Cognitive radio can significantly improve the spectrum utilization by enabling secondary users (SUs) to opportunistically access the spectrum licensed to primary users (PUs). One practical yet challenging scenario is when both the PU occupancy and the channel fading change over time and exhibit temporal correlations. Little work has been done for simultaneously exploiting the temporal memory in both channel fading and PU occupancy for spectrum scheduling, and in particular, the scenario where PU occupancy presents a long temporal memory has been underexplored. In this work, we consider a cognitive radio network with multiple PUs and one SU, where a spectrum server is employed for scheduling the SU to transmit over one of the PU channels opportunistically. A primary goal is to understand the tradeoffs that arise from the intricate interactions between channel fading and PU occupancy, and the impact of the associated temporal memory. By casting the problem as a partially observable Markov decision process, we identify and illustrate a set of multi-tier tradeoffs that go beyond the classic "exploitation vs. exploration" tradeoff. We show that a simple greedy policy is optimal in some special cases. To build a deeper understanding of the tradeoffs, we further introduce a full-observation genie-aided system that helps in decomposing the tradeoffs in the original system into multiple layers, which we examine progressively. Numerical examples indicate that the optimal scheduler in the original system, with observation on the scheduled channel only, achieves a performance very close to the genie-aided system. In addition, the optimal policy in the original system significantly outperforms randomized scheduling, as well as a policy that explores memory in PU occupancy only, pointing to the advantages of jointly exploiting the temporal correlation structure in both channel fading and PU occupancy.

We consider a cognitive radio network with multiple primary users (PUs) and one secondary user (SU), where a spectrum server is utilized for scheduling the SU to transmit over one of the PU channels opportunistically. One practical yet challenging scenario is when both the PU occupancy and the channel fading vary over time and exhibit temporal correlations. Little work has been done for exploiting such temporal memory in the channel fading and the PU occupancy simultaneously for opportunistic spectrum scheduling. Further, the scenario where PU occupancy possesses a long temporal memory has been underexplored as well. By casting the problem as a partially observable Markov decision process, we aim to understand the intricate tradeoffs resulting from the interactions of the two sets of system states (i.e., channel fading and PU occupancy) and the impact of the associated temporal memory. We identify and illustrate a set of multi-tier tradeoffs that go beyond the classic “exploitation vs. exploration” tradeoff. For certain special cases, we establish the optimality of a simple greedy policy. To build a more comprehensive understanding, we then introduce a full-observation genie-aided system that helps in decomposing the tradeoffs in the original system into multiple layers, which we examine progressively. Numerical examples indicate that the optimal scheduler in the original system, with observation on the scheduled channel only, achieves a performance very close to the genie-aided system. In addition, the optimal policy in the original system significantly outperforms randomized scheduling, as well as a policy that explores memory in one system state only, pointing to the merit of jointly exploiting the temporal correlation structure in both channel fading and PU occupancy.

We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage, using random access, over available primary user (PU) channels. Our focus is on SUs' queueing delay performance, for which a systematic understanding is lacking. We take a fluid queue approximation approach to study the steady-state delay performance of SUs, for cases with a single PU channel and multiple PU channels. Using stochastic fluid models, we represent the queue dynamics as Poisson driven stochastic differential equations, and characterize the moments of the SUs' queue lengths accordingly. Since in practical systems, an SU would have no knowledge of other users' activities, its contention probability has to be set based on local information. With this observation, we develop adaptive algorithms to find the optimal contention probability that minimizes the mean queue lengths. Moreover, we study the impact of multiple channels and multiple interfaces on SUs' delay performance. As expected, the use of multiple channels and/or multiple interfaces leads to significant delay reduction. Finally, we consider packet generation control to meet the delay requirements for SUs, and develop randomized and queue-length-based control mechanisms accordingly.

We consider a cognitive radio network where primary users (PUs) employ network coding for data transmissions. We view network coding as a spectrum shaper, in the sense that it increases spectrum availability to secondary users (SUs) and offers more structure of spectrum holes, which in turn improves the predictability of the primary spectrum. With this spectrum shaping effect of network coding, each SU can carry out adaptive channel sensing by dynamically updating the list of the (predicted) idle PU channels and giving priority to these channels for spectrum sensing. This dynamic spectrum access approach with network coding improves how SUs detect and utilize spectrum holes over PU channels. Our results show that compared to the existing approaches based on retransmission, both PUs and SUs can achieve higher stable throughput, thanks to the spectrum shaping effect of network coding.

We consider a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage, using random access, over available primary user (PU) channels. Our focus is on SUs' queueing delay performance, for which a systematic understanding is lacking. We take a fluid queue approximation approach to study the steady-state delay performance of SUs, for cases with a single PU channel and multiple PU channels. Using stochastic fluid models, we represent the queue dynamics as Poisson driven stochastic differential equations, and characterize the moments of the SUs' queue lengths accordingly. Since in practical systems, a secondary user would have no knowledge of other users' activities, its contention probability has to be set based on local information. With this observation, we develop adaptive algorithms to find the optimal contention probability that minimizes the mean queue lengths. Moreover, we study the impact of multiple channels and multiple interfaces, on SUs' delay performance. As expected, the use of multiple channels and/or multiple interfaces leads to significant delay reduction.