Covert Communications in MEC-Based Networked ISAC Systems Towards Low-Altitude Economy

TL;DR

This paper designs covert transmission strategies for MEC-based networked ISAC systems in low-altitude economy, optimizing energy, resources, and UAV trajectories to ensure covert operations and efficient sensing and communication.

Contribution

It introduces a novel joint optimization framework for covert MEC-based ISAC systems in LAE, including closed-form DEP expressions and an alternating optimization algorithm.

Findings

The proposed algorithm effectively minimizes energy consumption.

Trade-offs among communication, sensing, and computation are demonstrated.

Simulation results validate the approach's superiority over benchmarks.

Abstract

Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the convert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain UAVs' covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on quality of MEC services, DEP, and radar signal-to-interference-and-noise ratio, and the causality of UAV trajectories. An alternating optimization based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation based algorithm, while the latter is solved via a trust-region based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.