Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 208-210· 0 citations· 9 references
Abstract
As the transition toward sixth-generation (6G) wireless networks accelerates, the demand for ultra-low latency and high energy efficiency has become paramount. Traditional Mobile Edge Computing (MEC) frameworks face significant challenges in highly dynamic and interference-limited environments. This survey explores a synergistic architectural framework that integrates Fluid Antenna Systems (FAS), Reconfigurable Intelligent Surfaces (RIS), and Hybrid Non-Orthogonal Multiple Access (NOMA)-MEC to address these requirements. We investigate how FAS-enabled port selection enhances channel disparity for optimized NOMA pairing, while RIS-controlled interference landscapes provide the stability necessary for robust Successive Interference Cancellation (SIC) decoding. This comprehensive survey provides a detailed roadmap for future research, highlighting the critical role of programmable physical layers in enabling the next generation of intelligent edge computing systems.
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
Charalampos Oikonomidis, E. T. Michailidis, N. Miridakis· 0 citations
The migration of wireless networks towards sixth-generation (6G) cellular communications and Wi-Fi 8 is targeting ultra-high data rates, massive connectivity, and in-built sensing. However, these sophisticated applications introduce new challenges in achieving high levels of energy efficiency, spectral efficiency, and sensing accuracy in ultra-dense and heterogeneous wireless networks. The existing literature has mainly addressed energy efficiency or spectral efficiency in hybrid multi-radio access technology (multi-RAT)-based wireless communications. Therefore, in this manuscript, an intelligent Integrated Sensing and Communication (ISAC) solution for hybrid 6G and Wi-Fi 8 communications is proposed to concurrently maximize energy efficiency (EE), spectral efficiency (SE), and sensing performance. The system model used in this manuscript utilizes Reconfigurable Intelligent Surfaces (RIS) and formulates a multi-objective problem considering communications quality of service and sensing constraints. For this non-convex problem, an optimized deep reinforcement learning (DRL)-based controller to dynamically control RIS phases, beamforming, as well as power allocation in multiple radio communications links has been proposed. The simulation results indicate that the proposed method significantly enhances EE and SE while maintaining reliable environmental sensing accuracy under the simulated conditions, attaining a spectral efficiency of 7.1 bits/s/Hz with 32 RIS elements and 12.3 bits/s/Hz with 128 RIS elements. This work proposes novel framework that performs joint energy efficiency, spectral efficiency, and sensing performance optimization for hybrid 6G and Wi-Fi 8 networks. The proposed approach utilizes RIS-aided ISAC to enable intelligent multi-objective optimization using deep reinforcement learning.
Zacheous Aasa· International Journal of Net...· 0 citations
Reconfigurable Intelligent Surfaces (RISs) and Movable Antennas (MAs) have emerged as transformative enablers of next-generation wireless networks by introducing programmable and adaptive control over the radio environment. However, their performance critically depends on how and where these elements are positioned across diverse deployment settings. Despite extensive research on RIS/MA-assisted communications, a unified understanding of their placement strategies, spanning aerial, terrestrial, static, dynamic, and hybrid (indoor/outdoor) environments, remains absent. To bridge this gap, this paper provides a comprehensive analysis of the state-of-the-art in RIS and MA placement. We first outline the theoretical foundations of RIS and MAs, highlighting their complementary roles in environment reconfiguration and channel adaptation. Next, we propose an original taxonomy that categorizes placement paradigms based on mobility, spatial hierarchy, and environmental context. The survey systematically analyzes existing contributions within this taxonomy, compares key methodologies, and synthesizes major performance metrics and trade-offs reported in the literature. Furthermore, we identify critical challenges related to large-scale coordination, energy efficiency, and environmental adaptability, and outline promising future research directions integrating AI-driven control, digital twin modeling, and cross-layer design. This work offers the first unified framework for understanding and classifying RIS and MA placement, providing researchers and practitioners with a solid foundation for designing intelligent, reconfigurable, and mobility-aware wireless environments.
Omar Sami Oubbati, A. Ameur, A. Rachedi et al.· IEEE Open Journal of the Com...· 1 citation
Wireless access technology is a key area of research since 6G wireless networks are anticipated to enable immersive communication, enormous IoT, intelligent sensing, and ubiquitous coverage. Utilizing the method of literature review, this paper examines Massive Multiple-Input Multiple-Output (Massive MIMO) and Non-Orthogonal Multiple Access (NOMA) as two complementary access technologies for 6G wireless networks and compares their roles in the spatial and power domains. The review finds that Massive MIMO improves spectrum efficiency, system capacity, and link reliability through large antenna arrays, beamforming, and spatial multiplexing, while NOMA increases access density and edge-user fairness through power-domain multiplexing and successive interference cancellation. Their convergence can better support high-capacity 6G access, but it also introduces asynchronous interference, channel state information overhead, and high-frequency coherence challenges. By connecting these challenges with Radio Access Network digital twins, AI-assisted scheduling, and integrated sensing and communication, the paper clarifies a practical research path for Massive MIMO-NOMA convergence in future 6G networks.
Yijiao Liu· Applied and Computational En...· 0 citations
—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.
H. Al-Tayyar, S. Ayoob· Journal of Communications So...· 0 citations
This research Paper focuses on an Ultra-Dense Network (UDN) is a core enabling technology for 5G and 6G wireless systems, proposed to meet escalating capacity demands and support new high-rate, low-latency services. The fundamental principle is network densification, achieved by deploying a massive number of low-power Access Points (APs) and communication links per unit area, dramatically shortening the distance between transmitters and receivers to improve signal quality and spatial frequency reuse. Ultimately, the successful operation of UDNs relies heavily on advanced, AI-driven management systems to dynamically optimize resources, manage interference, and ensure seamless, high-performance connectivity in an inherently complex environment: Our key objectives are improving the Massive Capacity and Data Rates, Enhanced Coverage and Reliability, Ultra-Low Latency, Massive Connectivity Internet of Things (IoT) to maintaining the advanced resilient communication system all the time and every times.
P. Pradhan, Pramod D Gangejar· Journal of Ad-hoc Network an...· 0 citations
