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 Open Access
Performance analysis of CSMA in an unslotted cognitive radio network with licensed channels and unlicensed channels
 Dong Bi Zhu^{1} and
 Bong Dae Choi^{2}Email author
https://doi.org/10.1186/16871499201212
© Zhu and Choi; licensee Springer. 2012
 Received: 22 January 2011
 Accepted: 13 January 2012
 Published: 13 January 2012
Abstract
We consider a carrier sense multiple access(CSMA) in an unslotted cognitive radio network under a mixed spectrum environment of licensed channels and unlicensed channels. In this system, primary users use only licensed channels, while secondary users use unlicensed channels and opportunistically can use licensed channels unused by primary users temporally. If an arriving secondary user(SU) does not find any idle channels, then the SU either enters the retrial group with probability q for later retrial or gives up its service and leaves the system with probability 1  q. SUs in the retrial group retry independently after exponentially distributed random time. We analyze the system by continuous time Markov chain(CTMC) with level dependent QBD structure and obtain the steady state probability of the system by matrix analytic method. In numerical results, we compare the performance of two systems with retrial(q > 0) and without retrial(q = 0). It is shown that the retrial phenomenon of SUs has an impact on the performance of SUs in cognitive radio networks.
Keywords
 Cognitive Radio Network
 CSMA; Retry SU
 Matrix Analytic Method
 Throughput
 Loss probability
1 Introduction
Advances in wireless communication systems and development of new services have significantly increased the demand for more frequency bands. Recently, the FCC has reported that the licensed bands are vastly underutilized [1]. To overcome spectrum scarcity, licensed spectrum bands need to be more intelligently utilized. For this purpose, cognitive radios have recently emerged as a promising technique to improve the utilization of the existing radio spectrum. Cognitive radio improves the spectrum efficiencies by enabling SUs to opportunistically access the channels unused by PUs.
There have been many studies on the opportunistic spectrum access for a cognitive radio network. Cognitive radio networks can be classified as single channel(e.g., [2, 4, 5, 7]) and multichannel(e.g., [3, 6, 8]). Both networks further can be classified a slotted structure(e.g., [2, 3, 6]) and an unslotted structure(e.g., [7–9]) where the slotted structure means the time axis for network is divided by time slots and all PUs and SUs are synchronized at the time slot.
Most of these studies deal with temporal use of the SUs on licensed bands. Multiple accesses of SUs on a mixed environment of licensed and unlicensed bands have been investigated [10–12]. H. AlMahdi et. al. [12] investigated CSMA in an unslotted cognitive radio networks under a mixed spectrum environment of licensed and unlicensed bands where the blocked SUs and the preempted SUs are forced to leave the system forever when there are no idle channels in the system. But in practical situation, the blocked SUs and the preempted SUs may do not leave the system forever and try to continue their services after random amount of time. This is a motivation of our work.
In this paper, we investigate the same model in [12] with an additional retrial phenomenon: the blocked SUs and the preempted SUs either enter a retrial group with probability q or leave the system with probability 1  q if all channels are busy. We investigate the effect of the retrial phenomenon of SUs on the system performance of an unslotted cognitive radio network. We analyze the system by CTMC with a level dependent QBD structure and obtain the steady state probability of the system using matrix analytic method. Note that, when q = 0, our model reduces to the system model in [12].
The rest of this paper is organized as follows. In Section 2, the operation of CSMA in unslotted cognitive radio networks with a mixed spectrum environment is described in details. In Section 3, we analyze the performance of the system by using matrix analytic method. Numerical examples are presented in section 4 and conclusions are given in section 5.
2 System model
On the arrival of a PU, if there are idle primary channels, the PU occupies one of those idle channels and send its message. If there is no idle primary channel but at least one primary channels are occupied by SUs, the arriving PU selects and preempts one of the primary channels occupied by SUs. If all primary channels are busy with PUs, the arriving PU is queued in the finite buffer with the size N  c_{1}(N ≥ c_{1}).
A SU always senses the channels before each message transmission. It is assumed that the sensing period of the channel is negligible. An arriving SU first senses unlicensed channels. If there are idle secondary channels, then the SU occupies one of the idle secondary channels and transmits a message and leaves the system after its transmission. When the arriving SU does not find idle secondary channels, the SU senses the primary channels and transmits its message on an idle primary channel if any, otherwise the SU either enters the retrial group with probability q for later retrial or give up its service and leaves the system with probability 1  q.
When a SU is preempted by a PU, the SU performs spectrum handoff to a secondary channel. Spectrum handoff procedures aim to help SUs find another idle channel to send its message. If the preempted SU finds idle secondary channels, the SU handoffs to one of those idle channels, otherwise the SU either enters the retrial group with probability q or leaves the system with probability 1  q.
SUs in the retrial group retry to the system in order to transmit their messages. The retrial time of SUs is defined as the amount of time between two consecutive retrials made by a SU and is assumed to be independent of all previous retrial times. We assume that the retrial times are exponentially distributed with mean ν^{1}. When a SU in the retrial group retries to the system, the SU repeats the same procedure as an arriving SU does.
3 Performance analysis of the system
In this section, we analyze CSMA in an unslotted cognitive radio network with a mixed spectrum environment of licensed and unlicensed channels by using matrix analytic method.
where c = c_{1} + c_{2}.
 i)A is the transition rate matrix from level k to level k+1, given by$A=\left[\begin{array}{cccccccccc}\hfill {E}_{c+1}\hfill & \hfill {A}_{0}\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill {E}_{c}\hfill & \hfill {A}_{1}\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill {E}_{{c}_{2}+2}\hfill & \hfill {A}_{{c}_{1}1}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill & \hfill {E}_{{c}_{2}+1}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill {E}_{{c}_{2}+1}\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill {E}_{{c}_{2}+1}\hfill \end{array}\right]$where E_{ n }= Diag(0,...,0, λ_{2}q) is an n × n diagonal matrix and A_{ n }, 0 ≤ n ≤ c_{1}  1 are (c  n + 1) × (c  n) matrices as follows.${A}_{n}=\left[\begin{array}{cccc}\hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \ddots \hfill & \hfill \vdots \hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill {\lambda}_{1q}\hfill \end{array}\right].$
 ii)k B_{2} is the transition rate matrices from lethe transition rate matrices from the transition rate matrices from el k to level k  1. B_{2} is given by${B}_{2}=Diag\phantom{\rule{0.3em}{0ex}}\left({B}_{0}^{\left(2\right)},{B}_{1}^{\left(2\right)},\cdots \phantom{\rule{0.3em}{0ex}},{B}_{N}^{\left(2\right)}\right)$where ${B}_{n}^{\left(2\right)}$, 0 ≤ n ≤ c_{1} are (c  n + 1) × (c  n + 1) matrices given by${B}_{n}^{\left(2\right)}=\left[\begin{array}{ccccc}\hfill 0\hfill & \hfill \nu \hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill \nu \hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill 0\hfill \\ \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill & \hfill \vdots \hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill \nu \hfill \\ \hfill 0\hfill & \hfill 0\hfill & \hfill 0\hfill & \hfill \cdots \phantom{\rule{0.3em}{0ex}}\hfill & \hfill \left(1q\right)\nu \hfill \end{array}\right]$
and ${B}_{{c}_{1}}^{\left(2\right)}={B}_{{c}_{1}+1}^{\left(2\right)}=\cdots ={B}_{N}^{\left(2\right)}.$.
 iii)B_{1}  k Γ is the transition rate matrices in level k. The matrix Γ is given by$\Gamma =Diag\left({\Gamma}_{0},{\Gamma}_{1},\cdots \phantom{\rule{0.3em}{0ex}},{\Gamma}_{N}\right)$(1)
Note that the generator Q is a level dependent. It is obvious that the system is stable if q is less than 1. Let Π be the steady state probability vector, which is the unique solution of linear equations ΠQ = 0, Πe_{∞} = 1; where e_{∞} is an infinite dimensional column vector whose elements are all equal to 1. We partition Π as Π = (Π_{0}, Π_{1}, Π_{2},...) where Π_{ k }= (Π_{k,0}, Π_{k,1},...,Π_{k,N}), ${\Pi}_{k,n}=\left({\pi}_{k,n,0,}{\pi}_{k,n,1},\cdots \phantom{\rule{0.3em}{0ex}},{\pi}_{k,n,{d}_{n}}\right)$ and π_{k,n,j}is the probability that Markov chain is in state (k,n,j) in the steady state. We can obtain the steady state probability vector Π by matrix analytic method(See an Appendix for detailed derivation).
The main performance measures of cognitive radio network are loss probability P_{ l }and the throughput T_{ s }of SUs.
Let ${\pi}_{.n,j}\triangleq {\sum}_{k=0}^{\infty}{\pi}_{k,n,j}$ is the probability that there are n PUs in the system and j SUs in service. Note that, since $n+{d}_{n}\ge c,{\sum}_{n=0}^{N}{\pi}_{.n,{d}_{n}}$ is the probability that all c channels are busy.
The loss probability P_{ l }of SUs is defined by the ratio of the loss rate of SUs to the arrival rate of SUs. Loss of SUs occurs by the following three kinds of events:

a new SU is blocked and then gives up its service and thus its loss rate is given by${\lambda}_{2}\left(1q\right)\sum _{n=0}^{N}{\pi}_{.n,{d}_{n}}$
where ${\sum}_{n=0}^{N}{\pi}_{.n,{d}_{n}}$ is the probability that all c channels are busy.

a retrial SU is blocked and then gives up its service and thus its loss rate is given by$\sum _{k=1}^{\infty}\sum _{n=0}^{N}k\mathcal{V}\left(1q\right){\pi}_{k,n,{d}_{n}}$
where kν is a retrial rate when there are k SUs in a retrial group.

a SU is preempted by a PU and then gives up its service and thus its loss rate is given by${\lambda}_{1}\left(1q\right)\sum _{n=0}^{{c}_{1}1}{\pi}_{.n,{d}_{n}}$
where a SU is preempted when all c channels are busy and at least one SU occupy primary channel.
4 Numerical results
In this section, we present numerical examples to investigate the performance evaluation of CSMA in unslotted cognitive radio networks with a mixed spectrum environment. We set the parameters c_{1} = 4, c_{2} = 3 channels, ${\mu}_{1}^{1}=2,\phantom{\rule{2.77695pt}{0ex}}{\mu}_{2}^{1}=2.5\left(sec\right)$.
5 Conclusions
We have considered CSMA in an unslotted cognitive radio network under a mixed spectrum environment of licensed and unlicensed channels and investigated the effect of retrial phenomenon of SUs. We analyze the system by CTMC with a level dependent QBD structure and obtain the steady state probability of the system using matrix analytic method, and then obtain performance measures of SU such as the loss probability of SUs and the throughput of SUs. Numerical results show that the retrial phenomenon of SUs have an impact on the loss probability and throughput of SUs in cognitive radio networks under a mixed spectrum environments of licensed and unlicensed channels.
Conflicting interests
The authors declare that they have no competing interests.
Appendix
The generator Q is the level dependent quasi birthanddeath matrix. Let Π be the steady state probability vector, which is the unique solution of linear equations ΠQ = 0, Πe_{∞} = 1 where e_{∞} is an infinite dimensional column vector whose elements are all equal to 1. To obtain the steady state probabilities, we use Neuts' approximation method (Neuts and Rao [13]). We choose appropriate K by this method and assume that only K calls among the retrial calls in the retrial group can retry for the service even if there are retrial calls greater than K in the current retrial group.
where A_{2} ≡ K B_{2}, A_{1} ≡ B_{1}KΓ.
Iterations will be continued until max_{i, j}[R(n + 1)]_{ ij } [R(n)]_{ ij } < ε is satisfied. [R(n)]_{ ij }is the i × j th elements of n th iteration R(n).
Declarations
Acknowledgements
This research is supported by the Korea Foundation for Advanced Studies' International Scholar Exchange Fellowship for the academic year of 20092010, and the MKE(Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency), Korea.
Authors’ Affiliations
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