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

arXiv: https://arxiv.org/abs/2204.06390 · HTML: https://mlsanity.com/paper/2204.06390/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Optimizing Coverage and Capacity in Cellular Networks using Machine Learning](https://mlsanity.com/paper/2010.13710/) (2020)
- [Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/1903.06787/) (2019)
- [Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.04723/) (2020)
- [Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems](https://mlsanity.com/paper/2106.03001/) (2021)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [Coverage Characterization of STAR-RIS Networks: NOMA and OMA](https://mlsanity.com/paper/2104.10006/) (2021)

## Built on by (most cited first, 2 of 2)

- [DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks](https://mlsanity.com/paper/2209.00511/) (2022)
- [UCB-driven Utility Function Search for Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2405.00410/) (2024)

## Abstract (arXiv)

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.