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 network...

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Bibliographic Details
Published in:Wireless personal communications 2021-11, Vol.121 (1), p.221-243
Main Authors: Mirzaei, A., Najafi Souha, Alireza
Format: Article
Language:English
Subjects:
Algorithms
Communications Engineering
Computer Communication Networks
Configurations
Constraint modelling
Data storage
Deep learning
Edge computing
Engineering
Machine learning
Mathematical analysis
Mathematical problems
Mobile computing
Networks
Neural networks
Optimization
Power efficiency
Power flow
Resource allocation
Signal,Image and Speech Processing
Smart grid
Topology
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Summary: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.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-08632-8