Joint Sensing, Communication, and Computation in UAV-Assisted Systems

This article proposes a joint sensing, communication, and computation (JSCC) framework in unmanned aerial vehicle (UAV)-assisted systems, where multifunctional 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 multiobjective optimization problem (MOOP) that jointly optimizes the transmit beampattern, computation offloading, and UAV trajectory. To achieve the computation–sensing tradeoff region, we first transform the MOOP into a single-objective optimization problem (SOOP) via the
$\epsilon _{1}$
-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 nonconvex 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 the successive convex approximation (SCA) method to handle the remaining nonconvex 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., nonorthogonal multiple access (NOMA) and space-division multiple access (SDMA). Simulation results demonstrate that the proposed algorithm can achieve an improved computation–sensing tradeoff region compared to conventional schemes especially when exploiting NOMA. Moreover, the multifunctional performance can be significantly improved while stringently guaranteeing both radar sensing and computation offloading requirements.