TY - GEN
T1 - RIS-assisted Task Offloading for Wireless Dead Zone to Minimize Delay in Edge Computing
AU - Mukherjee, Mithun
AU - Kumar, Vikas
AU - Kumar, Suman
AU - Mavromoustakis, C. X.
AU - Zhang, Qi
AU - Guo, Mian
N1 - Funding Information:
This work is supported in part by the Nanjing University of Information Science and Technology Start-up Fund Grant 1521632201012, the National Natural Science Foundation of China 61901128, and the European Union’s Horizon 2020 research and innovation programme with title Smart and Health Ageing through People Engaging in supporting Systems (SHAPES) project under grant agreement No 857159. Mithun Mukherjee is the corresponding author.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - End-users under poor wireless network coverage generally suffer from underutilization of bandwidth. This adversely affects the overall performance of task offloading to the edge server. In this work, we study a Reconfigurable Intelligent Surface (RIS)-assisted wireless network that enables end-user's devices under weak signal reception areas to enhance their offloading opportunities for delay minimization. It becomes a challenging task to allocate uploading bandwidth allocation for the offloaded tasks from end-user devices under different signal coverage areas. We formulate the optimization problem of bandwidth allocation for the offloaded tasks in the edge server and the offloading decisions as a quadratically constrained quadratic problem. We exploit a semi-definite relaxation (SDR) method to solve the problem. Moreover, during optimization, we minimize the adverse impact of bandwidth allocation for poor end-users on good end-users performance. From extensive simulation results, we show remarkably elevated improvement in delay reduction with RIS assistance compared to other baselines, increasing the number and ratio of end-user devices under good and poor signal reception areas.
AB - End-users under poor wireless network coverage generally suffer from underutilization of bandwidth. This adversely affects the overall performance of task offloading to the edge server. In this work, we study a Reconfigurable Intelligent Surface (RIS)-assisted wireless network that enables end-user's devices under weak signal reception areas to enhance their offloading opportunities for delay minimization. It becomes a challenging task to allocate uploading bandwidth allocation for the offloaded tasks from end-user devices under different signal coverage areas. We formulate the optimization problem of bandwidth allocation for the offloaded tasks in the edge server and the offloading decisions as a quadratically constrained quadratic problem. We exploit a semi-definite relaxation (SDR) method to solve the problem. Moreover, during optimization, we minimize the adverse impact of bandwidth allocation for poor end-users on good end-users performance. From extensive simulation results, we show remarkably elevated improvement in delay reduction with RIS assistance compared to other baselines, increasing the number and ratio of end-user devices under good and poor signal reception areas.
KW - deadline-aware task offloading
KW - edge computing
KW - Mobile edge computing
KW - offloading
KW - reconfigurable intelligent surfaces
UR - http://www.scopus.com/inward/record.url?scp=85146937993&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM48099.2022.10001478
DO - 10.1109/GLOBECOM48099.2022.10001478
M3 - Conference contribution
AN - SCOPUS:85146937993
T3 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
SP - 2554
EP - 2559
BT - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Global Communications Conference, GLOBECOM 2022
Y2 - 4 December 2022 through 8 December 2022
ER -