Abstract:
In the ever-evolving realm of wireless communications, the integration of intelligent reflecting surface (IRS) and mobile edge computing for task offloading has ignited e...Show MoreMetadata
Abstract:
In the ever-evolving realm of wireless communications, the integration of intelligent reflecting surface (IRS) and mobile edge computing for task offloading has ignited extensive curiosity. However, previous research primarily concentrated on task offloading assuming that a user equipment (UE) possesses the system channel information and the computing resources of base stations (BSs). Acknowledging that UEs typically lack access to such system information, and in pursuit of equilibrium in computing loads between UEs and BSs, we present a bilateral online task allocation approach grounded in partial offloading to minimize task completion latency. Specifically, addressing uncertainties in channel information and available computing resources at BSs, we employ the online ridge regression method on the UE side to continuously adjust the task allocation proportion for offloading to BSs. On the IRS side, we formulate the BS selection as a multi-armed bandit problem, proposing an online learning algorithm based on Thompson sampling to determine the set of BSs for edge computing while managing the task allocation among the selected BSs. Simulation results demonstrate the superior performance of our algorithm.
Published in: IEEE Wireless Communications Letters ( Volume: 13, Issue: 6, June 2024)