Computing Efficiency Optimization for UAV-Enabled Integrated Sensing, Computing, and Communication: A Memory-Based Deep Reinforcement Learning Approach

doi:10.3390/drones10030180

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Computing Efficiency Optimization for UAV-Enabled Integrated Sensing, Computing, and Communication: A Memory-Based Deep Reinforcement Learning Approach

2026-03-08

Honghao Qi *

Muqing Wu

DOI：10.3390/drones10030180

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Abstract

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Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with the computational assistance of ground access points (APs). Given the limited onboard energy, ensuring energy-efficient operation of UAVs is crucial to support the long-term sustainability of network performance. In this paper, we define computing efficiency as the ratio between the total number of successfully processed computational bits and the overall UAV energy consumption, under the constraint of a required sensing threshold. To maximize this performance metric, this paper jointly optimizes the beamforming vector, the CPU frequency, and the trajectory of the UAV. This optimization problem is modeled as a Markov decision process (MDP) and solved using a deep reinforcement learning (DRL) approach based on a memory mechanism. Specifically, a long short-term memory (LSTM) and twin delayed deep deterministic policy gradient (TD3)-based trajectory design and resource allocation (LTTDRA) algorithm is proposed. LSTM units are integrated into the actor and critic to effectively capture the temporal correlations in dynamic environments, thereby enhancing policy stability and accelerating algorithm convergence. The reward function is meticulously designed to alleviate sparse-penalty effects and learn high-performance strategies in complex environments with multiple constraints. Extensive simulations are conducted under various settings and network scenarios, and the results consistently indicate that the proposed approach substantially outperforms the baseline schemes.

Keywords:

UAV

ISCC

trajectory design

resource allocation

computing efficiency

DRL

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