2026· Journal of Communications Software and Systems· 0 citations· 30 references
TL;DR
This paper investigates the enhancement of energy efficiency (EE) in a Multi-Input Single-Output (MISO) system under Rician fading channels using Reconfigurable Intelligent Surfaces using Reconfigurable Intelligent Surfaces (RIS).
Abstract
—Next generation networks (5G and beyond) face significant challenges in improving energy efficiency without sacrificing performance. This paper investigates the enhancement of energy efficiency (EE) in a Multi-Input Single-Output (MISO) system under Rician fading channels using Reconfigurable Intelligent Surfaces (RIS). Unlike traditional fully-active models, we investigate a different optimization framework combining Maximum Ratio Transmission (MRT) beamforming, genetic algorithms, and RIS phase shift optimization under selective RIS element activation. Two activation schemes are evaluated: random ON/OFF and Top-contributing 30% element selection based on channel gain to balance reflection gain against hardware power overhead. Simulations using MATLAB demonstrate that activating only the Top-contributing 30% of RIS elements achieves high EE with acceptable SNR. Among all techniques, phase shift optimization with selective RIS activation yields the highest EE of 5.3 × 10⁶ bits/Joule, highlighting its potential for energy-efficient 6G communications.
This paper explores the integration of RIS into 6G architectures, focusing on enhanced beamforming techniques and dynamic channel modeling to improve signal reliability, coverage, and spectral efficiency and evaluates the adaptability of RIS to environmental changes and user mobility.
This survey provides a unified reference for researchers and practitioners and establishes a roadmap for the future development of RCNs, including scalability, practical deployment, integration with emerging 6G technologies, coexistence with evolving network architectures, standardization, and security and privacy.
D. Galappaththige, C. Tellambura, Ranga Kulathunga et al.· 0 citations
A low-complexity scheme integrating RIS block selection with adaptive beamforming and a deep neural network (DNN)-based prediction architecture that avoids the exponential complexity growth typically associated with an increasing number of reflecting elements is proposed.
An alternating optimization (AO) framework is developed by integrating Dinkelbach’s method, first-order Taylor expansion, and a top- $M_{o}$ activation mechanism to maximize energy efficiency for a non-orthogonal multiple access (NOMA) system under predefined quality-of-service (QoS) constraints.
Wen-Yu Song, Hongyi Luo, Daniel K. C. So et al.· IEEE Wireless Communications...· 0 citations
Unlike conventional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) constitute a paradigm shift in intelligent surface design by facilitating electromagnetic wave manipulation in both transmission and reflection domains, thereby achieving full-spac...
This dissertation focuses on the design, optimization, and experimental evaluation of NextG RANs integrating Open RAN principles, data-driven control loops, and intelligent resource allocation, demonstrating NextG RANs reconfigurable in real time to meet 6G requirements.
Maria Tsampazi· 0 citations
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