In this paper, a general spectrum access scheme is introduced for multi-class secondary users operating in a hybrid interweave/underlay cognitive radio network. The hybrid channel access mode combines the benefits of interweave transmission (opportunistic access with high throughput) and that of the underlay transmission (anytime transmission with controlled power). Additionally, classifying the SUs helps to meet their different quality of service (QoS) requirements. The proposed scheme tackles three challenges: resolving the contention of the SUs to access the channel, scheduling an arbitrary number of SU classes, and providing a tunable spectrum resources allocation scheme. Specifically, each class of SUs is assigned a number of time slots for exclusive hybrid channel access according to their QoS requirements. The numerical results show the superiority and flexibility of the proposed scheme compared to other related work in literature.

In this paper, the hybrid interweave/underlay channel access mode is studied for an energy harvesting (EH) cognitive radio network with multi-class secondary users (SUs). The hybrid channel access mode combines the benefits of interweave transmission (opportunistic access with high throughput) and that of the underlay transmission (any time transmission with controlled power). EH upgrades the SUs' devices to be self sustainable. Additionally, classifying the SUs helps to meet their different quality of service (QoS) requirements. The system is modelled as a mixed observable Markov decision process (MOMDP) to handle the uncertainty in the primary user (PU) activity and consider future rewards. The MOMDP model is solved to maximize the SUs' throughput using two algorithms, namely, the point-based value iteration and the heuristic search value iteration (HSVI). Moreover, skipping the schedule of some SUs is proved to increase the channel utilization. The HSVI is proved to be efficient and reduces the time complexity significantly. Compared to related work in literature, the proposed model is proved to be superior in terms of throughput, tunable to meet the different QoS requirements of SU classes, and can accommodate any number of SU classes. Finally, the effect of some system parameters on the proposed system performance is studied and some insights are derived about the structure of the optimal policy and the system parameters values.

Cognitive radio (CR) is a technology that aims to enhance the use of the underutilized spectrum. Spectrum handoff plays this role by allowing the unlicensed secondary users (SUs) to use the licensed frequency bands of the primary users (PUs). Moreover, it helps the SU to vacate the channel at the presence of the PU and find a suitable channel for the interrupted SU to continue his unfinished transmission. In this paper, an analytical model for the general case of non-identical channels in CR networks is introduced for both fixed and probabilistic sequence approaches for target channel selection. Moreover, a new balancing model is suggested to generate the probabilistic sequence. Both the fixed and probabilistic sequence approaches are optimized using the meta-heuristic methods of particle swarm and genetic algorithm in the seek of finding an optimal approach to minimize the extended data delivery time of the SUs. The results are based on the pre-emptive resume priority M/M/1 queueing network model and are shown for different network cases. Based on the optimization results, a fast hybrid approach is proposed and found to achieve near optimal solution.

This paper elaborates on the suboptimal greedy algorithm for target channel sequence selection as presented by Wanget al.in the paper, “Optimal Target Channel Sequence Design for Multiple Spectrum Handoffs in Cognitive Radio Networks,”IEEE Transactions on Communications, vol. 60, no. 9, pp. 2444–2455, September 2012. They claimed that the greedy algorithm requires comparing six target channel sequences. We prove that only five target channel sequences comparisons are sufficient. Hence, we present a modified algorithm and test it with a set of numerical results. It appears that the new algorithm reduces the processing time compared to the original one especially for a network with a large number of candidate channels.