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arXiv 2310.04063cs.ITmath.IT
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完美与非完美CSI下离散相移IRS辅助多用户网络的全局最优资源分配设计

Globally Optimal Resource Allocation Design for Discrete Phase Shift IRS-Assisted Multiuser Networks with Perfect and Imperfect CSI

Yifei Wu, Dongfang Xu, Derrick Wing Kwan Ng, Robert Schober, Wolfgang Gerstacker

AI总结:

本文研究了离散相移IRS辅助多用户MISO系统在完美与非完美CSI下的资源分配问题,联合优化基站波束成形与IRS相移以最小化总发射功率,提出基于GBD的全局最优算法和基于SCA的低复杂度次优算法,数值结果验证了其有效性与显著性能增益。

摘要:

智能反射面(IRS)是一种极具前景的低成本解决方案,通过实现无线传播环境的定制化,能够在未来通信系统中获得高频谱效率和能量效率。尽管目前对IRS辅助多用户无线通信系统的资源分配设计已有大量研究,但最优设计及其对应的性能上界仍未被完全理解。为弥补这一知识空白,本文研究了采用实际离散IRS相移器的IRS辅助多用户多输入单输出系统的最优资源分配设计。具体而言,我们联合优化基站的波束成形向量和离散IRS相移,以在完美和非完美信道状态信息(CSI)的情况下最小化总发射功率。为此,我们开发了两种基于广义Benders分解(GBD)方法的新算法,分别用于在完美和非完美CSI下获得全局最优解。此外,为便于实际实现,我们利用逐次凸近似(SCA)提出了两种相应的低复杂度次优算法,并保证了其收敛性。特别是对于非完美CSI,我们采用有界误差模型来表征CSI的不确定性,并提出一种新的变换将鲁棒服务质量约束凸化。我们的数值结果证实了所提出的基于GBD的算法在完美和非完美CSI下对所考虑系统的最优性。此外,我们发现所提出的两种基于SCA的算法均能在少数几次迭代内达到局部最优解。并且,与基于交替优化的最先进解决方案相比,所提出的低复杂度基于SCA的方案实现了显著的性能增益。

英文摘要:

Intelligent reflecting surfaces (IRSs) are a promising low-cost solution for achieving high spectral and energy efficiency in future communication systems by enabling the customization of wireless propagation environments. Despite the plethora of research on resource allocation design for IRS-assisted multiuser wireless communication systems, the optimal design and the corresponding performance upper bound are still not fully understood. To bridge this gap in knowledge, in this paper, we investigate the optimal resource allocation design for IRS-assisted multiuser multiple-input single-output systems employing practical discrete IRS phase shifters. In particular, we jointly optimize the beamforming vector at the base station and the discrete IRS phase shifts to minimize the total transmit power for the cases of perfect and imperfect channel state information (CSI) knowledge. To this end, two novel algorithms based on the generalized Benders decomposition (GBD) method are developed to obtain the globally optimal solution for perfect and imperfect CSI, respectively. Moreover, to facilitate practical implementation, we propose two corresponding low-complexity suboptimal algorithms with guaranteed convergence by capitalizing on successive convex approximation (SCA). In particular, for imperfect CSI, we adopt a bounded error model to characterize the CSI uncertainty and propose a new transformation to convexify the robust quality-of-service constraints. Our numerical results confirm the optimality of the proposed GBD-based algorithms for the considered system for both perfect and imperfect CSI. Furthermore, we unveil that both proposed SCA-based algorithms can attain a locally optimal solution within a few iterations. Moreover, compared with the state-of-the-art solution based on alternating optimization, the proposed low-complexity SCA-based schemes achieve a significant performance gain.

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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