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

• URL: http://arxiv.org/abs/2312.09420v1
• Date: Tue, 14 Nov 2023 06:43:36 GMT
• Title: Fairness-Driven Optimization of RIS-Augmented 5G Networks for Seamless 3D UAV Connectivity Using DRL Algorithms
• Authors: Yu Tian, Ahmed Alhammadi, Jiguang He, Aymen Fakhreddine, Faouzi Bader
• Abstract summary: We study the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output systems. We propose two novel algorithms to address this problem.
• Score: 8.296140341710462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Sum Throughput Maximization in Multi-BD Symbiotic Radio NOMA Network Assisted by Active-STAR-RIS [2.5049072387358247]
  We employ active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASRIS) to aid in establishing and enhancing communication within a commensal symbiotic radio (CSR) network. Unlike traditional RIS, ASRIS not only ensures coverage in an omni directional manner but also amplifies received signals, consequently elevating overall network performance.
  arXiv  Detail & Related papers  (2024-01-16T11:54:32Z)
• STAR-RIS-Assisted-Full-Duplex Jamming Design for Secure Wireless Communications System [38.44290756174189]
  We propose a novel secure communication scheme to protect critical and sensitive devices from eavesdroppers. We aim to maximize the FD beam shift coefficients for the ESRIS, as mode selection, as to eavesdropper amplitudes and phase shift coefficients for the MSRIS.
  arXiv  Detail & Related papers  (2023-09-08T19:36:02Z)
• Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications [59.34642007625687]
  The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots. An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
  arXiv  Detail & Related papers  (2022-05-03T17:14:47Z)
• Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces [115.85072043481414]
  We propose a novel framework for guaranteeing ultra-reliable millimeter wave (mmW) communications using multiple artificial intelligence (AI)-enabled reconfigurable intelligent surfaces (RISs) The use of multiple AI-powered RISs allows changing the propagation direction of the signals transmitted from a mmW access point (AP) Two centralized and distributed controllers are proposed to control the policies of the mmW AP and RISs.
  arXiv  Detail & Related papers  (2021-03-31T19:15:49Z)
• Digital Beamforming Robust to Time-Varying Carrier Frequency Offset [21.18926642388997]
  We present novel beamforming algorithms that are robust to signal corruptions arising from a time-variant carrier frequency offset. We propose two atomic-norm-minimization (ANM)-based methods to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals.
  arXiv  Detail & Related papers  (2021-03-08T18:08:56Z)
• Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design [39.21220050099642]
  Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems. We propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies.
  arXiv  Detail & Related papers  (2021-01-22T14:56:28Z)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)
• Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning [21.770491711632832]
  The reconfigurable intelligent surface (RIS) has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL) The proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-02-24T04:28:44Z)
• RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design [116.88396201197533]
  A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency. A novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. A decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS.
  arXiv  Detail & Related papers  (2020-01-28T14:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.