A novel frame structure for centralized cooperative cognitive networks to achieve overhead-throughput tradeoff
Mingyue Xu, Ying Wang, Gen Li, Weidong Zhang, Gaofeng Nie · 2012
This paper addresses overhead-throughput tradeoff issues for the centralized cooperative cognitive network. Taking the reporting overhead into consideration, a novel frame structure consisting of M + 1 subframes is proposed to maximize the achievable throughput of the cognitive network. According to the channel condition between primary user (PU) and each secondary user (SU), the overhead-throughput tradeoff problem is investigated in two scenarios. For scenario I, we focus on optimizing the number of reporting secondary users (SUs) to achieve overhead-throughput tradeoff. For scenario II, we not only optimize the number of reporting SUs, but also design reporting SUs' selection methods. Numerical results show that under the proposed frame structure, there exists an optimal number of reporting SUs to achieve overhead-throughput tradeoff, and maximize the achievable throughput of the cognitive network.