  Nowadays, data caching is being used as a high-speed data storage layer in
mobile edge computing networks employing flow control methodologies at an
exponential rate. This study shows how to discover the best architecture for
backhaul networks with caching capability using a distributed offloading
technique. This article used a continuous power flow analysis to achieve the
optimum load constraints, wherein the power of macro base stations with various
caching capacities is supplied by either an intelligent grid network or
renewable energy systems. This work proposes ubiquitous connectivity between
users at the cell edge and offloading the macro cells so as to provide features
the macro cell itself cannot cope with, such as extreme changes in the required
user data rate and energy efficiency. The offloading framework is then reformed
into a neural weighted framework that considers convergence and Lyapunov
instability requirements of mobile-edge computing under Karush Kuhn Tucker
optimization restrictions in order to get accurate solutions. The cell-layer
performance is analyzed in the boundary and in the center point of the cells.
The analytical and simulation results show that the suggested method
outperforms other energy-saving techniques. Also, compared to other solutions
studied in the literature, the proposed approach shows a two to three times
increase in both the throughput of the cell edge users and the aggregate
throughput per cluster.
