4.7 Article

On-Request Wireless Charging and Partial Computation Offloading In Multi-Access Edge Computing Systems

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 10, Pages 6665-6679

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3075920

Keywords

Inductive charging; Wireless communication; Massive MIMO; Antenna arrays; Edge computing; Wireless sensor networks; Wireless power transfer; Edge computing; wireless charging; energy efficient network; partial data offloading; optimization

Funding

  1. National Science Foundation under ECCS Grant [1808912]
  2. Directorate For Engineering
  3. Div Of Electrical, Commun & Cyber Sys [1808912] Funding Source: National Science Foundation

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The combination of wireless charging and computation offloading in edge networks provides a promising solution for power-intensive and computation-heavy applications on user devices. A novel two-stage algorithm is proposed to minimize energy consumption for computation offloading and maximize energy received from wireless charging, optimizing overall performance within power and latency constraints. The results show that optimal energy beamforming outperforms other schemes and that partial offloading leads to lower energy consumption and better wireless charging performance.
Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user-devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point (AP), each equipped with a massive MIMO antenna array, supporting multiple users requesting data computation and wireless charging. The goal is to minimize the energy consumption for computation offloading and maximize the received energy at the user from wireless charging. The proposed solution is a novel two-stage algorithm employing a nested descent algorithm, primal-dual subgradient and linear programming techniques to perform data partitioning and time allocation for computation offloading and design the optimal energy beamforming for wireless charging, all within MEC-AP transmit power and latency constraints. Algorithm results show that optimal energy beamforming significantly outperforms other schemes such as isotropic or directed charging without beam power allocation. Compared to binary offloading, data partition in partial offloading leads to lower energy consumption and more charging time, resulting in better wireless charging performance. The charged energy over an extended period of multiple time-slots both with and without computation offloading can be substantial. Wireless charging from MEC-AP thus offers a viable untethered approach for supplying energy to user-devices.

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