一种用于联合发射预编码与STAR-RIS系数优化的粒子群辅助梯度元学习算法
A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimization
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中文总结 AI 辅助
提出粒子群辅助梯度元学习算法，联合优化发射预编码与STAR-RIS系数，提升多用户下行加权和速率，性能优于传统交替优化。
中文摘要 AI 辅助
本文研究了在多用户下行链路中，联合优化发射预编码器以及同时透射和反射的可重构智能表面（STAR-RIS）的透射/反射系数，以最大化加权和速率（WSR）。针对这一非凸问题，我们提出了一种粒子群辅助梯度元学习（PSA-GML）算法。原始问题首先通过幅度分裂参数化和压缩预编码器表示进行等价变换，这些变换自动满足能量守恒约束并降低搜索维度。随后，粒子群优化（PSO）对STAR-RIS系数进行全局搜索，以产生高质量且对初始化鲁棒的暖启动，其中发射预编码器以闭式形式获得。与传统交替优化（AO）不同，由一阶梯度元学习训练的逐坐标长短期记忆（LSTM）元优化器进一步联合细化系数和预编码器，从数据中学习逐坐标自适应更新规则。该元优化器离线训练，并可直接应用于未见过的信道而无需进一步调整。数值结果表明，在N=32个元素和K=4个用户、10 dB条件下，PSA-GML实现了11.06 bits/s/Hz的WSR，比AO高出13.1%（即使采用多次随机重启也高出6.2%），比随机相位方案高出35.1%。在干扰受限区域，它无需手动超参数调整即可达到手工设计的Adam细化的83.9%，并且跨区域零样本迁移，表明所学更新规则捕捉了内在的WSR景观结构。
英文摘要
This paper investigates the joint optimization of the transmit precoder and the transmission/reflection coefficients of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to maximize the weighted sum rate (WSR) in a multi-user downlink. We propose a particle-swarm-assisted gradient meta-learning (PSA-GML) algorithm for this non-convex problem. The original problem is first equivalently transformed via an amplitude-split parameterization and a collapsed precoder representation, which automatically satisfy the energy-conservation constraint and reduce the search dimension. Particle swarm optimization (PSO) then performs a global search over the STAR-RIS coefficients to yield a high-quality, initialization-robust warm start, with the transmit precoder obtained in closed form. Departing from conventional alternating optimization (AO), a coordinate-wise long short-term memory (LSTM) meta-optimizer trained by first-order gradient meta-learning further refines the coefficients and precoder jointly, learning per-coordinate adaptive update rules from data. The meta-optimizer is trained offline and applied to unseen channels without further adaptation. Numerical results show that PSA-GML attains an 11.06 bits/s/Hz WSR at 10 dB with N=32 elements and K=4 users, exceeding AO by 13.1% (and by 6.2% even with multiple random restarts) and the random-phase scheme by 35.1%. In the interference-limited regime it reaches 83.9% of the hand-designed Adam refinement without manual hyper-parameter tuning, and it transfers zero-shot across regimes, indicating that the learned update rule captures the intrinsic WSR landscape structure.
发表机构
- Mianyang City College（绵阳城市学院）
机构由 AI 辅助整理，请以论文原文为准。
