Towards Optimal Configuration in MEC Neural Networks: Deep Learning-Based Optimal Resource Allocation

Nowadays, the application of data caching in mobile edge computing networks is exponentially increasing as a high-speed data storage layer using deep learning (DL) approaches. This paper presents an DL-based resource allocation approach to find the optimal topology for cache-enabled backhaul networks. In the practical scenarios, a numerous of radial configurations of test systems have been applied for training stages. This paper also applied the continuation power flow analysis to achieve the maximum load limit in which the power of macro base stations with the caches of different sizes is provided through either smart grid network or renewable power systems. To increase the power efficiency index of this approach the power sharing capability was enabled among different layer of network components through smart grids. In order to obtain the optimal solution, the DL-based mathematical problem is reformulated into a neural weighting model considering convergence conditions and Lyapunov stability of the mobile-edge-computing under Karush–Kuhn–Tucker optimality constraints. The mathematical analysis and simulation results demonstrate that the performance of the proposed algorithm is better than other energy efficiency algorithms. The proposed approach can effectively increase the total system throughput and network’s utility in addition to guarantee user fairness index.