Preprint Article Version 1 This version is not peer-reviewed

User-Cooperative Dynamic Resource Allocation for Backscatter-Aided Wireless-Powered MEC Network

Version 1 : Received: 15 August 2024 / Approved: 16 August 2024 / Online: 21 August 2024 (08:34:46 CEST)

How to cite: He, H.; Zhou, C.; Huang, F.; Shen, H.; Yang, Y.; Liang, S. User-Cooperative Dynamic Resource Allocation for Backscatter-Aided Wireless-Powered MEC Network. Preprints 2024, 2024081532. https://doi.org/10.20944/preprints202408.1532.v1 He, H.; Zhou, C.; Huang, F.; Shen, H.; Yang, Y.; Liang, S. User-Cooperative Dynamic Resource Allocation for Backscatter-Aided Wireless-Powered MEC Network. Preprints 2024, 2024081532. https://doi.org/10.20944/preprints202408.1532.v1

Abstract

Backscatter communication, which transmits information by passively reflecting radio 1 frequency (RF) signals, has become a focal point of interest due to its potential to significantly enhance 2 the energy efficiency of Wireless Power Mobile Edge Computing (WPMEC) networks and extend the 3 operational lifespan of terminal devices. However, there is little research on the integration of user 4 cooperation in WPMEC scenarios within volatile network environments. In this paper, we propose 5 a dynamic task offloading scheme for a Backscatter-assisted WPMEC system, which involves two 6 mobile devices (MDs) and a Hybrid Access Point (HAP) with user cooperation. We formulate the 7 energy efficiency (EE) maximization problem as a stochastic programming problem, considering 8 the randomness of task arrivals and time-varying wireless channels. By leveraging Dinkelbach’s 9 method and stochastic network optimization technique, we transform the problem into a series of 10 deterministic sub-problems for each time slot, and convert the non-convex sub-problem into convex 11 ones. We propose a low-complex EE maximization algorithm to solve the convex problems efficiently. 12 Extensive simulations are conducted to validate the performance of our algorithm under various 13 system parameter settings. Experiment results demonstrate that our algorithm not only outperforms 14 the benchmark algorithms by approximately 23%, but also stabilize all queues within the MEC 15 system.

Keywords

Mobile edge computing (MEC); Wireless Power Transfer (WPT); User Cooperation (UC); 17 backscatter communication (Backcom); Lyapunov optimization

Subject

Computer Science and Mathematics, Computer Networks and Communications

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