In low-altitude economy (LAE), the deployment of unmanned aerial vehicles (UAVs) provides substantial convenience and enhances operational efficiency. This letter investigates a joint resource and trajectory optimization problem in a UAV-enabled integrated sensing, computation, and communication (ISCC) network where the UAV senses and processes the status information from sensing targets (STs), and then sends the computed results to the data collection center (DC). Aiming to maximize the sensing data volume while minimizing the age of information (AoI), we jointly optimize the UAV’s sensing schedule, number of sensing trials, time allocation, transmit power, CPU frequency, and trajectory. The problem is formulated as a Markov decision process (MDP), and a hybrid deep reinforcement learning (DRL) framework is proposed to derive optimal policies. Specifically, we adopt the twin delayed deep deterministic policy gradient (TD3) framework and enhance it with a hybrid-baseline prioritized experience replay (PER) mechanism, denoted as HPTD3. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes.
Unmanned Aerial Vehicles (UAVs) are promising relay platforms due to their flexible deployment and high probability of line-of-sight (LoS) connectivity. This paper compares three deep reinforcement learning (DRL) algorithms-Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Recurrent PPO with LSTM memory-...
Aniket Subbanwar, Ojas Joshi, Amit Agarwal· International Conference on...· 0 citations
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
Unmanned Aerial Vehicles (UAVs) have gained widespread attention in diverse applications like military, medical, aerial surveillance and many more. Presently, the problem of limited bandwidth and geographic factors has raised the need for effective and timely data transfer. Training UAVs with reinforcement learning-bas...
Divyanshu Bhardwaj, Angel Kanjiya, N. Jadav et al.· 2026 IEEE International Work...· 0 citations
This paper proposes AERIS, an offline policy improvement framework for multi-UAV ISAC that learns from fixed flight logs under centralized training and decentralized execution and designs STAR-CRDT, an offline multi-agent RL algorithm that performs support-aware local action rectification and distills only trusted impr...
Ziyuan Wang, Yi-Fan Sui, Wei Wei et al.· 0 citations
A UAV motion-aware image capturing and communication system that dynamically optimizes data offloading by jointly considering scenario variability and communication resource allocation and a fast learning-based optimization algorithm (FLO-MICC).
A service-driven regional partitioning scheme is proposed to support traffic-aware UAV communication, and an adaptive handshaking mechanism is introduced to improve cooperative sensing accuracy by mitigating residual inter-region phase errors with controlled synchronization overhead.
Linghui Miao, Shijian Gao· arXiv.org· 0 citations
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