[论文解读] Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization

Yifei Yang, Beixiong Zheng+2 more|arXiv (Cornell University)|Jun 21, 2019

Advanced Wireless Communication Technologies参考文献 22被引用 30

一句话总结

该论文提出了一种适用于IRS增强型OFDM系统的实用传输协议，通过将相邻的智能反射面（IRS）单元分组，以减少信道训练开销，同时实现发射功率分配与IRS反射系数的联合优化。该方法通过平衡训练开销与波束成形灵活性，实现了显著的速率增益，仿真结果表明其在真实信道条件下的性能显著优于传统方案。

ABSTRACT

Intelligent reflecting surface (IRS) is a promising new technology for achieving both spectrum and energy efficient wireless communication systems in the future. However, existing works on IRS mainly consider frequency-flat channels and assume perfect knowledge of channel state information (CSI) at the transmitter. Motivated by this, in this paper we study an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system under frequency-selective channels and propose a practical transmission protocol with channel estimation. First, to reduce the overhead in channel training and estimation and to exploit the channel spatial correlation, we propose a novel IRS elements grouping method, where each group consists of a set of adjacent IRS elements that share a common reflection coefficient. Based on this grouping method, we propose a practical transmission protocol where only the combined channel of each group needs to be estimated, thus substantially reducing the training overhead. Next, with any given grouping and estimated CSI, we formulate the problem to maximize the achievable rate by jointly optimizing the transmit power allocation and the IRS passive array reflection coefficients. Although the formulated problem is non-convex and thus difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution for it, by alternately optimizing the power allocation and the passive array coefficients in an iterative manner, along with a customized method for the initialization. Simulation results show that the proposed design significantly improves the OFDM link rate performance as compared to the case without using IRS. Moreover, it is shown that there exists an optimal size for IRS elements grouping which achieves the maximum achievable rate due to the trade-off between the training overhead and IRS passive beamforming flexibility.

研究动机与目标

• 解决在频率选择性衰落环境下，IRS辅助的OFDM系统中训练开销高和信CSI不完善的问题。
• 通过让相邻的IRS单元共享反射系数，减少信道估计开销。
• 在估计的CSI条件下，联合优化发射功率分配与IRS无源波束成形，以实现速率最大化。
• 通过最优IRS分组比分析训练开销与波束成形增益之间的权衡。
• 在不同SNR范围和信道相干时间下评估性能。

提出的方法

• 提出一种IRS单元分组策略，其中相邻单元共享同一反射系数，从而减少需估计的信道数量。
• 设计一种实用的传输协议，仅估计每组的合并信道，显著降低训练开销。
• 建立一个非凸优化问题，通过联合功率分配与无源波束成形系数设计以最大化可实现速率。
• 开发一种迭代算法，交替优化功率分配与反射系数，并采用定制化初始化方法以获得高质量的次优解。
• 采用考虑空间相关性和频率选择性衰落环境中导频开销的信道估计模型。
• 引入与相干时间相关的分组策略，表明最优分组比随信道相干时间增加而提高。

实验结果

研究问题

• RQ1 如何通过IRS单元分组在不牺牲波束成形增益的前提下减少IRS-OFDM系统中的训练开销？
• RQ2 在训练开销与无源波束成形灵活性之间达到平衡的最优IRS分组比是多少？
• RQ3 在所提出的协议下，可实现速率性能如何随SNR和信道相干时间变化？
• RQ4 联合优化功率分配与IRS反射系数是否能优于采用随机或固定相移的基准方案？
• RQ5 信道估计误差对系统性能有何影响，特别是在低SNR场景下？

主要发现

• 所提出的协议在无IRS分组的系统中实现了显著更高的可实现速率，尤其在低SNR和高相干时间场景下表现更优。
• 由于训练开销与波束成形多样性之间的权衡，存在一个最优的IRS分组比，性能在中等分组比时达到峰值。
• 在低SNR（γd = 0 dB）时，过低或过高的分组比均导致性能下降，这是由于信道估计误差增加，可实现速率在中等分组比时出现明显峰值。
• 在高SNR（γd = 20 dB）时，最优分组比随信道相干时间增加而提高，因为更长的相干时间可更好地补偿训练开销。
• 所提出的带定制初始化的迭代算法在远低于全优化复杂度的情况下，实现了接近最优的速率性能。
• 即使在高训练开销下，所提方法在所有SNR范围和相干时间条件下仍优于随机相位基准方案。