Trajectory Design and Resource Allocation for Multi-UAV-Assisted Sensing, Communication, and Edge Computing Integration

TL;DR

This paper introduces a multi-UAV system integrating sensing, communication, and edge computing, optimizing energy efficiency through joint trajectory, resource, and task management using multi-agent reinforcement learning.

Contribution

It proposes a novel multi-agent MDP framework and a multi-agent PPO algorithm with attention for efficient resource allocation in UAV-assisted networks.

Findings

Significant energy reduction achieved in simulations.

Outperforms baseline methods in resource utilization.

Faster convergence demonstrated in experiments.

Abstract

In this paper, we propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and edge computing services to mobile users (MUs) in proximity, alongside their target sensing capabilities by using multi-input multi-output arrays. For the purpose of enhance the computation efficiency, we consider task compression, where each MU can partially compress their offloaded data prior to transmission to trim its size. The objective is to minimize the weighted energy consumption by jointly optimizing the transmit beamforming, the UAVs' trajectories, the compression and offloading partition, the computation resource allocation, while fulfilling the causal-effect correlation between communication and computation as well as adhering to the constraints on sensing quality. To tackle it, we first reformulate the original problem as a multi-agent Markov decision process (MDP), which involves heterogeneous agents to decompose the large state spaces and action spaces of MDP. Then, we propose a multi-agent proximal policy optimization algorithm with attention mechanism to handle the decision-making problem. Simulation results validate the significant effectiveness of the proposed method in reducing energy consumption. Moreover, it demonstrates superior performance compared to the baselines in relation to resource utilization and convergence speed.