RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization

• URL: http://arxiv.org/abs/2512.18788v1
• Date: Sun, 21 Dec 2025 16:00:50 GMT
• Title: RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization
• Authors: George C. Alexandropoulos, Kostantinos D. Katsanos, George Stamatelis, Ioannis Gavras,
• Abstract summary: chapter overviews the concept of Smart Wireless Environments motivated by the emerging technology of Reconfigurable Intelligent Surfaces (RISs)<n> Focusing on the recent trend of Beyond-Diagonal (BD) RISs, two distributed designs of respective SWEs are presented.<n>A hybrid distributed and fusion machine learning framework based on multi-branch attention-based convolutional Neural Networks (NNs), NN parameter sharing, and neuroevolutionary training is presented.<n>Performance evaluation results showcase that the distributedly optimized RIS-enabled SWE achieves near-optimal sum-rate performance with low online computational complexity.
• Score: 30.03861501202603
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This chapter overviews the concept of Smart Wireless Environments (SWEs) motivated by the emerging technology of Reconfigurable Intelligent Surfaces (RISs). The operating principles and state-of-the-art hardware architectures of programmable metasurfaces are first introduced. Subsequently, key performance objectives and use cases of RIS-enabled SWEs, including spectral and energy efficiency, physical-layer security, integrated sensing and communications, as well as the emerging paradigm of over-the-air computing, are discussed. Focusing on the recent trend of Beyond-Diagonal (BD) RISs, two distributed designs of respective SWEs are presented. The first deals with a multi-user Multiple-Input Single-Output (MISO) system operating within the area of influence of a SWE comprising multiple BD-RISs. A hybrid distributed and fusion machine learning framework based on multi-branch attention-based convolutional Neural Networks (NNs), NN parameter sharing, and neuroevolutionary training is presented, which enables online mapping of channel realizations to the BD-RIS configurations as well as the multi-user transmit precoder. Performance evaluation results showcase that the distributedly optimized RIS-enabled SWE achieves near-optimal sum-rate performance with low online computational complexity. The second design focuses on the wideband interference MISO broadcast channel, where each base station exclusively controls one BD-RIS to serve its assigned group of users. A cooperative optimization framework that jointly designs the base station transmit precoders as well as the tunable capacitances and switch matrices of all metasurfaces is presented. Numerical results demonstrating the superior sum-rate performance of the designed RIS-enabled SWE for multi-cell MISO networks over benchmark schemes, considering non-cooperative configuration and conventional diagonal metasurfaces, are presented.

Related papers

• RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches [51.56300276709421]
  This paper investigates a reconfigurable intelligent surface (RIS)-assisted multi-waveguide pinching-antenna (PA) system (PASS) for multi-user downlink information transmission.<n>By leveraging a graph-structured topology of the RIS-assisted PASS, a novel three-stage graph neural network (GNN) is proposed, which learns PA positions based on user locations.
  arXiv  Detail & Related papers  (2025-11-25T13:43:44Z)
• Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI [2.8408947965063778]
  The framework exploits uplink pilot sequences to implicitly learn channel characteristics, reducing both pilot overhead and inference complexity.<n>The proposed design achieves over 90% of the spectral efficiency of exhaustive search with only 2.2% of its computation time, cutting energy consumption by nearly two orders of magnitude.
  arXiv  Detail & Related papers  (2025-09-16T04:29:14Z)
• Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models [4.02185297590683]
  A space-air-ground integrated network (SAGIN) for the Internet of Things is investigated.<n>The MF-RIS is capable of simultaneously amplifying, and harvesting wireless energy.
  arXiv  Detail & Related papers  (2025-07-22T03:40:56Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods [83.98961686408171]
  Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS.
  arXiv  Detail & Related papers  (2022-04-28T09:26:14Z)
• A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints [4.727307803726522]
  We consider a RIS-aided multiuser multiple-input single-output downlink communication system. We develop a deep quantization neural network (DQNN) to simultaneously design the active and passive beamforming. We extend the two proposed DQNN-based algorithms to the case that the discrete phase shifts and imperfect CSI are considered simultaneously.
  arXiv  Detail & Related papers  (2021-11-12T03:53:20Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces [50.622375361505824]
  Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs. We devise low-complexity supervised learning approaches for the RISs' phase configurations.
  arXiv  Detail & Related papers  (2020-10-09T05:35:27Z)
• 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.