Energy Minimization For Fog Computing-Enabled Hierarchical Networks With Dynamic Tdd

This paper investigates the resource allocation in the fog computing-enabled hierarchical networks, where multiple small cells are connected in a macro cell, and the mobile users (MUs) in each small cell can offloading their computation tasks to the fog server (FS) deployed on the macro base station (MBS). Dynamic time division duplex (D-TDD) settings are employed for the uplink (UL) and downlink (DL) transmissions to improve the spectrum efficiency. For such a system, we formulate an optimization problem to minimize the average energy consumption of the MUs by jointly optimizing the offloading ratio and subcarrier allocation in the presence of multiple system constraints, including the latency constraint and the subcarrier resource constraint. Since this problem is a mixed integer programming (MIP) problem and no known solution approach, we first transform it into two sub-problems, i.e., a linear programming problem (for solving the offloading decisions) and a many-to-one matching problem (for solving the subcarrier allocation). Then, a joint optimization algorithm is designed. The simulation results show that our proposed strategy is significant better than other benchmark schemes. Besides, it is also observed that with the increment of the proportion of the UL transmission, the average energy consumption of MUs decreases.