Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach
This work addresses coverage and capacity optimization for wireless network operators, presenting an incremental improvement with a novel update strategy.
The paper tackles the conflicting relationship between coverage and capacity in wireless networks by proposing a machine learning-based multi-objective optimization algorithm for STAR-RISs assisted networks, achieving improved performance over fixed weight-based methods as demonstrated in numerical results.
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.