Joint Radio and Computation Resource Allocation for NOMA-Enabled MEC in Multi-Cell Networks

Mobile edge computing (MEC) enables the users of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the users. In this paper, we investigate how non-orthogonal multiple access (NOMA) techniques can be exploited to achieve energy-efficient MEC in multi-cell networks. To this end, we first characterize the energy efficiency of the considered system, taking into account the impact of both intra- and inter-cell interference in multi-cell networks. We then jointly optimize the subchannel allocation, power allocation, and the computation resource allocation to maximize the energy efficiency of NOMA-enabled MEC, while simultaneously satisfying the maximum tolerable delay constraints of the users. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in multi-cell networks compared to the existing baselines.

[1]  Yan Zhang,et al.  Mobile Edge Computing: A Survey , 2018, IEEE Internet of Things Journal.

[2]  Yuan Wu,et al.  NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation , 2018, IEEE Transactions on Vehicular Technology.

[3]  Nirwan Ansari,et al.  Edge Computing Aware NOMA for 5G Networks , 2017, IEEE Internet of Things Journal.

[4]  Pingzhi Fan,et al.  Simple Semi-Grant-Free Transmission Strategies Assisted by Non-Orthogonal Multiple Access , 2018, IEEE Transactions on Communications.

[5]  Ronald L. Rivest,et al.  A Knapsack Type Public Key Cryptosystem Based On Arithmetic in Finite Fields , 1984, CRYPTO.

[6]  H. Vincent Poor,et al.  Non-Orthogonal Multiple Access: Common Myths and Critical Questions , 2018, IEEE Wireless Communications.

[7]  Pingzhi Fan,et al.  Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions , 2016, IEEE Transactions on Vehicular Technology.

[8]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[9]  Jamie S. Evans,et al.  Low-Complexity Distributed Algorithms for Spectrum Balancing in Multi-User DSL Networks , 2006, 2006 IEEE International Conference on Communications.

[10]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[11]  Zhu Han,et al.  Green Large-Scale Fog Computing Resource Allocation Using Joint Benders Decomposition, Dinkelbach Algorithm, ADMM, and Branch-and-Bound , 2019, IEEE Internet of Things Journal.

[12]  Feng Wang,et al.  Optimized Multiuser Computation Offloading with Multi-Antenna NOMA , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[13]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[14]  Zhiguo Ding,et al.  Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems , 2017, IEEE Transactions on Communications.

[15]  Zhu Han,et al.  Spectrum Allocation and Power Control for Non-Orthogonal Multiple Access in HetNets , 2017, IEEE Transactions on Wireless Communications.

[16]  Yueming Cai,et al.  Joint Computing Resource, Power, and Channel Allocations for D2D-Assisted and NOMA-Based Mobile Edge Computing , 2019, IEEE Access.