2026· Journal of Communications Software and Systems· 1 citation· 38 references
TL;DR
An adaptive multi-mode Deep Reinforcement Learning (DRL) framework for intelligent RIS-assisted anti-jamming communication in dynamic 6G wireless networks that maintains stable communication performance under strong jamming power, CSI uncertainty, and high-mobility scenarios is proposed.
Abstract
—This paper proposes an adaptive multi-mode Deep Reinforcement Learning (DRL) framework for intelligent RIS-assisted anti-jamming communication in dynamic 6G wireless networks. The proposed Framework jointly integrates RIS beamforming, channel hopping, and transmit power adaptation through a DRL-Driven decision engine capable of dynamically responding to varying interference conditions and channel fluctuations. To improve deployment realism, practical constraints including imperfect Channel State Information (CSI), finite-resolution RIS phase quantization, reflection loss, control delay, and user mobility are incorporated into the system model. The anti-jamming problem is formulated as a Markov decision process and solved using DQN, PPO, and SAC algorithms. Extensive simulations are conducted using MATLAB-based wireless channel modeling and Python-based DRL training platforms. Simulation results demonstrate that the proposed framework achieves approximately 25%–40% higher throughput and 18%– 35% SINR improvement compared with conventional anti-jamming approaches. Moreover, the proposed scheme maintains stable communication performance under strong jamming power, CSI uncertainty, and high-mobility scenarios. Statistical evaluations over 20 independent random seeds further confirm the robustness and reproducibility of the proposed framework.
Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.
Kalpesh Popat, Divyakant T. Meva· Telecommunications Systems· 0 citations
A Deep Reinforcement Learning (DRL)-based framework for dynamic spectrum access in 6G heterogeneous Cognitive Radio Networks (Het-CRNs), wherein secondary users learn optimal channel selection policies through direct interaction with the radio environment, without requiring explicit statistical channel models is proposed.
Naadir Kamal, R. Kumar· Global Journal of Engineerin...· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations
Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods, indicating that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
N.Prashanth Kumar N.Prashanth Kumar, A. A. A Akshitha, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
A scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs) and demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks.
Valdemar Farré, J. Vega-Sánchez, Alejandro Cama-Pinto et al.· Italian National Conference...· 0 citations
Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless networks due to its ability to intelligently manipulate the propagation environment. In RIS-assisted millimeter-wave multiantenna MIMO communication networks, the joint optimization of RIS phase configuration and resource allocation under heterogeneous user priorities remains challenging. This paper proposes a deep learning-based framework that incorporates user priority weights into both channel estimation and resource allocation through and unsupervised learning. We formulate the joint optimization problem of RIS phase shifts, base station beamforming, and user priority scheduling under α-fairness criteria. A neural network architecture is designed to learn the mapping from channel state information and user weights to optimal resource allocation policies. Simulation results demonstrate that the proposed approach achieves significant performance improvements of 6.5–13.8% in throughput compared to baseline schemes across multiple metrics. The devised framework attains enhanced performance metrics with lower computational burden, which renders it far more expandable than iterative optimization approaches.
Chao-Qun Pei, Gewei Tan· 2026 8th International Confe...· 0 citations
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