Transformer Based Approach for Wireless Resource Allocation Problems Involving Mixed Discrete and Continuous Variables
Transformer Based Approach for Wireless Resource Allocation Problems Involving Mixed Discrete and Continuous Variables
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DOI：
10.1109/spawc53906.2023.10304444
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Bingqing Song;Zhicheng Zhou;Chenliang Li;Dongning Guo;Xiao Fu;Mingyi Hong
中科院分区：
文献类型：
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作者：
Bingqing Song;Zhicheng Zhou;Chenliang Li;Dongning Guo;Xiao Fu;Mingyi Hong
许多具有挑战性的无线通信问题涉及联合优化一组离散变量(例如，天线的子集)和一组连续变量(例如，功率分配)，其中涉及离散变量的子问题本质上是组合的。虽然已经开发了许多启发式方法来处理这些问题(例如，基于贪婪的、基于穷举搜索的方法)，但它们仍然招致较高的计算代价。在这项工作中，我们提出了一种基于机器学习的算法来学习这类问题的近似高质量解。与现有的基于学习的方法大多只关注连续问题不同，我们提出了一种两阶段方法，在第一阶段使用变压器来寻找离散变量集，然后在第二阶段对连续变量进行优化(同时固定直接变量)。我们使用MIMO系统中的联合用户调度和波束形成问题来验证我们的方法的有效性。结果表明，与启发式贪婪算法相比，即使信道状态信息质量较低，该方法也能生成高质量的活跃用户集，而只需很少的计算时间。
Many challenging wireless communication problems involve jointly optimizing a set of discrete variables (e.g., subset of antennas) and continuous variables (e.g., power allocation), where the subproblem involving discrete variables are intrinsically combinatorial. Although many heuristic methods have been developed to deal with these problems (e.g., greedy based, exhaustive search based methods), they still incur high computational costs. In this work, we propose a machine learning-based algorithm to learn an approximate high-quality solution for this class of problems. Differently than the existing learning-based methods which mostly only focusing on continuous problems, we propose a two-stage approach, where in the first stage a Transformer is used to find the set of discrete variables, followed by a second stage where the continuous variables are optimized (while fixing the dicrete variables). We demonstrate the effectiveness of our approach using a joint user scheduling and beamforming problem in MIMO systems. We show that the proposed method can generate high-quality active user sets, even with low-quality channel state information, while only using a fraction of computational time compared with a heuristic greedy algorithm.