针对多输入多输出(MIMO:Multiple Input Multiple Output)认知物联网(C-IoT:Cognitive Internet of Things)系统中存在频谱效率低的问题，提出了一种基于智能反射面(IRS:Intelligent Reflecting Surface)辅助的交替迭代的块坐标下降算法。以主接收机处的干扰功率、次发射机处的发射功率和IRS处的单位模为约束条件，通过联合优化次发射机处的主动波束形成和IRS处的被动波束形成最大化系统加权和速率。将复杂的非凸优化问题分解为子问题后，分别使用拉格朗日对偶法和逐次凸逼近法对子问题进行处理。仿真结果表明，在多天线用户场景下所提算法可以快速收敛，通过增加IRS反射元件的数量或正确部署IRS的位置可以有效提高C-IoT系统的频谱效率。
Joint Beamforming Design for IRS-Assisted C-IoT System
Aiming at the problem of low spectrum efficiency in MIMO(Multiple Input Multiple Output)C-IoT(Cognitive Internet of Things)systems,a block coordinate descent algorithm based on alternating iterative assisted by IRS(Intelligent Reflecting Surface)is proposed.System weighted sum rate is maximized by jointly optimizing active beamforming at secondary transmitter and passive beamforming at IRS,and is constrained by the interference power at the primary receiver,the transmit power at the secondary transmitter,and the unit mode at the IRS.After decomposing the complex non-convex optimization problem into subproblems,the subproblems are processed using the Lagrange Dual method and the Successive Convex Approximation method,respectively.The simulation results show that the proposed algorithm can converge quickly in a multi-antenna user scenario,and the spectrum efficiency of the C-IoT system can be effectively improved by increasing the number of IRS reflective elements or correctly deploying the location of the IRS.
intelligent reflecting surfacecognitive internet of thingsmultiple input multiple output(MIMO)beamforming
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