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arXiv 2311.04032eess.SP
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面向有源 IRS 辅助无线网络的高性能功率分配策略

High-performance Power Allocation Strategies for Active IRS-aided Wireless Network

Yifan Zhao, Xuehui Wang, Yan Wang, Xianpeng Wang, Zhilin Chen, Feng Shu, Cunhua Pan, Jiangzhou Wang

AI总结:

针对有源 IRS 辅助网络中基站与 IRS 的功率分配问题,推导 SNR 关于 PA 因子的表达式,提出 ESMPI-GA 及低复杂度三阶 Taylor 闭式方法,显著缩小与穷举搜索的速率差距。

摘要:

由于有源智能反射面(IRS)具有对抗双重衰落效应的内在能力,它正受到广泛关注。有源 IRS 的主要特点是必须由功率供电,因此自然产生了如何在基站(BS)与 IRS 之间分配总功率的问题,以充分挖掘功率分配(PA)所带来的速率增益,并消除现有 PA 策略与最优穷举搜索(ES)之间的速率差距。首先,将信噪比(SNR)表达式推导为 PA 因子 beta∈[0,1] 的函数。随后,为提升传统梯度上升(GA)的速率性能,提出了一种等间距多点初始化 GA(ESMPI-GA)方法。由于迭代式 GA 具有缓慢的线性收敛特性,所提出的 ESMPI-GA 复杂度较高。最终,为降低这种高复杂度,提出了一种以 beta0=0.5 为中心、采用三阶 Taylor 展开(TTE)的低复杂度闭式 PA 方法。仿真结果表明,所提出的 ESMPI-GA 和 TTE 明显优于等功率分配等现有方法。

英文摘要:

Due to its intrinsic ability to combat the double fading effect, the active intelligent reflective surface (IRS) becomes popular. The main feature of active IRS must be supplied by power, and the problem of how to allocate the total power between base station (BS) and IRS to fully explore the rate gain achieved by power allocation (PA) to remove the rate gap between existing PA strategies and optimal exhaustive search (ES) arises naturally. First, the signal-to-noise ratio (SNR) expression is derived to be a function of PA factor beta [0, 1]. Then, to improve the rate performance of the conventional gradient ascent (GA), an equal-spacing-multiple-point-initialization GA (ESMPI-GA) method is proposed. Due to its slow linear convergence from iterative GA, the proposed ESMPI-GA is high-complexity. Eventually, to reduce this high complexity, a low-complexity closed-form PA method with third-order Taylor expansion (TTE) centered at point beta0 = 0.5 is proposed. Simulation results show that the proposed ESMPI-GA and TTE obviously outperform existing methods like equal PA.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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