Joint Sensing, Communication and Computation in UAV-Assisted Systems

This paper proposes a joint sensing, communication and computation (JSCC) framework in unmanned aerial vehicle (UAV)-assisted systems, where multi-functional terminal devices (TDs) can perform high-accuracy radar sensing as well as offload computation data to an airborne mobile edge computing (MEC) server over the same frequency band. The key objective of the JSCC framework is to simultaneously minimize the transmitted sensing beampattern matching error whilst maximizing the minimum computation efficiency of TDs. This problem is formulated as a multi-objective optimization problem (MOOP) that jointly optimizes the transmit beampattern, computation offloading, and UAV trajectory. To achieve the computation-sensing trade-off region, we first transform the MOOP into a single-objective optimization problem (SOOP) via the 1-constraint method. To make it more tractable, a generalized Dinkelbach’s and successive convex approximation (GD-SCA) algorithm is proposed. Specifically, GD-SCA transfers the non-convex max-min fractional programming in the resultant SOOP by introducing a general auxiliary polynomial via generalized Dinkelbach’s algorithm. Thereafter, the transmit beampattern, computation offloading, and UAV trajectory optimization are decoupled into two nested subproblems, which can be iteratively solved by invoking successive convex approximation (SCA) method to handle the remaining non-convex components. The proposed GD-SCA can obtain high-quality suboptimal solutions of the original MOOP. We validate the effectiveness of the proposed algorithm by considering two multiple access techniques, i.e., non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA). Simulation results demonstrate that the proposed algorithm can achieve an improved computation-sensing trade-off region compared to conventional schemes especially when exploiting NOMA. Moreover, the multi-functional performance can be significantly improved while stringently guaranteeing both radar sensing and computation offloading requirements.