IRS辅助SIMO-MAC认知无线电系统频谱感知优化策略
Spectrum Sensing Optimization Strategy of IRS Assisted SIMO-MAC Cognitive Radio System
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摘要: 认知无线电(Cognitive Radio, CR)通过允许次用户与主用户共享频谱,在提高无线通信的频谱效率方面具有重要意义。最近,智能反射表面(Intelligent Reflecting Surface,IRS)的提出,使我们可以通过IRS反射元件重构信道环境来提高无线通信系统的通信质量。本文提出了一个IRS辅助的单输入多输出多址接入信道(Single Input Multiple Output Multiple Access Channels, SIMO-MAC) CR网络,并结合一种新型的IRS辅助多用户协作频谱感知方案来增强感知性能。目标是在次用户的功率约束以及对主用户的干扰约束下,通过联合优化次用户发射功率分配和IRS相位,最大化次用户的和速率。为了解决所建立的变量耦合的非凸问题,我们采用基于块坐标下降的高效交替优化算法,首先通过拉格朗日乘子法和注水功率分配算法以及Karush-Kuhn-Tucker (KKT)算法求解得到最优的功率分配,然后构造多目标优化问题,利用连续凸逼近、半定松弛方法将非凸问题转化为一个半正定规划的凸问题,从而求解出IRS相移矩阵的近似最优解。仿真结果表明,本文所提方案可以极大地提高认知无线电网络的频谱感知性能以及频谱效率。Abstract: Cognitive radio (CR) was of crucial importance in improving the spectral efficiency of wireless communications systems by allowing the secondary users (SU) to share the spectrum with the primary user (PU). Recently, the proposal of Intelligent Reflecting Surface (IRS) allowed us to reconstruct the channel environment through IRS reflective elements to improve the performance of wireless communication systems. In this paper, we proposed an IRS-assisted single input multiple output multiple access channels (SIMO-MAC) CR network. Moreover, we proposed a novel IRS-assisted cooperative spectrum sensing (CSS) scheme to improve the sensing performance. We formulated the sum rate maximization problem by jointly optimizing the power allocation of secondary users and the phase shifts of IRS, subjected to the power constraint of the secondary user and the interference constraint to the primary user. In order to tackle the non-convex problem with couple variables, we exploited an efficient alternating optimization algorithm based on block coordinate descent. Firstly, the optimal power allocation was obtained by adopting the Lagrange multiplier method, the water injection power allocation and Karush-Kuhn-Tucker (KKT) condition. Secondly, the multi-objective optimization problem was constructed, and the non-convex problem was transformed into a convex problem of semi-positive definite programming by using successive convex approximation (SCA) and semi-definite relaxation (SDR), so as to solve the approximate optimal solution of the IRS phase shift matrix. Simulation results show that our proposed scheme can greatly improve spectral sensing performance and the spectrum efficiency of the Cognitive radio network.