Dynamic Computation Offloading for MIMO Mobile Edge Computing Systems With Energy Harvesting

By providing spatial diversity gain, the incorporation of multiple antennas into mobile edge computing (MEC) systems can improve the transmission performance. Meanwhile, employing energy harvesting (EH) helps enhance the system sustainability. In this paper, we focus on multi-input multi-output (MIMO) MEC systems with EH and studies the computation offloading. The design objective is to minimize the time average of a weighted sum of energy consumption and execution delay, meanwhile stabilizing the battery energy queue. To this end, we formulate the problem as a statistic program and propose a dynamic computation offloading (DCO) algorithm in which the transmitter covariance matrix, CPU-cycle frequencies for local computing, and partial offloading ratio are jointly optimized. Based on Lyapunov optimization, the program is first transformed into a nonconvex per-time slot problem. Then, we solve it by the successive convex approximation (SCA) technique, where a sequence of convex problems are created and solved. Simulation results demonstrate that the proposed algorithm is asymptotically optimal and outperforms several benchmark schemes in terms of both the average system cost and task drop ratio.

Automated summary

By focusing on multi-input multi-output (MIMO) mobile edge computing (MEC) systems with energy harvesting (EH), this paper studies computation offloading. The dynamic computation offloading (DCO) algorithm optimizes the transmitter covariance matrix, CPU-cycle frequencies, and partial offloading ratio to minimize energy consumption and execution delay. Through Lyapunov optimization and successive convex approximation (SCA), the algorithm proves to be asymptotically optimal and outperforms benchmark schemes in system cost and task drop ratio.