有限分辨率微波线性模拟计算机（MiLAC）辅助的多用户波束成形
Finite-Resolution Microwave Linear Analog Computer (MiLAC)-Aided Multiuser Beamforming
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文研究有限分辨率微波线性模拟计算机辅助的多用户波束成形，通过联合优化离散电纳和功率分配，并利用ADMM算法，以显著降低的连接性和有限分辨率逼近未量化全连接MiLAC的性能。
AI中文摘要：
大规模天线阵列对于未来无线网络至关重要，但会带来沉重的硬件和数字处理负担。微波线性模拟计算机（MiLAC）为解决这些挑战提供了一种有前景的架构。在MiLAC中，波束成形完全在模拟域中通过由可调导纳元件组成的可重构多端口微波网络实现。然而，现有研究假设这些元件是连续可调的，而实际实现只能支持有限数量的可配置状态。本文研究了在无源互易约束和指定连接模式下的有限分辨率MiLAC辅助多用户波束成形问题。每个可调电纳从对称均匀的有限分辨率码本中选择。我们提出了一个在线问题，针对给定码本联合优化离散电纳和功率分配，以及一个离线问题，基于信道统计设计码本。通过利用奇均匀码本结构，我们将每个多电平电纳表示为二电平变量的加权和，并为在线波束成形和离线码本设计问题开发了精确的连续惩罚模型。我们证明，对于足够大的惩罚参数，这些惩罚模型与其原始离散对应问题全局等价。基于这些公式，我们开发了高效的基于ADMM的算法来解决这两个问题。数值结果表明，未量化的全连接MiLAC的性能可以通过显著降低的连接性和有限分辨率来逼近。特别是，一个3比特茎连接MiLAC实现了未量化全连接MiLAC总速率性能的95%以上，而仅需要其22%的可调元件。
英文摘要：
Large-scale antenna arrays are essential to future wireless networks but impose significant hardware and digital-processing burdens. The microwave linear analog computer (MiLAC) offers a promising architecture for addressing these challenges. In a MiLAC, beamforming is realized entirely in the analog domain through a reconfigurable multiport microwave network comprising tunable admittance elements. However, existing studies assume that these elements are continuously tunable, whereas practical implementations can support only finitely many configurable states. This paper investigates finite-resolution MiLAC-aided multiuser beamforming under lossless and reciprocal constraints with prescribed connectivity patterns. Each tunable susceptance is selected from a symmetric uniform finite-resolution codebook. We formulate an online problem that jointly optimizes the discrete susceptances and power allocation for a prescribed codebook, and an offline problem that designs the codebook based on channel statistics. By exploiting the odd-uniform codebook structure, we express each multilevel susceptance as a weighted sum of two-level variables and develop exact continuous penalty models for both the online beamforming and offline codebook design problems. We prove that, for sufficiently large penalty parameters, these penalty models are globally equivalent to their original discrete counterparts. Building on these formulations, we develop efficient ADMM-based algorithms for solving the two problems. Numerical results demonstrate that the performance of an unquantized fully-connected MiLAC can be approached with substantially reduced connectivity and finite resolution. In particular, a 3-bit stem-connected MiLAC achieves more than 95% of the sum-rate performance of the unquantized fully-connected MiLAC while requiring only 22% of its tunable components.
