Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks

Abstract

In cognitive radio networks (CRNs), hybrid overlay and underlay sharing transmission mode is an effective technique to improve the efficiency of radio spectrum. Unlike existing works in literatures where only one secondary user (SU) uses both overlay and underlay mode, the different transmission modes should dynamically be allocated to different SUs according to their different quality of services (QoS) to achieve the maximal efficiency of radio spectrum. However, dynamic sharing mode allocation for heterogeneous services is still a great challenge in CNRs. In this paper, we propose a new resource allocation method based on dynamic allocation hybrid sharing transmission mode of overlay and underlay (DyHySOU) to obtain extra spectrum resource for SUs without interfering with the primary users. We formulate the Dy-HySOU resource allocation problem as a mixed-integer programming to optimize the total system throughput with simultaneous heterogeneous QoS guarantee. To decrease the algorithm complexity, we divide the problem into two sub-problems: subchannel allocation and power allocation. Cutset is used to achieve the optimal subchannel allocation, and the optimal power allocation is obtained by Lagrangian dual function decomposition and subgradient algorithm. Simulation results show that the proposed algorithm further improves spectrum utilization with simultaneous fairness guarantee, and the achieved Dy-HySOU diversity gain is satisfying.

Age of Information and Success Probability Analysis in Hybrid Spectrum Access-Based Massive Cognitive Radio Networks

“…Similarly, a hybrid mode strategy for dynamic spectrum access using double threshold energy detection and Markov model was considered in [19], while a hybrid mode-based resource allocation method was proposed in [20], where the secondary base station allocates sub-channels and total power to SUs based on the received channel state information. In a similar work, the achievable throughput was maximized by [21].…”

“…By substituting (2) into ( 16), the analysis for the success probability can be obtained conditioned on Φ. The proof is straightforward from Appendix A of [20].…”

Mixed-integer programming based techniques for resource allocation in underlay cognitive radio networks: A survey

“…The sub-channel allocation problem was solved using the Hungarian method for assignment and the power allocation problem by the Lagrangian dual. Two recent papers [73] and [74] considered a hybrid underlay/overlay CRN problem. The resulting objective function was non-linear and the authors decomposed the problem into two; sub-channel allocation and power allocation.…”

“…Few papers in the literature have used some versions of decompositions. For example, [72], [73], and [74] have all used some form of decomposition, to mention just few. The most common type of decompositions we came across in the literature involve decomposing the problem into 1) power allocation and 2) sub-channel allocation.…”