[论文解读] Max-Min Fairness in IRS-Aided Multi-Cell MISO Systems with Joint Transmit and Reflective Beamforming

Hailiang Xie, Jie Xu+1 more|arXiv (Cornell University)|Feb 28, 2020

Advanced Wireless Communication Technologies被引用 11

一句话总结

该论文提出在IRS辅助的多小区MISO系统中联合优化发射波束成形与反射波束成形，以最大化最小加权SINR，采用精确和非精确的交替优化方法，结合SCA与SDR。非精确方法在性能和复杂度方面均优于精确方法，且在单位幅度反射约束下性能损失可忽略不计。

ABSTRACT

This paper investigates an intelligent reflecting surface (IRS)-aided multi-cell multiple-input single-output (MISO) system with several multi-antenna base stations (BSs) each communicating with a single-antenna user, in which an IRS is dedicatedly deployed for assisting the wireless transmission and suppressing the inter-cell interference. Under this setup, we jointly optimize the coordinated transmit beamforming at the BSs and the reflective beamforming at the IRS, for the purpose of maximizing the minimum weighted signal-to-interference-plus-noise ratio (SINR) at the users, subject to the individual maximum transmit power at the BSs and the reflection constraints at the IRS. To solve the non-convex problem, we first present an exact-alternating-optimization design to optimize the transmit and reflective beamforming vectors in an alternating manner, in which the transmit and reflective beamforming optimization subproblems are solved exactly by using the technique of semi-definite relaxation (SDR). However, it has high computational complexity and may lead to compromised performance due to the uncertainty of randomization in SDR. To avoid these drawbacks, we further propose an inexact-alternating-optimization design, in which the transmit and reflective beamforming optimization subproblems are solved inexactly based on the principle of successive convex approximation (SCA). In addition, to further reduce the complexity, we propose a low-complexity inexact-alternating-optimization design, in which the reflective beamforming optimization subproblem is solved more inexactly. Numerical results show that the significant performance gains achieved by the proposed three designs against benchmark schemes. Moreover, the inexact-alternating-optimization designs outperform the exact-alternating-optimization one in terms of both the achieved min-weighted-SINR value and the computational complexity.

研究动机与目标

• 利用智能反射面（IRS）解决密集多小区MISO网络中的小区间干扰和覆盖不佳问题。
• 最大化所有用户中最小加权信噪比（SINR），以确保公平性。
• 在功率和反射约束下，联合优化基站的协调发射波束成形与IRS的反射波束成形。
• 设计低复杂度算法，在性能与计算效率之间取得平衡，以实现实际部署。

提出的方法

• 建立一个非凸的最小加权SINR最大化问题，包含发射功率和IRS反射约束。
• 提出一种精确交替优化方法，利用二阶锥规划（SOCP）求解发射波束成形，利用半定松弛（SDR）求解反射波束成形。
• 提出一种基于连续凸逼近（SCA）的非精确交替优化方法，以降低计算复杂度并避免SDR随机化问题。
• 通过进一步放松反射波束成形子问题的解，提出一种低复杂度变体。
• 应用交替优化方法，迭代优化发射波束成形和反射波束成形向量。
• 通过将优化后的幅度投影至单位模长，将解适配至单位幅度反射约束，性能损失最小。

实验结果

研究问题

• RQ1 联合发射与反射波束成形设计能否显著提升IRS辅助多小区MISO系统中的公平性与SINR？
• RQ2 非精确交替优化方法在性能与复杂度方面相较于精确交替优化方法表现如何？
• RQ3 对IRS单元施加单位幅度反射约束会带来多大的性能损失？
• RQ4 所提出的算法能否在降低计算成本的同时实现近优性能，适用于实际部署？

主要发现

• 所提出的非精确交替优化方法在实现的最小加权SINR和计算复杂度方面均优于精确交替优化方法。
• 低复杂度的非精确交替优化设计显著降低了计算复杂度，仅带来轻微的性能退化。
• IRS部署通过增强有用信号并抑制小区间干扰，显著提升了SINR，尤其对边缘用户改善明显。
• 在单位幅度反射约束下，所提算法实现了近优性能，与全幅度设计相比性能损失可忽略不计。
• 数值结果表明，所提方案显著优于无IRS或采用随机反射波束成形的基准方案。