TY - GEN
T1 - Optimal task partition and power allocation for mobile edge computing with NOMA
AU - Fang, Fang
AU - Xu, Yanqing
AU - DIng, Zhiguo
AU - Shen, Chao
AU - Peng, Mugen
AU - Karagiannidis, George K.
N1 - Funding Information:
Y. Xu and C. Shen are with the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China (e-mail: yanqing [email protected]). Chao Shen is supported by the Beijing NSF (L172020), NSFC (61871027 and U1834210), State Key Laboratory of Rail Traffic Control and Safety (RCS2019ZZ002), and Major Projects of the Beijing Municipal Science and Technology Commission (Z181100003218010).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Mobile edge computing (MEC) can provide considerable computing capabilities for Internet of Things (IoT) devices, especially for applications with latency sensitive tasks. By applying non-orthogonal multiple access (NOMA) in MEC, multiple users can offload their tasks simultaneously on the same frequency band. In this paper, the minimization problem of task completion time is investigated for the NOMA enabled multi-user MEC networks. We adopt emph{partial offloading}, in which each user's task can be partitioned, while the formulated problem is quasi-convex. Thus a bisection search (BSS) algorithm is proposed to achieve the minimum task completion time for the multi- user case. To reduce the complexity and evaluate the optimality of the BSS algorithm, we further derive closed- form expressions for the optimal task partition ratio and offloading power for a two-user NOMA-MEC network. Simulations demonstrate the convergence and optimality of the proposed BSS algorithm and the effectiveness of the optimal approach.
AB - Mobile edge computing (MEC) can provide considerable computing capabilities for Internet of Things (IoT) devices, especially for applications with latency sensitive tasks. By applying non-orthogonal multiple access (NOMA) in MEC, multiple users can offload their tasks simultaneously on the same frequency band. In this paper, the minimization problem of task completion time is investigated for the NOMA enabled multi-user MEC networks. We adopt emph{partial offloading}, in which each user's task can be partitioned, while the formulated problem is quasi-convex. Thus a bisection search (BSS) algorithm is proposed to achieve the minimum task completion time for the multi- user case. To reduce the complexity and evaluate the optimality of the BSS algorithm, we further derive closed- form expressions for the optimal task partition ratio and offloading power for a two-user NOMA-MEC network. Simulations demonstrate the convergence and optimality of the proposed BSS algorithm and the effectiveness of the optimal approach.
KW - Delay minimization
KW - MEC
KW - NOMA
KW - Offload
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85081986310&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9013893
DO - 10.1109/GLOBECOM38437.2019.9013893
M3 - Conference contribution
AN - SCOPUS:85081986310
T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -