Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 13 references
Abstract
This study proposes a novel Deep Reinforcement Learning (DRL)-based resource allocation architecture that dynamically mitigates physical layer impairments in Long Range (LoRa) communication networks established with Unmanned Aerial Vehicles (UAVs) operating at tactical speeds. Traditional Adaptive Data Rate (ADR) algorithms used in LoRaWAN networks misinterpret the Doppler shift under high mobility as path loss, leading to an unwarranted increase in the spreading factor and subsequent communication link failures. In this work, a cross-layer Deep Q-Network (DQN) agent is designed to incorporate UAV velocity into the state space, autonomously selecting the optimal spreading factor and transmission power by predicting frequency shifts at the physical layer. Simulations conducted in a realistic Rayleigh fading channel model demonstrate that the proposed method increases the Packet Delivery Ratio (PDR) to over 85% at high speeds, significantly outperforming conventional algorithms.
Results show that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks, particularly in dense deployments.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert Syst. J. Knowl. Eng.· 0 citations
This paper studies the fast and high-performance FA reconfiguration for low-altitude FA networks with multi-agent reinforcement learning (MARL) and presents an electromagnetic digital twin (EM-DT)-assisted MARL framework to fill the sim-to-real gap.
Tong Zhang, Yanan Su, Shuai Wang et al.· 0 citations
Intelligent vehicular networks require reliable high-capacity links in dynamic non-stationary environments. Conventional geometry-based stochastic models (GBSMs) struggle to accurately capture the complex propagation effects in aerial-assisted links, particularly those affected by unmanned aerial vehicle (UAV) attitude fluctuations. In this paper, we propose a three-dimensional (3D) artificial intelligence (AI)-enhanced channel model for aerial double reconfigurable intelligent surface (RIS)-assisted vehicle-to-vehicle (V2V) communications. The proposed model integrates UAV mobility, random attitude fluctuations, and double-RIS configurations. We derive the complex channel impulse response (CIR), spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and frequency correlation function (FCF). A lightweight multilayer perceptron (MLP) neural network is embedded to compensate for RIS perturbation errors induced by UAV dynamics. Furthermore, the twin delayed deep deterministic policy gradient (TD3) method is employed to jointly optimize UAV trajectories and RIS parameters. Simulation results demonstrate that the proposed model effectively captures spatial-temporal-frequency non-stationary characteristics, achieving superior channel correlation and communication capacity compared to conventional single-RIS models and other deep reinforcement learning-based algorithms.
Ying Zhao, Zhimin Chen, Ming Li et al.· IEEE Transactions on Cogniti...· 0 citations
A sensing information-assisted superimposed pilot channel estimation method is proposed in UAV orthogonal frequency division multiplexing systems that achieves rapid convergence and optimal symbol detection performance at a low pilot power ratio, effectively improving spectral efficiency in dynamic UAV communication scenarios.
Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
Yi Yang, Qianqian Zhang, Huaxia Wang· arXiv.org· 0 citations
Low Power Wide Area Networks (LPWANs), particularly LoRaWAN, are increasingly being deployed in mobile Internet of Things (IoT) applications. In these applications, adaptive data rate (ADR) control is essential for maintaining reliable and energy-efficient communication under dynamic wireless conditions. However, ADR performance remains constrained when channel state information (CSI) is imperfect. Although reinforcement learning (RL)-based ADR methods have demonstrated notable improvement over conventional approaches, their effectiveness under imperfect CSI deployment scenarios remains insufficiently explored. In this paper, we investigate the robustness of RL-based ADR approaches with imperfect CSI for mobile LoRaWAN networks using a mobility-aware simulation framework. Standard ADR, heuristic ADR, Deep Q-Network (DQN)-based ADR, and Proximal Policy Optimisation (PPO)-based ADR are evaluated across varying mobility patterns, network layouts, and channel dynamics. Key performance metrics include packet delivery ratio, latency, runtime efficiency, and retention of baseline capability are evaluated. The Results show that PPO achieves the best performance under imperfect CSI conditions, attaining a mean packet delivery ratio of 0.9954, outperforming standard ADR, heuristic ADR, and DQN by 29.49%, 19.12%, and 1.55%, respectively. In addition, PPO reduced latency by 53.12% and runtime by 81.94% compare to DQN while preserving over 99% of baseline communication reliability under scenario variation. These findings demonstrate that policy-gradient learning provides better transferability, robustness, and deployment readiness compared with conventional and value-based ADR approaches. The study highlights the importance of evaluating intelligent wireless controllers not only for optimisation performance, but also for cross-scenario generalisation before real-world deployment in mobile LPWAN systems.
Unknown authors· FUDMA Journal of Sciences· 0 citations
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