Journal
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 16, Issue 4, Pages 1754-1767Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2019.2939685
Keywords
Task analysis; Resource management; Computational modeling; Heterogeneous networks; Cloud computing; Wireless communication; Energy consumption; Heterogeneous networks; multi-access edge computing; OFDMA; radio resources allocation; successive convex approximation; task offloading; wireless backhaul
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Funding
- NSERC
- Concordia University
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Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices' energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users' tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.
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