论文标题
从乏味到简单的RIS辅助无源边界：RICIAN通道的放松算法
Transforming RIS-Assisted Passive Beamforming from Tedious to Simple: A Relaxation Algorithm for Rician Channel
论文作者
论文摘要
本文研究了可重新配置的智能表面（RIS）辅助误差通信系统中最大化信噪比（SNR）的问题。该问题将被重新重新构成与单位圈子约束的复杂二次形式问题。我们证明，当它是排名第一的问题时，SNR最大化问题具有封闭形式的全球最佳解决方案，而前研究人员将其视为优化问题。此外，我们提出了一种放松算法（RA），该算法放宽了对雷利商问题的约束，然后将解决方案投射回去，其中ra获得的SNR与上限的SNR相同，但具有明显较低的时间消耗。然后，当发射机天线N_T和RIS n的单位数量与N/n_t-> c一起生长时，我们会渐近地分析其性能。最后，我们的数值模拟表明，RA可实现上限的98％以上的表现，并在歧管优化（MO）的时间消耗（MO）和0.1％的半决赛弛豫（SDR）中达到了1％。
This paper investigates the problem of maximizing the signal-to-noise ratio (SNR) in reconfigurable intelligent surface (RIS)-assisted MISO communication systems. The problem will be reformulated as a complex quadratic form problem with unit circle constraints. We proved that the SNR maximizing problem has a closed-form global optimal solution when it is a rank-one problem, whereas the former researchers regarded it as an optimization problem. Moreover, We propose a relaxation algorithm (RA) that relaxes the constraints to that of Rayleigh's quotient problem and then projects the solution back, where the SNR obtained by RA achieves much the same SNR as the upper bound but with significantly low time consumption. Then we asymptotically analyze its performance when the transmitter antennas n_t and the number of units of RIS N grow large together, with N/n_t -> c. Finally, our numerical simulations show that RA achieves over 98% of the performance of the upper bound and takes below 1% time consumption of manifold optimization (MO) and 0.1% of semidefinite relaxation (SDR).
