Paper Info
Title
Distributionally Robust Chance-Constrained Optimization For Communication And Offloading In Wbans
Abstract
In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computation offloading scheme in wireless body area networks (WBANs), where each sensor firstly receives radio frequency (RE) energy and then offloads body physiological computation tasks via low-power BackCom to the access point (AP) for edge computing. Specifically, only rough first and second-order moment statistics are obtained for the estimation errors of CSI. Based on all the possible distributions of CSI errors, we aim to minimize the end-to-end system latency by jointly optimizing the power of the signal transmitted by the AP and the power reflection coefficient with energy chance restrictions and throughput requirement constraints. In order to solve the proposed non-convex chance-constrained optimization problem, we approximate chance constraints by the conditional value-at-risk (CVaR), and apply an efficient block coordinate descent (BCD) algorithm to solve it. Simulation results are provided to corroborate that the proposed method outperforms other methods for the non-Gaussian mismatch.
Year
DOI
Venue
2020
10.1109/GLOBECOM42002.2020.9322069
2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Keywords
DocType
ISSN
Wireless Body Area Networks (WBANs), Mobile Computation Offloading, Backscatter Communication (BackCom), Conditional value-at-risk (CVaR) method, Distributionally robust chance-constrained optimization
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Zhuang Ling103.04
Fengye Hu27120.07
Yu Zhang300.68
Feifei Gao43093212.03
Zhu Han511215760.71