Spectrum Handoff Strategy Using Cumulative Probability in Cognitive Radio Networks

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1 Spectrum Handoff Strategy Using Cumulative Probability in Cognitive Radio Networks Adisorn Lertsinsrubtavee, Naceur Malouch and Serge Fdida Laboratoire d Informatiqe de Paris 6 (LIP6) Université Pierre et Marie Curie (UPMC) Paris, France {Adisorn.Lertsinsrubtavee, Naceur.Malouch, Serge.Fdida}@lip6.fr Abstract The existing spectrum handoff techniques for cognitive radio perform handoff based solely on channel availability. This can result in a frequent number of channel handoffs and cannot ensure the quality requirement of applications such as delay bounds. This paper proposes the use of cumulative probability based on past backlog measurements to determine whether and how to perform handoffs. In order to prevent unnecessary handoff operation, secondary user should keep the same channel as long as the cumulative probability estimation does not violate some bound. However, the estimation from past observations may not correspond to the the real behavior in the immediate future even if efficient prediction models are employed. Thus, we propose the use of backup channels for short time periods to alleviate this problem. We analyze our strategies through extensive simulations and we compare them to random and classic approaches. Results show that our proposed strategies significantly reduce the number of channel handoffs up to 76% while still supporting the delay bound requirement. I. INTRODUCTION A spectrum handoff functionality in Cognitive Radio (CR) is slightly different from other wireless technologies such as Wi- Fi or GSM. Normally there, a handoff operation is requested when the signal of current cell is worse than the neighbor cells when users change their location. In CR networks, the secondary user (SU) has to decide to perform handoff when the primary user (PU) is detected in the licensed channel. As was emphasized in [1] and [2], many research works seek to perform channel handoff if the licensed channel becomes unavailable or PU is detected. The communication of SU has to be interrupted and SU packets must wait in the transmission buffer. The communication can be resumed when the connection is successfully switched to a new channel (spectrum handoff). Consequently, long waiting delays can be incurred and packets may be lost. The IEEE Wireless Regional Area Network (WRAN), switching delay is required to be lower than 2 seconds [3]. Here the switching delay includes times for channel selection process, spectrum sensing, signaling for channel establishment, and the RF reconfiguration. Also the FLO (Forward Link Only) air interface used in mobile broadcasting systems, for example MediaFLO developed by Qualcomm has an average physical layer channel switching up to 1.5 second [4]. This switching delay will be reduced depending on the hardware technology but it is still a significant factor to influence the global performance. Nevertheless, SU might perform unnecessary handoff because sometimes PU occupies the channel only for a short periods intermittently. The communication can be continued again on the same channel if SU waits until PU finishes its transmission. In addition, this waiting time should not impact the global performance of the communication regarding the application requirements, e.g. delay, throughput or loss ratio. The study [5] has compared the the performance where SU always decides to wait until PU finishes its transmission, and where SU always does handoff whenever PU is detected based on PRP M/G/1 queueing model. However, SU may suffer from long waiting time delay, if PU occupies the channel for a long period. The delay bandwidth product is proposed in [6] to help SU decide to wait until PU finishes its transmission or if it is better to perform handoff. A channel selection based on low occupancy by primary users and handoff based on prediction models was proposed in various research works [7] and [8]. Conversely, the prediction error cannot be avoided especially for non stationary traffic. SU may selects a bad quality channel leading to fast successive handoffs and degrading the network performance. The study [9] has conducted a large scale measurement and found that the prediction error of PU activity can be approximated by Beta distribution. A dynamic channel selection framework is proposed for realtime application based on utilizing a channel capacity and data loss rate [1]. To reduce the spectrum handoff delay, the out-ofband channels are classified as backup and candidate channels as introduced in IEEE [11]. This classification helps the channel selection process to facilitate fast discovery of the best channel to use when PU is detected on the operating channel, but no algorithm is specified on how to build the list of backup channels. In this paper, we consider a compromise decision to stay on the same channel or to handoff to another channel while supporting a delay bound requirement from the application. This implies the need of observations from the past and most importantly, it requires that the quality of current channel can actually be estimated. In addition, selecting a bad quality channel causes frequent channel handoffs. Therefore, the selection of the new target channel should also take into account similar quality estimations. To achieve these goals, we propose new strategies for the spectrum handoff procedure in cognitive radio networks

2 which make the following contributions. First, a cumulative probability (CP rob) is proposed to estimate channel adequacy for the current transmission and to make handoff decisions. Second, a complete spectrum handoff algorithm based on cumulative probabilities () is designed. It uses a queue in the MAC layer and another in the radio layer. Third, a backup channel for a short time period is applied to our strategy in order to avoid fast successive handoffs even if primary behavior can not be predicted. Finally, we evaluate our method compared to an opportunistic random handoff approach and the low occupancy selection approach. The following sections of this paper are organized as follows. Section II presents the system model. Section III presents the new proposed spectrum handoff strategies using CP rob and backup channels. The performance of the proposed strategies are evaluated in Section IV through simulations, and then the paper concludes in Section V. II. SYSTEM MODEL We consider a wireless device of SU equipped with a cognitive radio that has access to n licensed channels. SU can use any one of the channels at a time if the channel is declared available by the sensing module of the cognitive radio. Each channel has its own independent availability pattern. Collected information about channel availability through spectrum sensing are gathered and analyzed by a queue manager at the MAC layer. Packets from the application are buffered in the MAC queue. If the channel is usable, the MAC queue manager transfers packets to the Physical layer interface (Fig. 1). In this work, we consider spectrum handoff as deployed by a cognitive radio node to focus on the internal algorithm and the channel quality estimation. The Physical interface sends packets to the base station of the cognitive radio network only if the current active channel is available. Virtual Q_ch 1 Secondary User Fig. 1. MAC Q_manager Physical interfaces Ch 1 Current Active Channel Ch 2 Ch n Node architecture for spectrum handoff CR Base Station To estimate the quality of every channel, SU creates a virtual queue for each channel (Fig. 1). The MAC queue manager then forwards packets from the application layer to all virtual queues. Each virtual queue can synchronize information about channel availability from the spectrum sensing module. Then it updates the channel availability pattern to be the same as a real physical channel. If this channel is declared available, packets will be removed from the virtual queue. Section III.A explains why virtual queues are deployed. III. SPECTRUM HANDOFF STRATEGIES USING CUMULATIVE PROBABILITIES Even if the objective of the application is to satisfy a given delay constraint, it is not practical to perform spectrum handoff based on delay measurements. First, these measurements usually require additional feedback messages from the receiver or from the next hop to compute the end-to-end delay or the one-hop delay. Second, the delay of a packet is measured only after the packet is received which means after it is removed from the MAC queue, thus this value can be obtained too late especially if the packet enters a full queue or when the current channel becomes unavailable. In the latter case, the delay measurement will be obtained only when the channel becomes available again. Consequently, SU cannot update the current state of the channel promptly and may wait for the report message thus exceeding the delay constraint. On the other hand, SU can estimate a backlog delay instantaneously once a packet arrives at the MAC queue based on remaining packets in the queue. Therefore, the number of packets in the queue is able to describe the current state of the channel more efficiently than a delay measurement. A. The Cumulative Probability (CP rob) The cumulative probability CP rob can ensure that a packet is serviced through the channel within a backlog delay bound D max and with a maximum probability of delay violation P max. For instance, if D max is 5 ms and P max is.2, it means that no more than 2 % of sending packets should experience a backlog delay larger than 5 ms. The parameters D max and P max depend on the requirements of the application. More precisely, the D max and P max of a delay sensitive application should be lower than the file transfer application. Apart from the delay requirement, the cumulative probability is able to reflect both the state of channels and the traffic sent by the application. Indeed, if packets are sent only when the channel is available then this channel is considered to be usable even if unavailable periods are long. Also, the cumulative probability can decrease the number of unnecessary handoffs due to intermittent channel availability since its computation naturally combines several queue size measurements. Now, to compute CP rob at he MAC queue and the virtual queues, SU estimates first the backlog delay each time a packet p arrives at the queue (Fig. 2). As discussed above, this delay is calculated based on the current queue size at the arrival time by the following expression = size(p) bw i (1) where size(p) is the packet size and bw i is the transmission bandwidth offered by the channel. If is greater than D max, a counter Nmax, w that maintains the number of times the estimated backlog delay is larger than the delay bound, is incremented. The queue manager computes Nmax w over a sliding window of previous backlog delay measurements including the current one. Then, it calculates the ratio CP rob

3 1 2 3 Observation Window (size = ) Calculate Arrival Packet Fig. 2. < D max > D max MAC Q_manager Ch 1 Current Active Channel Ch 2 Ch n Observation Window (size = ) Calculate Forwarded Packet from MAC queue Virtual Q_ch Observation Window (size = ) Calculate Forwarded Packet from MAC queue Virtual Q_ch 2 Observation Window (size = ) Calculate Forwarded Packet from MAC queue Virtual Q_ch n sensing sensing sensing Computing CP rob at the MAC Q-manager and the Virtual queues between max and the total number of packet arrivals in the observation window. Ch 1 Ch 2 Ch n CP rob = max (2) The size of the observation window has a substantial impact on the accuracy of estimating the quality of channels. For example, a large observation window size may not keep the CP rob up-to-date because the traffic from PU and/or SU may change rapidly. In contrast, a short observation window size cannot give enough information to estimate the channel behavior. However, we investigate this impact through simulation in section IV.B. Once a packet arrives at the MAC layer, the MAC queue manager forwards the current packet to the virtual queue for each channel. Then, CP rob is estimated both in the MAC queue and the virtual queue.the MAC queue manager has to estimate the CP rob of the current queue. Besides, CP rob is computed for all the other channels using, on one side, the previous channel status provided by the sensing module for every channel, and on the other side, the arrival instants of packets sent by the application (Fig. 2). This is computed in order to emulate a transmission from SU over each channel and thus compute what CP rob would be if the on-going transmission is sent over another channel instead of the current one. This is possible through the deployment of virtual queues. Let us remember, CP rob is computed at the MAC queue manager in order to decide when SU should do handoff. If CP rob is greater than P max, it means the current active channel cannot provide the required service quality. On the other hand the CP rob of virtual queue is computed to estimate the quality of each channel. The MAC queue manager selects a channel which has the lowest CP rob based on the estimation of the virtual queues and decide to perform handoff according to the procedure described in the next section. B. : Spectrum Handoff based on Cumulative Probabilities One of the objectives of SU is to reduce the number of channel handoffs. To do so, SU should try to keep the same channel if it estimates that the delay bound will not be violated or if other channels can not offer better delays anyway. When handoff is necessary, then the new selected channel should be chosen in order to prevent as much as possible additional future handoffs. Accordingly, SU decides to perform handoff to channel i if the current channel is not available or bw i > bw current, and CP rob current > P max,i.e.,the quality of the current channel is not good enough, and CP rob current CP rob i,i.e., The quality of the target channel is better than the current one and thus handoff is worthwhile. The parameter is a threshold of quality improvement that should be tuned according to the tolerance allowed by the application. Notice that when the channel is available and the new bandwidth does not increase then clearly there is no need for handoff. The target channel i is the one that has the minimum CP rob i among all available channels. C. -BC: Using Short Time Backup Channels Selecting an inappropriate channel after the procedure can still occur because the estimation from the observation window may not provide the same behavior in the immediate future. Moreover, even if sophisticated prediction techniques are used, PU behavior can change at any time unexpectedly. In that case, SU may have to repeat the handoff process. Thus, using a backup channel can alleviate this problem by allowing SU to tune to another channel in addition to the newly selected one when performing the handoff. The best channel according to is set to be the active channel and the second best channel is chosen to be the backup channel. This procedure avoids performing quick successive handoffs. The backup channel can also be used during the backup time to transmit packets. However, at the end of the backup time, SU has to release one of the two channels by the following two rules. The active channel is unavailable and the backup channel is available, SU releases the current active channel and sets the backup channel to be active. In all other cases, SU releases the backup channel. The larger the backup time, the lower the number of handoffs, however, the backup channel can not be used by other users during that time. That is why the backup time should be short. Its impact on the global performance is also examined in the next section. We call this variant of the handoff algorithm -BC. IV. SIMULATION RESULTS We use OMNeT++ simulator [12] to evaluate the performance of our strategies and compare to the opportunistic

4 handoff with random selection approach and also to the classic low occupancy selection approach. The opportunistic and random approach performs handoff immediately once the current channel becomes unavailable. Then, it selects a new available channel randomly. For the low occupancy selection, SU selects a new available channel which offers the longest available time, or in other words the lowest channel occupancy by primary users. However, we further improve this classic approach by adding a waiting time before handoff so the comparison to our strategy is not biased. Hence, whenever the current channel becomes unavailable, we wait for a time W H before performing the handoff. At the end of the W H period, if the channel is available then the handoff is not done and the current channel is kept. This technique can reduce unnecessary handoffs if the W H is well chosen. We simulate a cognitive radio node with a MAC queue and a physical queue before transmission. In order to compute cumulative probabilities CP rob for all channels, we also simulate a virtual physical queue for every channel. To focus on the handoff algorithm and its performance, we simulate a single user between the cognitive radio node and a base station. A. Simulation Parameters and Performance Metrics To simulate primary user activity, we consider channels that are switching between available and unavailable periods with durations exponentially distributed. The means of these durations denoted by µ available and µ unavailable are chosen and combined among the set {1, 1}seconds in order to create 9 channels with different properties (Table I). For the cognitive radio traffic, we follow a Voice over IP model that alternates between idle periods and sending periods with a constant rate. All the values of simulation parameters are mentioned in Table I. Furthermore, every simulation is repeated 2 times and averages along with 95% confident intervals computed with the t-distribution are shown in all plots. To evaluate the performance of the different handoff strategies, we examine the number of channel handoffs and the observed delay violation ratio P v which is the ratio of the number of packets which have backlog delay over D max to the total number of packets. This ratio should be lower than P max as required by the application. In the following sections, we study the impact of the configuration parameters on the different strategies while showing the efficiency of and -BC compared to other strategies. B. Impact of System Parameters In this section, we investigate the impact of system parameters and show through simulation the optimal value of the window size, the threshold of quality improvement and the backup time. Impact of window size. We investigate first what would be the adequate size of the observation window to be used in computing the cumulative probability CP rob. Since CP rob is computed both at the MAC queue and for the virtual queues, Parameter TABLE I SIMULATION CONFIGURATION Value Channel bandwidth 32 kbps Propagation delay to the base station 5 ms Handoff delay 1 ms Channel 1: µ available, µ unavailable 1, 1 in seconds Channel 2: µ available, µ unavailable 1, 2 in seconds Channel 3: µ available, µ unavailable 1, 5 in seconds Channel 4: µ available, µ unavailable 2, 1 in seconds Channel 5: µ available, µ unavailable 2, 2 in seconds Channel 6: µ available, µ unavailable 2, 5 in seconds Channel 7: µ available, µ unavailable 5, 2 in seconds Channel 8: µ available, µ unavailable 5, 5 in seconds Channel 9: µ available, µ unavailable 5, 1 in seconds 5.5 s SU mean idle period µ idle 2.5 s SU transmission rate 64 kbps Packet size bytes P max % D max 4 ms we considered a suitable window size for each. Let us begin with the window size of the MAC queue. As the current active channel maintained by the MAC queue is dynamic, the active channel can be changed in a short period intermittently. For instance, SU switches from channel 1 to channel 2, according to whether the CP rob is over the threshold (P max ). After switching, MAC queue may decide to do short successive handoffs, even though channel 2 is the best channel. The fact of the matter is that a small observation windows is not sufficient to estimate the current behavior of the dynamic channel at the MAC queue. When the current active is moved to channel 2, the statistical data in the observation window reflects the performance using channel 1 (Fig. 3). Thus, the CP rob of MAC queue is still over P max. For this reason, a large window size is more suitable for the MAC queue than a small one. This large window size should contain enough information about the behavior of channel usage from the past. We call the window which records the delay of each packet from the beginning of transmission to W holedb. < D max t -1 t CProb>P max > D max Channel usage Fig. 3. Observation Window of MAC Q_manager CProb still over P max Impact of short observation window at the MAC queue We compare the performance between applying the

5 W holedb and a small observation window (a window size of 1 measurement) at the MAC queue through the simulation while varying the source traffic. Fig. 4(a) shows that applying the W holedb at the MAC queue can reduce the number of channel handoffs significantly compared to the small observation window. Fig. 4(b) illustrates the delay violation ratio where P max constraint is set to %. Apparently, the W holedb can satisfy to the required performance and the delay violation P v is almost equal to P max. However, the small observation window increases the number of handoff substantially while decreases unnecessary P v. Hence, the W holedb is suitable to apply for a window size of the MAC queue WholeDB Short observation window (a) Number of channel Handoffs Fig WholeDB Short observation window Impact of the observation window size at the MAC queue Nevertheless, a window size of the virtual queue should be different from the MAC queue because a virtual queue has the responsibility of estimating the CP rob for only one specific channel. Thus, it is more important to keep the CP rob up-todate by using a small window size. Fig. 5 show the impact of varying the window size at the virtual queue Size of observation window (a) Number of channel Handoffs Fig Size of observation window Impact of the observation window size at the Virtual queue The delay violation ratio P v increases significantly when the window size is over 2. This shows that a large window size is not suitable for a virtual queue. Even though a larger window size can reduce the number of channel handoffs, P v is increased over P max limit which is not acceptable. A small window size between 5 to 2 can achieve the required performance (%) perfectly and P v does not exceed the P max. However, the number of channel handoffs slightly falls when the window size increases. Thus, a window size of 1 measurements (packets) is a good tradeoff between both performance metrics. Impact of the threshold of quality improvement. Fig. 6 illustrates the impact of the threshold of quality improvement used to assess the validity of the handoff decision (Section III.B). These results suggest that. (%) could be a compromise value of because the P v and the number of handoffs do not vary much after this value. A very small value is not adequate because it decreases P v unnecessarily while increasing significantly the number of handoffs. Otherwise, using is efficient in reducing the number of handoffs without impacting the required delay performance Threshold of quality improvement (a) Number of channel handoffs Fig Threshold of quality improvement Impact of the threshold of quality improvement Impact of varying the backup time. Fig. 7 shows the impact of varying the backup time used in -BC. Although the number of handoffs declines steadily when the backup time is increased, the longer the backup time, the less chance for other SUs to find channels to select. Hence, the backup time should be set in order to reduce the P v accordingly. After Fig. 7(b), we set this value to 1.5s for the next simulations Backup Time (s) (a) Number of channel handoffs Fig Backup Time (s) Impact of backup time used in -BC C. Comparison of Different Strategies In this section the performance of and -BC is studied and compared to the random and the low occupancy strategies while applying the values of system parameters from the previous section, i.e. window size = 1, =. and backup time = 1.5s. In this first set of simulations, we vary the load by varying the source traffic. The mean sending period is varied from 1 to 5.5s while keeping the mean idle period µ idle fixed at 2.5s. Fig. 8 illustrates the number of channel handoffs for each strategy, and Fig. 9 illustrates the P v achieved. and -BC strategies are able to reduce the number of channel handoffs significantly compared to the opportunistic random approach by around 66% and 76% respectively while the delay violation ratio P v is lower than

6 the limit (%). Similarly, the low occupancy approach also can reduce the number of channel handoffs moderately, but it is difficult to tune the waiting time before handoff W H to meet the P max and D max requirement. Here, W H =.4s provides the performance near the required P max (%), but the number of handoffs is still higher than and - BC by around 29% and 5% respectively. Backup CH to be Active(%) Random Selection Low Occupancy(WH=) Low Occupancy(WH=.4) Fig. 8. Number of channel handoffs. increases from 1 to Random Selection Low Occupancy(WH=) Low Occupancy(WH=.4) Fig. 9. Delay violation ratio. increases from 1 to 5.5 Importance of backup channel. From all the previous figures, it appears that -BC reduces the delay violation ratio and the number of channel handoffs. Let us remember that at the end of backup time, SU has to release one channel either the active channel or the backup channel. We have found that around in 2% of the cases (Fig. 1), SU releases the active channel and sets the backup channel to be active. This is due to sudden changes in PU behavior that makes the channel unavailable. The backup channel is then useful to avoid interrupting the communication by a second quick handoff as in. Fig. 11 and 12 show the cumulative distribution function (CDF) of the end-to-end delay of packets at the receiver for Fig. 1. Impact of backup channel: Backup channel is set to be actvie different values of traffic load ( ). We observe for instance that when 81% of packets with delay less ms is achieved for and 82% for -BC. These high percentages are observed for all values of the CDF and for all traffic loads. Cumulative Distribution Function Cumulative Distribution Function CDF of E2E Delay ().4 = 3.5s = 4.5s.2 = 5.5s End to End Delay (s) Fig. 11. CDF of End to End Delay () CDF of E2E Delay () = 1.5s = 2.5s End to End Delay (s) Fig. 12. = 1.5s = 2.5s = 3.5s = 4.5s = 5.5s CDF of End to End Delay (-BC) Varying the delay violation requirement. Fig. 13 shows the delay violation ratio when the P max constraint is varied. Interestingly, the and/or -BC can achieve the required performance and the delay violation ratio P v does not exceed the P max except when the latter is less than 1%. When P max = 5%, then P v = 8.9% for SCHP and 6.5% for -BC which are still small violations. As we have shown above, only the opportunistic handoff strategy is able to achieve the lowest P v but at the cost of a huge number of handoffs.

7 Maximum probability of delay violation (%) Fig. 13. Performance results of delay violation ratio while varying the maximum allowed probability of delay violation Impact on primary users. Considering again the case µ on = 5.5. The interference to PUs is examined and illustrated in Fig. 14. The impact on PUs is captured through the interference rate which is computed based on the number of times when SU transmits packets during unavailable periods of channels. We have found that all approaches, including and -BC, reduce the interference rate by around 35% compared to random handoff. This is because the random selection chooses more often channels that are in the end of an availability period, and thus packets in the Physical queue are transmitted while the channel becomes unavailable. Interference Rate (hits/second) Random Selection Low Occupancy(WH=) Low Occupancy(WH=.4) Fig. 14. Impact on primary users: Interference rate V. CONCLUSION AND FUTURE WORKS Using cumulative probabilities improves the performance of cognitive radio devices by reducing the number of channel handoffs while maintaining an application requirement. In this work, we have designed, a spectrum handoff strategy based on computing cumulative probabilities of backlog delays experienced over candidate channels. Besides, the handoff is performed only if the quality of the target channel is worthwhile. For further enhancement, short time backup channels are used in parallel with the new channel which avoids fast successive handoffs. Through simulations, we have provided insights on how to choose the parameters of in order to meet the target probability violation delay. Moreover, comparison with random handoff and low occupancy handoff shows the benefit of in term of reduction of the number of handoffs. Future works include mainly the investigation of other methods to compute the cumulative probability instead of deploying virtual queues. Also, it would be interesting if is extended in order to support variable but not measurable channel bandwidth. This will allow multiple secondary users in the network to be supported without the need of a control channel to exchange information about the available bandwidth. Finally, when multiple channels can be used in parallel by the same cognitive radio, we should investigate a practical way to combine their qualities. REFERENCES [1] I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury, CRAHNs: Cognitive radio ad hoc networks, Ad Hoc Networks, vol. 7, no. 5, pp , 29. [2] I. F. Akylidiz, W. Y. Lee, M. C. Vuran, and S. Mohanty, Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey, Computer Networks, vol. 5, no. 13, pp , 26. [3] IEEE P82.22/D.3.8.1, IEEE WG, Draft Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Policies and Procedures for Operation in the TV Bands, IEEE, 27. [4] M. R. Chari, F. Ling, A. Mantravadi, R. Krishnamoorthi, R. Vijayan, G. K. Walker, and R. Chandhok, Flo physical layer: An overview, IEEE Transactions on Broadcasting, vol. 53, pp. 145, February 27. [5] L.-C. Wang and C.-W. Wang, Spectrum handoff for cognitive radio networks:reactive-sensing or proactive-sensing, in Proc.IEEE Performance Computing and Communications Conference, 28. [6] S. Talat and L.-C. Wang, Qos-guaranteed channel selection scheme for cognitive radio networks variable channel bandwidths, in Proc. IEEE Communications,Circuits and Systems, 29. [7] M. Hoyhtya, S. Pollin, and A. Mammela, Classification-based predictive channel selection for cognitive radios, in Proc. IEEE International Conference on Communications, 21. [8] L. Yang, L. Cao, and H. Zheng, Proactive channel access in dynamic spectrum networks, Physical Communication, vol. 1, no. 2, pp , 28. [9] C. Song and Q. Zhang, Intelligent dynamic spectrum access assisted by channel usage prediction, in Proc. IEEE international conference on Computer Communications Workshops, 21. [1] W.-Y. Lee and I. F. Akyldiz, A spectrum decision framework for cognitive radio networks, IEEE Transactions on Mobile Computing, vol. 1, pp , February 211. [11] G. Ko, A. A. Franklin, S.-J. You, J.-S. Pak, M.-S. Song, and C.- J. Kim, Channel management in ieee wran systems, IEEE Communication Magazine, vol. 48, pp , September 21. [12] Omnet++: A discrete event simulation environment for communication networks, Available from Since 1993.