Distributionally Robust Chance-Constrained Backscatter Communication-Assisted Computation Offloading in WBANs

Abstract

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.