Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2311.08708v2
• Date: Fri, 17 Nov 2023 03:12:12 GMT
• Title: Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning
• Authors: Yu Min Park, Yan Kyaw Tun, Choong Seon Hong
• Abstract summary: NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system. STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission.
• Score: 15.95700167294255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.

Related papers

• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission [74.10928850232717]
  This paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm. We propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem.
  arXiv  Detail & Related papers  (2024-04-14T03:44:54Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Deep Learning Based Uplink Multi-User SIMO Beamforming Design [32.00286337259923]
  5G wireless communication networks offer high data rates, extensive coverage, minimal latency and energy-efficient performance. Traditional approaches have shortcomings when it comes to computational complexity and their ability to adapt to dynamic conditions. We propose a novel unsupervised deep learning framework, which is called NNBF, for the design of uplink receive multi-user single input multiple output (MU-SIMO) beamforming.
  arXiv  Detail & Related papers  (2023-09-28T17:04:41Z)
• DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks [55.0821435415241]
  A new paradigm in wireless communications, how to analyze the coverage and capacity performance of STAR-RISs becomes essential but challenging. To solve the coverage and capacity optimization problem in STAR-RIS assisted networks, a multi-objective policy optimization (MO-PPO) algorithm is proposed. In order to improve the performance of the MO-PPO algorithm, two update strategies, i.e., action-value-based update strategy (AVUS) and loss function-based update strategy (LFUS) are investigated.
  arXiv  Detail & Related papers  (2022-09-01T14:54:36Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study [29.884417706421218]
  This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing on the power allocation of decentralized detection. We apply and compare four algorithms designed to tackle Large scale global optimization (LGSO) problems. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.
  arXiv  Detail & Related papers  (2021-02-22T18:59:22Z)
• Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks [17.295158818748188]
  We propose a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. Users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.
  arXiv  Detail & Related papers  (2020-06-16T10:51:27Z)

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.