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
The proliferation of heterogeneous radio access technologies in sixth-generation (6G) wireless networks demands a fundamental rethinking of spectrum management strategies. Traditional spectrum sensing approaches, designed for relatively static channel conditions, are inadequate for the dynamic, interference-rich environments that characterize 6G deployments spanning sub-6 GHz, millimetre-wave, and terahertz bands simultaneously. This paper proposes a Deep Reinforcement Learning (DRL)-based framework for dynamic spectrum access in 6G heterogeneous Cognitive Radio Networks (Het-CRNs), wherein secondary users (SUs) learn optimal channel selection policies through direct interaction with the radio environment, without requiring explicit statistical channel models. Specifically, a Double Deep Q-Network (DDQN) architecture is adopted, augmented with a prioritized experience replay mechanism that accelerates policy convergence under non-stationary channel conditions. The proposed agent observes a composite state space encoding instantaneous channel occupancy, signal-to-interference-plus-noise ratio (SINR), primary user (PU) activity patterns, and residual energy levels, and selects actions that jointly optimize spectrum utilization efficiency, interference avoidance, and energy consumption. Simulation experiments conducted over a heterogeneous network topology with four primary users and eight secondary users demonstrate that the proposed DDQN-based scheme achieves a throughput gain of approximately 34% over conventional energy detection-based sensing, reduces interference to primary users by 61%, and attains a detection probability of 0.94 at a false alarm rate of 0.05. These results confirm the practical viability of DRL as a spectrum management backbone for next-generation cognitive radio systems.
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
The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate 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
The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture 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