Adaptive Assignment of Heterogeneous Users for Group-Based Cooperative Spectrum Sensing
Abstract: In this paper, we consider a multichannel cognitive radio network, where cooperative secondary users have heterogeneous sensing ability in terms of their sensing accuracy. We employ a group-based cooperative spectrum sensing (CSS) scheme in which cooperating secondary users are grouped such that different groups are responsible for sensing different channels. In this group-based CSS scheme, channels sharing the same cooperating users are scheduled to sense in different sensing rounds. In this work, we propose adaptively assigning the heterogeneous cooperating secondary users to different groups to maximize the throughput efficiency while maintaining a predefined sensing accuracy. To this end, we analytically derive a closed-form expression for the throughput efficiency in terms of the average opportunistic throughput and average sensing overhead. We also formulate the throughput efficiency maximization problem for heterogeneous secondary users as a nonlinear binary programming problem, which is computationally intractable. We then propose three efficient adaptive assignment heuristics that perform the assignment of users to groups and the assignment of those groups to the sensing rounds such that the throughput efficiency is maximized. Simulation results demonstrate that our proposed assignment heuristics can achieve near optimal performance with low computational complexity and can also improve the throughput efficiency
significantly compared to the existing nonadaptive assignment and sequential CSS schemes.