TY - GEN
T1 - Joint Cache Placement and NOMA-Based Task Offloading for Multi-User Mobile Edge Computing
AU - Dai, Hanzhe
AU - Wen, Haifeng
AU - Xing, Hong
AU - Ding, Zhiguo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - One of the emerging computing paradigms, mobile edge computing (MEC, also known as fog computing), has been developed to reduce both energy consumption and computation latency for computation-extensive IoT applications. Further, thanks to advantages brought by non-orthogonal multiple access (NOMA) in increasing the capacity of multiple-access channels (MAC), and by service caching in alleviating the burden of responding to repeated computation requests, this paper considers the joint design of communication, computation, and caching for multi-user MEC systems. Aiming for minimizing the weighted-sum energy consumption of communication and computation, given a finite set of computation services, we jointly optimize the NOMA transmission, the computation resources, and the Boolean-variable modeled cache placement, subject to the computation and caching capacity of the edge server as well as the computation latency constraints. To solve the formulated mixed-integer non-convex problem, first, given the cache placement, we solve the non-differentiable convex problem by Lagrangian dual method leveraging a semi-closed form of NOMA transmission power, followed by a one-dimension search for the optimal common task offloading time. Next, an optimal branch-and-bound (BnB) based caching strategy is proposed. Meanwhile, we also provide a heuristic suboptimal cache placement design to reduce computational complexity. Finally, numerical results show the striking performance of the proposed joint optimization of NOMA-based task offloading and service caching compared to the greedy cache placement and other benchmarks without either NOMA-based task offloading or service caching.
AB - One of the emerging computing paradigms, mobile edge computing (MEC, also known as fog computing), has been developed to reduce both energy consumption and computation latency for computation-extensive IoT applications. Further, thanks to advantages brought by non-orthogonal multiple access (NOMA) in increasing the capacity of multiple-access channels (MAC), and by service caching in alleviating the burden of responding to repeated computation requests, this paper considers the joint design of communication, computation, and caching for multi-user MEC systems. Aiming for minimizing the weighted-sum energy consumption of communication and computation, given a finite set of computation services, we jointly optimize the NOMA transmission, the computation resources, and the Boolean-variable modeled cache placement, subject to the computation and caching capacity of the edge server as well as the computation latency constraints. To solve the formulated mixed-integer non-convex problem, first, given the cache placement, we solve the non-differentiable convex problem by Lagrangian dual method leveraging a semi-closed form of NOMA transmission power, followed by a one-dimension search for the optimal common task offloading time. Next, an optimal branch-and-bound (BnB) based caching strategy is proposed. Meanwhile, we also provide a heuristic suboptimal cache placement design to reduce computational complexity. Finally, numerical results show the striking performance of the proposed joint optimization of NOMA-based task offloading and service caching compared to the greedy cache placement and other benchmarks without either NOMA-based task offloading or service caching.
KW - Mobile edge computing
KW - non-orthogonal multiple access
KW - resource allocation
KW - service caching
UR - http://www.scopus.com/inward/record.url?scp=85169818055&partnerID=8YFLogxK
U2 - 10.1109/VTC2023-Spring57618.2023.10201094
DO - 10.1109/VTC2023-Spring57618.2023.10201094
M3 - Conference contribution
AN - SCOPUS:85169818055
T3 - IEEE Vehicular Technology Conference
BT - 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Y2 - 20 June 2023 through 23 June 2023
ER -