are allowed

to access the channel based on their spectrum sensing result. Here we use y as

the CR-MS index and x as the channel index. The spectrum information

obtained by the y thCR-MS in [ t d , td+1 ] is denoted by the indicator function

Z y (d )={I y , x (d )}, where

I y , x (d )={ 1 if channel x is available0 if channel x is occupied

u y, x for unknown channel state

Due to limited sensing capability, the uncertainty of channel availability is

modeled as a Bernoulli random variable uy , xwith the sensing result for each

CR-MS pair is correct. A further assumption is that the belief of the traffic

loads of PR-MSs,which is associated with ϕ y , x , is the same among those CR-

MSs with uncertainty in the channel availability and we can therefore denote the

parameter of Bernoulli random variableuy , x, ϕ y , x, as ϕ x, which is independent

of the CR-MS index.

13/15

A CR-MS ends up in one of the three possible outcomes at the end of the

spectrum access phase: (i) the CR-MS successfully transmits bxbits via channel

xwhen it is the only user accessing the band (ii) collision occurs if either any

other CR-MS transmits on the same band simultaneously or the band is occupied by the licensed PR-MS, leading to a collision cost denoted by c ,

where c<0 (iii) the CR-MS does not transmit in the period, resulting in the

payoff 0. We assume additive white Gaussian noise (AWGN) time invariant

channel and the received power for CR-MSs are the same. Also, two CR-MSs

do not interfere with each other as long as they operate in different channels. In

this case the allowable transmission rate on a given band is constant regardless

which CR-MS uses it. Each CR-MS determines a set of strategy profile,

Sy={py ,0 , py ,1 ... , py , X }, indicating that the y thCR-MS accesses channel x

with probability py , x for x¿1 … X and does not access any channel with

probability py , 0. The decision is only applicable in a superframe and must be

re-determined based on the spectrum information derived in other superframes.

The expected payoff U y (d )over the strategy profile of the y thCR-MS in

[ t d , td+1 ] is

U y (d )= ∑x=1

I y,x ( d )=1

X

p y, x {c [1−∏j=1j ≠ y

Y

(1−p j , x )]+bx∏j=1j ≠ y

Y

(1−p j , x )}

+ ∑x=1

I y ,x ( d)=u y, x

X

p y , x{ϕx [c (1−∏j=1j ≠ y

Y

(1−p j , x ))+bx∏j=1j ≠ y

Y

(1−p j ,x )]+ (1−ϕ x ) c}

(21)

To highlight, the expected payoff depends on not only the strategy profile of the

y thCR-MS but also that of other CRs, indicating that for a CR-MS intending to

maximize its payoff, it should not be ignorant of other users’ strategy profiles.

Thus, we are motivated to use game theoretical model to deal with the

distributed spectrum access problem. In the following section, we derive

different approaches to find out the equilibrium solution for both public and

private spectrum information.

B. Spectrum Access Strategy

Since it is not always the case that there’s an SA broadcasting the

information to CR-MSs, individual CR-MS makes decision solely depending on

the private spectrum sensing information. In absence of the spectrum sensing

results of the other CR-MSs, a CR-MS is not able to find a global equilibrium

solution without the knowledge of its opponents’ channel access strategies. The

design philosophy for each CR turns out to be the maximin criterion, i.e. A CR-

MS determines the strategy profile such that

Sy= { py ,0¿ , py ,1

¿ ..., py , X¿ }=argmaxS y

¿ (22)

whereS− yis the strategy profile for all CR-MSs other than CR y .

We start by exploring the two-user and two-channel case. Let the spectrum

information obtained by CR1 be Z1 (d )={1,1}. Assume the strategy profile of

CR1 is S1= {0 , p1,1¿ , p1,2

¿ }. The maximin strategy is the strategy profile that

makes CR1 be indifferent to the strategy chosen by its opponents. That is, the

expected payoff of CR1 under maximin criterion shall be equalized regardless

of the strategy, access or wait, taken by CR2. The strategy named in equalizer

rule can be explicitly found by solving the set of equations,

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c p1,1+b2 p1,2=b1 p1,1+c p1,2 (23-

1)

p1,1+ p1,2=1 (23-2)

we have the maximin strategy profile for CR1:

S1={0 ,b2−c

b1+b2−2c,

b1−cb1+b2−2 c }

(24)

Note that the strategy profile in (24) is feasible if and only if it brings positive

payoff function. Otherwise, CR1 shall choose not to access any channel with probability 1. To generalize for a scenario with Y users and Xchannels, we

begin with the following lemmas:

Lemma 4: A CR-MS assigns probability 0 on those channel with

I y , x (d )=0∨(I y , x (d )=uy , x∧( bx+c ) ϕ x−c<0)

□

Lemma 4 is a procedure of eliminating dominated strategy: Accessing channels

occupied by PR-MSs and unknown channels bringing negative payoff even

when no other CR-MSs access the channel is dominated by not accessing any channel. Thus in the following, when referring to “X channels,” we excludes

those satisfying conditions in Lemma 4. Lemma 5: In the scenario with Y users and X channels, if the number of

channel assigned with positive probability X ≤ Y−1 for the CR-MS, the

maximin strategy for the CR is not to access any channel with probability 1.

Proof: It is obvious since the worst profile is that all the channels are

occupied by at least one CR-MS. □Inspired by Lemma 5, we can think of the other Y−1 CRs as a single

opponent-“environment.” The worst that the environment can harm the CR is to

occupy at most Y−1 bands, which we term as “worst case combination” in the

following. Therefore, any strategy of the environment that occupies less than

Y−1 bands is not of our concern in finding maximin strategy. The following

Lemma is another example:Lemma 6: In the scenario with Y users and X channels with X=Y . The

equalizer rule is maximin if the expected payoff is greater than 0. The maximin

strategy is

py , x¿ =¿

where ϕ jp=ϕ jif channel j is in unknown status and ϕ j

p=1if channel j is not

occupied.

Proof: For the equalizer rule { p1,1¿ ... , p1 , X

¿ }, the deviation version can be

written as { p y, 1¿ +ϵ 1 , ... , p y, X

¿ +ϵX } with ∑x=1

X

ϵ x=0. The strategy profile

achieves smaller payoff than the equalized one under the case that the channels

with ϵ x>0are occupied. The result in (25) is simply an extended version of (24)

.

□

The next Lemma shows the role of the equalizer rule in finding the maximin

strategy.Lemma 7: In the scenario withY users and X channels, a profile that does not

equalize the opponents’ pure strategy worst case combination cannot be a

maximin strategy.

15/15

Proof: Consider a small deviation from the equalizer rule, say

{ p y, 1¿ , …, py , j

¿ −ϵ , ... , py , i¿ +ϵ ,…, py , X

¿ }, where channel j is the one that the opponents access in the profile that brings least payoff, and the channel i is the

one that is not occupied by other CR-MSs. It is clear that the payoff is improved under the pure strategy profile. We can find ϵ >0 which is small enough such

that the order of the payoff for the pure strategy profile of the opponents

remains. Thus, the worst case pure strategy is improved. In that case, the

deviation from equalizer rule cannot be maximin strategy.

□

From Lemma 7, we see that an unequalized payoff can always be improved

via an equalizer rule. In the scenario described in Lemma 6, it is proved that the maximin strategy is uniquely determined by solving X equations (worst

combination of opponents) with X variables (strategy profile). However, in the

case with Y ≤ X−1, we have more equations than variables. The following

Lemma shows how we find the equalizer rule.Lemma 8: In the scenario withY users and X channels, to find the equalizer

rule that equalizes all channels, we formulate X equations, which are the

expected payoff under X different worst case combination of opponents with

any two of the combinations differ in only one channel assignments. For example, forX=4 andY=3, a user should equalize the environment’s strategy

that channel {1,2 } , {2,3 }, {3,4 } , {1,4 }are occupied.

Proof: It can be easily checked thatCY−1X −Xout of CY−1

X equations are

redundant. By eliminating the equivalent equations, we get only X equations as

described above. □In summary, to find out the maximin strategy profile in the scenario withY

users and X channels, a CR-MSs should refer to the equalizer rule;

nevertheless, it might be too pessimistic to equalize over all the X channels

especially when the cost of collision is low. Certain degree of persistency is

allowed in this case. Based on the above Lemmas, we propose the following

algorithm to find out the maximin strategy profile. Proposition 7: In the scenario withY users and X channels, whereY <X , we

find the maximin strategy profile following the procedures below:(i) Select x=Y channels with the largest benefits

(ii) Find equalizer strategy profile over the channels selected in (i), and assign

probability 0 on those not selected.

(iii) Calculate the equalized payoff in (ii)

(iv) Set x=Y +1 and repeat (i)-(iv) till x=X+1 The maximin strategy profile is determined by choosing the profile bringing the

largest equalized payoff calculated in (iii). If the largest payoff is smaller than

0, choose not to access with probability 1. □

C. Simulation Result

We assume that there are 10 unoccupied channels. The benefits for

successful transmission are: 9.17, 2.69, 7.65, 1.89, 2.88, 0.91, 5.76, 6.83, 5.46,

and 4.26. Also, we have one more channel with benefit 2.51 in unknown status

with available probability 0.3. Collision payoff is assumed to be -1. The total

benefit is defined as the summation of the channel benefit for

successful transmission plus the cost for collision. Simulation results are

averaged over 10000 times iteration. The result is compared with random

channel assignment and proportional mixed strategy assignment. For the

random assignment, CRs access each channel with equal probability; whereas for the proportional assignment, CRs access channel x with probability

16/15

bx /∑x=1

X

bx demonstrate the performance of maximin strategy in Proposition 7 as

shown in Fig. 8. It can be seen that the maximin strategy outperforms the other

two strategy profiles at the worst case channel assignment of the opponents at

any number of CR-MSs in the network due to the fact that the proposed

algorithm is optimal in the sense of maximin criterion. The individual worst

case benefit of the proposed algorithm is more than that of random access by

0.5 and than that of proportional assignment by 1. In large network, due to the

conservation property under maximin criterion, CR-MSs choose not to transmit

when the number of opponents is more than the number of channels bringing

positive benefit and thus keep the worst case of benefit at 0.

To conclude, when considering distributed spectrum sharing, the total

number of CRs accessing the channels is not limited in absence of a centralized

controller. In this case, despite the improvement in spectrum utilization,

frequent packet collisions among CRs result in degradation in system

throughput. We proposed distributed mechanisms to ensure system throughput

even in large network from the game theoretical view. A non-cooperative game

model is applied in describing the spectrum access competition among CRs. We

also pioneered utilize the most realistic spectrum sensing result in which

imperfectness due to limited sensing capability of CRs was taken into

consideration. The spectrum information with imperfection was used as side

information to aid individual CR in determining the optimal spectrum access

strategy. Numerical results showed that for the scenario with only private

spectrum information obtainable, the proposed algorithm designed under

maximin strategy profile assured the optimal benefit achievable under worst

case condition. CRs are prohibited from activating transmission under self-

enforcing mechanism, preventing the system from collision as well.

2 3 4 5 6 7 8 9 10 11-1

0

1

2

3

4

5

6

Number of CRs

Indi

vidu

al C

R B

enef

it (W

orst

Cas

e)

MaximinRandomProportional

Fig. 8. Individual CR-MS benefit under worst case

5. Routing and Control of CRN

A. Opportunistic Routing [林士鈞]

When facing the increasing demand o the spectrum, the concept of the CR

comes up for the underutilized spectrum. Dynamic spectrum access (DSA)

helps the CRs to fulfill the sufficient spectrum usage by using the spectrum hole

provided by the licensed PS. CRs can share the spectrum with PSs as long as

PS’s Qos is guaranteed, i.e. CRs is the link level technology requiring to sense

the spectrum of PSs being ”available”, then transmits packets to the receiving

17/15

node. In order to avoid the interference to the PS, the CRs share the medium in

an opportunistic way, i.e. establishing the opportunistic links between the CRs

and between the CR and the PS. Here comes quite a lot difference in such a

cognitive radio network with the wireless ad hoc (or sensor) networks.

However, operations of CRs shall not be limited to the link level. The

dynamically available of the CR links makes a new challenge when considering

the routing of the networking mechanism. Since each link is opportunistic

available for CRs, providing a workable and reliable route seems be a difficult

task. The concept of user cooperation which allows a source assisted by the

intermediate nodes explores the cooperative diversity and offers a significant

performance gain advantage. Although most existed routing protocols still think

the overhearing by the broadcast nature of the wireless environments which be

used for the cooperation purpose is the typical drawback that should be conquer,

the new coming opportunistic routing make cooperative diversity efficient and

practical on commodity hardware with the better throughput performance.

As being suitable for the routing of the wireless ad hoc (multi-hop) network,

Opportunistic routing considers the broadcast nature of the wireless

environment. When a node sends a packet through the air, all of the nodes in the

network may hear the packet. There is a link between each pair of the nodes in

the network. Opportunistic routing also considers the lossy nature of the

wireless environment. Due to the layer abstraction of data-link layer and

network layer, wireless networks suffer the unsuccessful transmission mainly

from the packet loss. Each link between two nodes owns a delivery probability,

which decreased with the distance increased. Since the delivery probability is

equal to one minus loss probability, i.e. the packet sends from the one side node

won’t be received by the other node with the probability in ergodic sense, each

attempt to transmit a packet can be considered as a Bernoulli trial. We formulate

the delivery ratio of the jth link in the nth time interval [tn,tn+1) is . With the

consideration of the above points, the wireless network can be modeled as all

pairs of the nodes are linked with a delivery probability assigned.

In order to avoid the interference to the PS, CRN links are available under

idle duration of PS that DSA can effectively fetch such opportunities, after

successful spectrum sensing. Link available period in CRN is in the range of

mil-seconds which gives CRN topology to be random even under all nodes

being static. We model the system according to the time slotted perspective. In

each time slot, PS would appear for the transmission with a probability. Once

the action is happened, it would hold until the end of the slot. It means if the

action taken by PS is not showing up, the whole slot is available for CR’s use

without being interrupted from PS’s traffic. An embedded continuous-time

Markov chain with the rates obtained from the statistics of spectrum

measurement is considered. The state transition diagram of the jth link in the nth

time interval [tn,tn+1) is shown in Fig. 10.

We formulate the available probability of the jth link in the nth time interval

[tn,tn+1) following a Bernoulli process as

(26)

18/15

Fig. 9. Network model of the wireless ad hoc network for opportunistic routing.

Fig. 10. State transition diagram of opportunistic link.

B. HARQ [歐永俊]

In [1][18][24], coded cooperation HARQ scheme is introduced. It integrates the

idea of cooperative communication and HARQ. Incremental redundancy is

generated through cooperative relay in order to exploit spatial diversity.

Bidirectional link is assumed in the above scheme.

In cognitive radio network, however, there exist a lot of unidirectional

links due to avoiding interference with primary users. Link level HARQ based

on feedback channel is frustrated. We now introduce the new idea of session

level HARQ. Error control is performed at session level (end-to-end) between

the source and the destination. We generate a coded packet from a message

packet at the source and divide the coded packet into many coded sub-packets.

Then, they are sent over different paths. Decoding is only performed at

destination by combining coded sub-packets that it has received. Link level

error control (acknowledgement between each link) is avoided. Each

intermediate node amplifies and forwards packets to next hop along its

predetermined routing path. A session level ACK (NACK) is only generated by

the destination provided that the origin message is (isn't) successfully recovered.

In the following subsections, link/path model and performance analysis are

provided.

(1). Link and path model

We assume that there exist K link-disjoint paths between the source and the

destination. Each path i, 1 ≤i ≤ K , has M i−1intermediate nodes. Each link

between a pair of node is modeled as an independent slow flat Rayleigh fading

channel. The received signal at node Ri , j is y i , j=hi , j xi , j−1+zi , j. Channel

gain hi , j is Rayleigh distributed. x i , j−1 is the signal generated from the

previous hop Ri , j−1. z i , j is independent zero-mean additive white Gaussian

noise with variance N 0/2. Each intermittent node amplifies and forwards the

received signal subject to the same signal power constraint Eb. So, the signal

generated by nodeRi , j will be x i , j=α i , j y i , j, α i , j2 =

Eb

hi , j2 Eb+N 0 /2

, αi , jis

the amplifying coefficient. The per hop received SNR is defined as γi , j=hi , j

2 Eb/ N 0. It is exponential distributed with mean γ i , j. The equivalent end-to-end SNR γeqi of path i is [19],

19/15

γe qi=[∏j=1

M i

(1+ 1γ i , j

)−1]−1

, 1≤i ≤ K (27)

The probability density function ofγeqi is difficult to evaluate and we use the bound γe qi

<γ bi=min (γi ,1 , γ i ,2 , …, γi , M i ). γbi is exponential distributed with mean

γbi=1

1γ i ,1

+ 1γ i ,2

+…+ 1γ i ,M i

, 1≤ i≤ K (28)

(2) Coding schemeWe assume the rate of the code is Rc. Message packet size is W bits. Coded packet size is B = W/Rc bits. We divide the coded packet into K coded sub-packets and they are transmitted over K different paths. The coded sub-packets are received, combined and decoded at the destination. The pairwise error probability between two codewords can be evaluated as

P (d|γb1, γ b2

,…,γ bK )=Q(√2d1 γ b1+2d2 γ b2

+…+2 dK γb K).

d is the

Hamming distance between the transmitted codeword and the codeword obtained after decoding. d i is the portion of Hamming distance contributed from the i’th part of the codeword transmitted through path i, 1 ≤i ≤ K , and d=d1+d2+…+dK . The unconditional pairwise error probability can be evaluated as

P (d )= 1π∫0

π2

(1+d1 γ b1

sin2 θ )−1

…(1+d K γ bK

sin2 θ )−1

d θ

≤ 12 ( 1

1+d1 γb1 )…( 11+d K γ bK )(29)

We can see that diversity gain of order K is achieved. In figure 11, the message packet is coded with convolutional code with rate 1/2 and the performance with different number of paths is shown.

Fig. 11. Packet error rate with K = 2; 4; 6; 8; 10 corresponds to different degree

of path diversity.

6. Cognitive Network Selection [鄭欣明]

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The concept of cooperative relay can be applied to heterogeneous radio access

technologies (RATs)/networks to generally network cognitive radios and nodes

in coexisting primary networks. Under this generalization, cognitive radio faces

a new challenge to select an appropriate radio access network among

heterogeneous RATs, to form cooperative relay. In other words, a cognitive

radio has to select an appropriate channel, among multiple access points of

multiple radio access networks, rather than opportunistic access a traditionally

specific link in a specific primary system.

To achieve the above goals, the network selection among various radio access

technologies/networks must provide service continuity and Quality of Service

(QoS) guarantees. The existing solutions [17][25] suggest mobile user to gain

knowledge about all heterogeneous RATs in the area of interest through the

negotiation to facilitate the selection. Specifically, mobile user should know the

available bandwidth of surrounding access points to prevent the connection after

selection being dropped due to resource deficiency. With distributed spectrum

sensing among multiple systems and cooperation, cognitive radios can detect

the availability of channels of different RATs and select the most appropriate

one according to the gathering information without negotiation between access

points and thus performance of selection is enhanced.

Fig.12 clearly represents a mobile-initiated cognitive network selection by

utilizing messages and protocols defined in 802.21[8]. In this figure, the

operation is divided into four phases: initiation [6], sensing and reasoning,

decision, and execution phases. In the initiation phase, the CR subscribes two

MIH events for the link parameters report on the serving access point (1). In

particular, CR configures threshold on the serving access point to report radio

measurement when specific thresholds are crossed (2). The type of this

measurement report may indicate an urgent request or just a periodic

informational message. Then CR may detect one of surrounding access points

through (3) and adds it into selection candidates.

Decision PhaseExecution Phase

Sensing and Reasoning Phase

Traditional UserMIH_MN_HO_Candidate_Query.request

CR

Initial Phase

Mobile User Serving Access Point

MIH MIHF Serving

2. MIH_Link_Configure_Thresholds

3. MIH_Link_Detected

4. MIH_Link_Parameters_Report

5. MIH_Link_Get_Parameters.request

5. MIH_Link_Get_Parameters.respond5. MIH_Link_Get_Parameters.respond

MIH_MN_HO_Candidate_Query.request

MIH_MN_HO_Candidate_Query.respondMIH_MN_HO_Candidate_Query.confirm

user RATNon-serving

RAT

Non-serving Access Point

1. MIH_Event_SubscribeConfirm

Confirm

Fig. 12. Message flow of cognitive network selection.

When CR receives the first link indication (4) reporting that the RSS of serving

access point is below thresholds setting in (2), it enters the sensing and

reasoning phase to obtain and estimate the system parameters through every

access point (5). On the other hand, legacy user only obtains the information by

querying serving and candidate access points, which introduces significant

delay. After CR periodically makes a network selection decision, CR updates

21/15

location information or establishes security associations with a target access

point in the execution phase.

Remarks: It is obvious that the proposed cognitive network selection in CR

outperforms the existing ones without spectrum sensing capability in terms of

signaling delay and overhead. As shown in Fig. 10, the explicit negotiations

between access point and user are omitted and thus signaling delay and

overhead can be minimized.

7. Conclusion

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