基于AO⁃CCP的双智能反射面辅助的鲁棒性波束设计

Robust Millimeter⁃wave Wireless Beamforming Design Assisted by Double Intelligent Reflecting Surfaces Based on AO⁃CCP Method

5G在带来更多通信场景的同时,使得硬件布置成本和功耗急剧上升。智能反射面(Intelligent Reflecting Surface,IRS)作为一项新兴技术,因其在重塑无线通信环境、降低系统功耗及设备开销等方面的潜力而备受关注。为提高网络的鲁棒性和能效,在非完美的信道状态信息(CSI)前提下提出了双IRS辅助的点对点毫米波(mmWave)无线通信算法。建立了一个在用户服务质量(QoS)、IRS相移约束下的功耗最小化优化模型。采用交替优化(AO)方法对发射波束赋形矢量(TBFV)和反射波束赋形矢量(RBFV)解耦,引入松弛变量和凸凹过程(CCP)方法对非凸问题进行转换分解,最后使用CVX工具求解。仿真结果表明,与最大比传输(MRT)点对点通信的方案相比,所提优化模型使AP的功耗降低25%,同时降低了10%的中断概率,验证了在能量利用率、鲁棒性、覆盖范围等方面的实际优势。

While 5G brings more communication scenarios,the cost of hardware deployment and power consumption has risen sharply.As an emerging technology,Intelligent Reflecting Surface(IRS)has attracted much attention due to its potential in reshaping wireless communication environment,reducing system power consumption and equipment overhead.To improve the robustness and energy efficiency of network,a double IRS.assisted point.to.point millimeter wave(mmWave)wireless communication algorithm is designed under the premise of imperfect channel state information(CSI).An optimization model for minimizing power consumption under the constraints of user Quality.of.Service(QoS)and IRS phase shift is established.The alternative optimization(AO)method is used to decouple the transmit beamforming vector(TBFV)and the reflected beamforming vector(RBFV),and the non.convex problem is transformed and decomposed by the slack variable and the convex.concave process(CCP)method,and finally the CVX tool is used.Simulation results show that,compared with the maximum ratio transmission(MRT)point.to.point communication scheme,the proposed optimization model reduces the power consumption of the AP by 25%and reduces the interruption probability by 10%,which verifies the energy utilization and robustness,coverage and other practical advantages.