Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach

TL;DR

This paper introduces a machine learning approach using a multi-objective proximal policy optimization algorithm to optimize coverage and capacity in STAR-RIS assisted wireless networks, balancing conflicting metrics effectively.

Contribution

It proposes a novel multi-objective optimization framework with a loss function-based update strategy for STAR-RIS networks, improving over fixed-weight methods.

Findings

The proposed MO-PPO algorithm outperforms fixed weight-based algorithms.

The update strategy effectively balances coverage and capacity.

Numerical results validate the approach's superiority.

Abstract

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.