论文标题
智能反射表面辅助认知无线电系统的稳健波束形成设计不完美的CSI
Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI
论文作者
论文摘要
在本文中，引入了智能反射表面（IRS），以增强认知无线电（CR）系统的网络性能。具体而言，我们根据IRS AIDED CR系统中的主要用户（PU）相关通道的统计通道错误模型（CSI）信息模型（CSI）信息模型（CSI）信息模型（CSI）信息模型和统计CSI错误模型研究了强大的波束形成设计。我们在二级用户（SU）发射器（ST）和IRS处共同优化二级用户（SU）发射器的发送预编码（TPC），以最大程度地减少ST的总发射功率，但会受到SUS服务质量的影响，这是反射光束形成的PU和单位模量的有限干扰。连续的凸近似方法（SCA）方法，Schur的补体，一般的标志性原理，逆卡方分布和惩罚凸孔concave程序用于处理这些复杂的约束。非凸优化问题转化为多个凸子问题，并提出了有效的算法。仿真结果验证了所提出的算法的效率，并揭示了CSI不确定性对ST的最小传输功率和优化问题的可行性率的影响。仿真结果还表明，应仔细选择ST的发射天线和IRS的相移数，以平衡通道实现的可行性率和总发射功率。
In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) at the secondary user (SU) transmitter (ST) and phase shifts at the IRS to minimize the ST' s total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. The successive convex approximation (SCA) method, Schur's complement, General sign-definiteness principle, inverse Chi-square distribution and penalty convex-concave procedure are invoked for dealing with these intricate constraints. The non-convex optimization problems are transformed into several convex subproblems and efficient algorithms are proposed. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems. Simulation results also show that the number of transmit antennas at the ST and the number of phase shifts at the IRS should be carefully chosen to balance the channel realization feasibility rate and the total transmit power.
