# Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless 3D UAV Connectivity Using DRL Algorithms

arXiv: https://arxiv.org/abs/2312.09420 · HTML: https://mlsanity.com/paper/2312.09420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks](https://mlsanity.com/paper/2303.14320/) (2023)
- [Deep Contextual Bandits for Orchestrating Multi-User MISO Systems with Multiple RISs](https://mlsanity.com/paper/2202.08194/) (2022)
- [Aerial Intelligent Reflecting Surface: Joint Placement and Passive Beamforming Design with 3D Beam Flattening](https://mlsanity.com/paper/2007.13295/) (2020)
- [Joint Active and Passive Beamforming for Intelligent Reflecting Surface-Assisted Massive MIMO Systems](https://mlsanity.com/paper/1912.00728/) (2019)
- [Stochastic Analysis of Cooperative Satellite-UAV Communications](https://mlsanity.com/paper/2010.12875/) (2020)
- [Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI](https://mlsanity.com/paper/2211.09702/) (2022)
- [Unleashing 3D Connectivity in Beyond 5G Networks with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2305.04661/) (2023)

## Abstract (arXiv)

In this paper, we study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems towards the extension of the wireless cellular coverage in 3D, where multiple RISs, each equipped with an array of passive elements, are deployed to assist a base station (BS) to simultaneously serve multiple unmanned aerial vehicles (UAVs) in the same time-frequency resource of 5G wireless communications. With a focus on ensuring fairness among UAVs, our objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) at UAVs by jointly optimizing the transmit beamforming parameters at the BS and phase shift parameters at RISs. We propose two novel algorithms to address this problem. The first algorithm aims to mitigate interference by calculating the BS beamforming matrix through matrix inverse operations once the phase shift parameters are determined. The second one is based on the principle that one RIS element only serves one UAV and the phase shift parameter of this RIS element is optimally designed to compensate the phase offset caused by the propagation and fading. To obtain the optimal parameters, we utilize one state-of-the-art reinforcement learning algorithm, deep deterministic policy gradient, to solve these two optimization problems. Simulation results are provided to illustrate the effectiveness of our proposed solution and some insightful remarks are observed.