Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation

TL;DR

This paper tackles maximizing the weighted sum computation rate $WSCR$ in a wireless-powered MEC network with dynamic multi-user collaboration among SDs, ADs, and IDs under OFDMA and harvest-and-offload protocols. It formulates a mixed-integer problem $\mathcal{P}_1$ to jointly optimize collaboration $\boldsymbol{\psi}$, time allocation $\boldsymbol{\alpha}$, and data distribution $\boldsymbol{l}$ under energy causality and minimum data requirements, and proves NP-hardness; it then decomposes the problem into a convex time/data allocation subproblem and a combinatorial collaboration subproblem, solved by an interior-point method (Algorithm 1) and a priority-based search (Algorithm 2). A deep learning-based accelerator (Algorithm 3) is proposed to further reduce runtime while preserving near-optimal performance. Numerical results show the DL-based approach achieves WSCR close to exhaustive search with significant speedups, demonstrating practical viability for real-time decision-making in IIoT-enabled MEC with wireless power transfer.

Abstract

The combination of mobile edge computing (MEC) and radio frequency-based wireless power transfer (WPT) presents a promising technique for providing sustainable energy supply and computing services at the network edge. This study considers a wireless-powered mobile edge computing system that includes a hybrid access point (HAP) equipped with a computing unit and multiple Internet of Things (IoT) devices. In particular, we propose a novel muti-user cooperation scheme to improve computation performance, where collaborative clusters are dynamically formed. Each collaborative cluster comprises a source device (SD) and an auxiliary device (AD), where the SD can partition the computation task into various segments for local processing, offloading to the HAP, and remote execution by the AD with the assistance of the HAP. Specifically, we aims to maximize the weighted sum computation rate (WSCR) of all the IoT devices in the network. This involves jointly optimizing collaboration, time and data allocation among multiple IoT devices and the HAP, while considering the energy causality property and the minimum data processing requirement of each device. Initially, an optimization algorithm based on the interior-point method is designed for time and data allocation. Subsequently, a priority-based iterative algorithm is developed to search for a near-optimal solution to the multi-user collaboration scheme. Finally, a deep learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.

Figures (14)

• Figure 1: The wireless-powered MEC network with multi-user cooperation.
• Figure 2: The data allocation for three types of devices. (The SD and AD form a collaborative cluster.)
• Figure 3: The harvest-and-offloading protocol with UC and OFDMA.
• Figure 4: The two-level optimization structure for sloving $\mathcal{P}_2$.
• Figure 5: Algorithm 1: Interior-Point Method Based Algorithm for Solving Problem $\mathcal{P}_5$.

Theorems & Definitions (10)

• Lemma 1
• proof
• Lemma 2
• proof
• Lemma 3
• proof
• Lemma 4
• proof
• Lemma 5
• proof