2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 20434-20446· 0 citations· 39 references
Abstract
High-performance networking is essential for Unmanned Aerial Vehicle (UAV) swarms to accomplish complex, coordinated missions. A central challenge in UAV swarm networking is managing concurrent multi-hop transmissions, where traditional protocols often struggle due to routing path conflicts and co-channel interference. To address this, we propose a novel multi-agent reinforcement learning (MARL)-based cross-layer transmission framework that maximizes system throughput by jointly optimizing network-layer routing, link-layer resource allocation, and UAV trajectories. We decouple this complex joint optimization problem and solve it with a routing-prioritized iterative scheme. For the routing sub-problem, an MARL approach is designed for agents to collaboratively plan concurrent routing paths. The non-convex resource allocation and trajectory sub-problems are handled using successive convex approximation (SCA). Experimental results demonstrate that our proposed framework significantly outperforms existing benchmarks in system throughput, end-to-end delay, and packet delivery ratio.
Reliable link maintenance is currently a critical bottleneck for unmanned aerial vehicle (UAV) swarm communications in complex electromagnetic environments where UAVs encounter both external malicious jamming and internal interference. Most recent studies have treated trajectory design and resource scheduling as decoupled problems or employed standard deep reinforcement learning methods to handle static spectral scenarios. However, these approaches lead to frequent link breakages and slow convergence when dealing with dynamic topologies and spatiotemporal interference. To tackle this challenge, we proposes a joint spatial-spectral adaptive coordination (JSSAC) framework and a deep recurrent attentionbased Q-network (DARQN) approach, utilizing a multi-head attention mechanism to intelligently aggregate heterogeneous neighbor features, thereby enhancing the swarm's adaptability to dynamic network topology. Moreover, considering that the spatial distribution of drones fundamentally determines the upper bound of the signal quality, we designed a communicationaware potential field mechanism that incorporates real-time signal-to-interference-plus-noise ratio feedback. Simulation results demonstrate that compared to DQN and DRQN algorithms, the proposed algorithm achieves transmission success rates of over 92%, representing improvements of 17% and 8% respectively, while also accelerating convergence speed.
Miao Liu, Nan Qi, Hua Jiang et al.· International Mediterranean...· 0 citations
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 systems· 0 citations
This paper proposes a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices and UAVs act as heterogeneous agents and utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers.
Ming Cheng, Canlin Zhu, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
Unmanned aerial vehicles play an increasingly important role in the low-altitude domain by collecting and transmitting aerial images. However, the inter-dependency between UAV motion and communication strategies has been largely overlooked. To address this gap, we propose a UAV motion-aware image capturing and communication (MICC) system that dynamically optimizes data offloading by jointly considering scenario variability and communication resource allocation. Specifically, we formulate an MICC optimization problem to maximize transmission accuracy and efficiency by adaptively controlling down-sampling ratios, compression ratios, and transmit power. Considering its non-convex nature, we first develop a geometric programming based algorithm (GP-MICC) to obtain high-fidelity solutions. Recognizing its high computational cost, which hinders real-time deployment, we further propose a fast learning-based optimization algorithm (FLO-MICC). Extensive experiments demonstrate that GP-MICC achieves excellent transmission performance, while FLO-MICC reduces computational time by over 12x with minimal performance loss, making it suitable for dynamic UAV scenarios.
This study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs that leverages game-theoretic incentives and Q-learning to adaptively select strategies.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
Simulation results confirm the effectiveness of distributed optimization and DRL-based coordination for scalable, resilient, and adaptable UAV deployment in disaster response and other mission-critical scenarios.
A. Abdellatif, Amr E. Aboeleneen, Mohamed M. Abdallah et al.· IEEE Open Journal of the Com...· 0 citations