论文标题
星际运输辅助网络中的覆盖范围和容量优化：一种机器学习方法
Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach
论文作者
论文摘要
覆盖范围和容量是无线网络中绩效评估的重要指标，而覆盖范围和容量有几个相互矛盾的关系，例如高发射功率有助于较大的覆盖范围，但高电池间干扰降低了容量性能。因此，为了在覆盖范围和容量之间取得平衡，提出了一种新型模型，以同时传输和反映可重构的智能表面（Star-riss）辅助网络的覆盖范围和容量优化。为了解决覆盖范围和容量优化（CCO）问题，提出了一种基于机器学习的多目标优化算法，即，提出了多目标近端策略优化（MO-PPO）算法。在此算法中，基于损失功能的更新策略是核心点，它能够在每个更新时通过最小值求解器计算覆盖范围和容量损失功能的权重。数值结果表明，所研究的更新策略的表现优于基于固定重量的MO算法。
Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e.g. high transmit power contributes to large coverage but high inter-cell interference reduces the capacity performance. Therefore, in order to strike a balance between the coverage and capacity, a novel model is proposed for the coverage and capacity optimization of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) assisted networks. To solve the coverage and capacity optimization (CCO) problem, a machine learning-based multi-objective optimization algorithm, i.e., the multi-objective proximal policy optimization (MO-PPO) algorithm, is proposed. In this algorithm, a loss function-based update strategy is the core point, which is able to calculate weights for both loss functions of coverage and capacity by a min-norm solver at each update. The numerical results demonstrate that the investigated update strategy outperforms the fixed weight-based MO algorithms.
