Path loss modeling and performance evaluation of double IRSs-aided wireless communication systems based on spatial scattering channel model

基于空间散射信道模型的双IRS辅助无线通信系统路径损耗建模与性能评估

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Abstract

Intelligent reflecting surface (IRS) is a key enabling technology to reshape the electromagnetic propagation environment and enhance the communication performance. Current single IRS-aided or multiple distributed IRSs-aided wireless communication systems leave inter-IRSs collaboration out of consideration, and as a result, the system performance may be severely restricted. For cooperative double IRSs-aided wireless communication systems, dyadic backscatter channel model is widely used in the performance analysis and optimization. However, the impact of factors such as the size and gain of IRS elements is omitted. As a result, the performance quantification and evaluation are inaccurate. In order to avoid the above limitations, spatial scattering channel model is leveraged to quantify the path loss of the double reflection link in typical application scenarios of double IRSs-aided wireless communication systems. When the near-field condition is satisfied, the electromagnetic wave signal transmitted between IRSs is a spherical wave, which leads to high-rank channel and a lower signal to noise ratio. This paper considers the rank-1 inter-IRSs equivalent channel and derives the closed-form received signal power which reveals its relationship with the deployment of IRSs and the physical and electromagnetic properties of IRSs. Taking the impact of near/far-field effects of IRS on signal propagation further into consideration, the network configurations under which double cooperative IRSs can enhance the system performance are recognized. Simulation results show that whether double IRSs should be selected to assist in the communication between the transmitter and the receiver depends on practical network configurations, and the same number of elements should be assigned to the two IRSs to maximize the system performance if they are adopted.

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