# Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2406.13280 · HTML: https://mlsanity.com/paper/2406.13280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Iteration Complexity Analysis of Block Coordinate Descent Methods](https://mlsanity.com/paper/1310.6957/) (2013)
- [Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges](https://mlsanity.com/paper/1609.06261/) (2016)
- [Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences](https://mlsanity.com/paper/2305.00135/) (2023)
- [Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization](https://mlsanity.com/paper/2007.08158/) (2020)
- [Block majorization-minimization with diminishing radius for constrained nonsmooth nonconvex optimization](https://mlsanity.com/paper/2012.03503/) (2020)
- [Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems](https://mlsanity.com/paper/2106.03001/) (2021)
- [An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems](https://mlsanity.com/paper/2112.05989/) (2021)
- [Matching Algorithms: Fundamentals, Applications and Challenges](https://mlsanity.com/paper/2103.03770/) (2021)
- [Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer](https://mlsanity.com/paper/2205.05029/) (2022)
- [Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming](https://mlsanity.com/paper/1907.12708/) (2019)
- [Satellite-based ITS Data Offloading & Computation in 6G Networks: A Cooperative Multi-Agent Proximal Policy Optimization DRL with Attention Approach](https://mlsanity.com/paper/2212.05757/) (2022)
- [SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization](https://mlsanity.com/paper/2307.15469/) (2023)
- [Federated Deep Reinforcement Learning for RIS-Assisted Indoor Multi-Robot Communication Systems](https://mlsanity.com/paper/2207.08056/) (2022)
- [Simultaneously Transmitting and Reflecting (STAR) RIS Assisted Over-the-Air Computation Systems](https://mlsanity.com/paper/2301.02044/) (2023)
- [Joint Location and Beamforming Design for STAR-RIS Assisted NOMA Systems](https://mlsanity.com/paper/2206.12836/) (2022)
- [Simultaneously Transmitting and Reflecting (STAR)-RISs: A Coupled Phase-Shift Model](https://mlsanity.com/paper/2110.02374/) (2021)
- [Simultaneous Transmitting and Reflecting-Reconfigurable Intelligent Surface in 6G: Design Guidelines and Future Perspectives](https://mlsanity.com/paper/2212.01097/) (2022)
- [Simultaneously Transmitting And Reflecting (STAR) RIS for 6G: Fundamentals, Recent Advances, and Future Directions](https://mlsanity.com/paper/2304.14180/) (2023)
- [NOMA for STAR-RIS Assisted UAV Networks](https://mlsanity.com/paper/2307.14345/) (2023)

## Built on by (most cited first, 1 of 1)

- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)

## Abstract (arXiv)

Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) provides 360-degree full-space coverage, optimizing both transmission and reflection for improved network performance and dynamic control of the indoor environment. However, deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. In this work, a novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication. To address these, we formulate an optimization problem involving user assignment, access point (AP) beamforming, and STAR-RIS phase control. A decomposition approach is used to solve the complex problem efficiently, employing a many-to-one matching algorithm for user-AP assignment and K-means clustering for resource management. Additionally, multi-agent deep reinforcement learning (MADRL) is leveraged to optimize the control of the STAR-RIS. Within the proposed MADRL framework, a novel approach is introduced in which each decision variable acts as an independent agent, enabling collaborative learning and decision making. The MADRL framework is enhanced by incorporating convex approximation (CA), which accelerates policy learning through suboptimal solutions from successive convex approximation (SCA), leading to faster adaptation and convergence. Simulations demonstrate significant improvements in network utility compared to baseline approaches.