AI 中文总结
针对多AP多用户系统在统计CSI下的和速率最大化问题，提出迭代交替优化算法与基于注意力Edge-GNN的GNN框架，实验显示GNN性能优于迭代算法且可泛化至不同用户数。
AI 中文摘要
本研究针对多接入点（AP）多用户系统中，在统计信道状态信息（CSI）条件下以和速率最大化为目标的联合接入点选择与预编码问题展开研究。为此，我们提出两种方法：第一种是迭代交替优化算法，该算法通过随机加权最小均方误差（SWMMSE）算法更新预编码向量，通过投影梯度下降步骤更新分配变量；第二种是基于图神经网络（GNN）的框架，该框架在推理阶段通过单次前向传播解决同一问题。我们在基于注意力的边图神经网络（Edge-GNN）架构基础上将其扩展至多AP场景，使其仅从统计CSI中联合学习分配变量与预编码向量。结果表明，在测试的信噪比（SNR）范围内，该GNN的性能优于迭代算法，且能在用户数量变化时保持相当的性能，我们还将两种方法与多种基线技术进行了对比。
英文摘要
This work addresses joint access point (AP) selection and precoding for sum-rate maximization under statistical channel state information (CSI) in multi-AP multi-user systems. To this end, we propose two approaches. The first method is an iterative alternating optimization algorithm that updates the precoding vectors via the stochastic WMMSE (SWMMSE) algorithm and the assignment variables via a projected gradient descent step. The second method is a graph neural network (GNN)-based framework that solves the same problem in a single forward pass during inference. Building on an attention-based Edge-GNN architecture, we extend it to a multi-AP scenario, enabling the joint learning of assignment variables and precoding vectors from statistical CSI alone. Results show that the GNN outperforms the iterative algorithm across the tested signal-to-noise ratio (SNR) range and generalizes to varying numbers of users with comparable performance. Both approaches are also compared to various baseline techniques.
CommentsSubmitted to IEEE for possible publication
