TY - JOUR
T1 - Multi-Tier Task Offloading with Intelligent Reflecting Surface and Massive MIMO Relay
AU - Wang, Kunlun
AU - Zhou, Yong
AU - Wu, Qingqing
AU - Chen, Wen
AU - Yang, Yang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.
AB - This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.
UR - https://www.scopus.com/pages/publications/85184385118
U2 - 10.1109/GLOBECOM46510.2021.9685898
DO - 10.1109/GLOBECOM46510.2021.9685898
M3 - 会议文章
AN - SCOPUS:85184385118
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
Y2 - 7 December 2021 through 11 December 2021
ER -
