[Paper Review] Wireless Powered User Cooperative Computation in Mobile Edge Computing Systems

Dixiao Wu, Feng Wang+2 more|arXiv (Cornell University)|Sep 5, 2018

Energy Harvesting in Wireless Networks4 citations

TL;DR

This paper proposes a wireless powered user cooperative computation framework in mobile edge computing, where idle devices harvest RF energy from a dedicated transmitter and assist active users in offloading and executing computation tasks. By jointly optimizing energy beamforming, communication, and computation resources under energy neutrality constraints, the scheme achieves a significant increase in computation rate, with numerical results showing substantial gains over non-cooperative benchmarks.

ABSTRACT

This paper studies a wireless powered mobile edge computing (MEC) system, where a dedicated energy transmitter (ET) uses the radio-frequency (RF) signal enabled wireless power transfer (WPT) to charge wireless devices for sustainable computation. In such a system, we present a new user cooperation approach to improve the computation performance of active devices, in which surrounding idle devices are enabled as helpers to use their opportunistically harvested wireless energy from the ET to help remotely execute active users' computation tasks. In particular, we consider a basic scenario with one user (with computation tasks to execute) and multiple helpers, in which the user can partition the computation tasks into various parts for local execution and computation offloading to helpers, respectively. Both the user and helpers are subject to the so-called energy neutrality constraints, such that their energy consumption does not exceed the respective energy harvested from the ET. Under this setup and considering a frequency division multiple access (FDMA) based computation offloading protocol, we maximize the computation rate (i.e., the number of computation bits over a particular time block) of the user, by jointly optimizing the transmit energy beamforming at the ET, as well as the communication and computation resource allocations at both the user and helpers. By leveraging the Lagrange duality method, we present the optimal solution to this problem in a semi-closed form. Numerical results show that the proposed wireless powered user cooperative computation design significantly improves the computation rate at the user, as compared to conventional schemes without such cooperation.

Motivation & Objective

• To address the challenge of sustainable computation in energy-constrained mobile edge computing (MEC) systems with limited energy availability.
• To exploit idle devices with unused computation resources and opportunistically harvested RF energy to assist active users in task offloading.
• To maximize the computation rate of active users under energy neutrality constraints, ensuring neither users nor helpers exceed their harvested energy.
• To jointly optimize energy beamforming at the transmitter, and communication and computation resource allocation at users and helpers.

Proposed method

• Formulates a frequency division multiple access (FDMA)-based computation offloading protocol to enable simultaneous communication and computation among users and helpers.
• Introduces a joint optimization problem for energy beamforming, time allocation, and computation offloading to maximize computation rate under energy neutrality constraints.
• Applies Lagrange duality to derive the optimal solution in semi-closed form, leveraging KKT conditions and the Lambert W function for analytical tractability.
• Solves the dual problem by deriving closed-form expressions for optimal computation rates and time allocations using the inverse of the function $ x e^x $, represented via the Lambert W function.
• Transforms the primal problem into an equivalent linear program (LP) after determining optimal rates, enabling efficient computation of the optimal task partitioning.
• Uses the Karush-Kuhn-Tucker (KKT) conditions to characterize the optimal primal-dual variables, including energy and time allocation across computation phases.

Experimental results

Research questions

• RQ1 Can idle devices with harvested RF energy be effectively leveraged to assist active users in computation offloading within a wireless powered MEC system?
• RQ2 How should energy beamforming, time allocation, and computation offloading be jointly optimized to maximize the computation rate under energy neutrality constraints?
• RQ3 What is the optimal structure of task partitioning between local execution and cooperative offloading to the helpers?
• RQ4 How does the proposed cooperative scheme compare to non-cooperative benchmarks in terms of computation rate and energy efficiency?

Key findings

• The proposed wireless powered user cooperative computation scheme significantly improves the computation rate of active users compared to conventional non-cooperative schemes.
• The optimal solution is derived in semi-closed form using Lagrange duality and the Lambert W function, enabling efficient computation of beamforming and resource allocation.
• The optimal computation rate for each user is achieved by balancing local computation, offloading to helpers, and energy harvesting, with explicit expressions derived for time and power allocation.
• The optimal task partitioning depends on the user’s and helpers’ channel conditions, computation capabilities, and energy harvesting efficiency, with the solution favoring offloading when beneficial.
• Numerical results confirm that the cooperative scheme outperforms non-cooperative benchmarks, especially in scenarios with high idle device availability and strong channel conditions.