Energy Efficient Computation Offloading and Virtual Connection Control in Uplink Small Cell Networks

TL;DR

This paper introduces a deep learning-based joint resource allocation and power control method for uplink small cell networks, significantly improving energy efficiency while maintaining user throughput.

Contribution

It proposes a novel joint resource and power allocation algorithm that addresses nonconvex hierarchical optimization problems in small cell networks.

Findings

Significant increase in energy efficiency compared to conventional methods.

Effective joint flow and power control with SLA-based resource allocation.

Validation through simulation demonstrating improved network performance.

Abstract

Nowadays, the use of soft computational techniques in power systems under the umbrella of machine learning is increasing with good reception. In this paper, we first present a deep learning approach to find the optimal configuration for HetNet systems. We used a very large number of radial configurations of a test system for training purposes. We also studied the issue of joint carrier/power allocation in multilayer hierarchical networks, in addition to ensuring the quality of experience for all subscribers, to achieve optimal power efficiency. The proposed method uses an adaptive load equilibrium model that aims to achieve "almost optimal" equity among all servers from the standpoint of the key performance indicator. Unlike current model-based energy efficiency methods, we propose a joint resource allocation, energy efficiency, and flow control algorithm to solve common nonconvex and hierarchical optimization problems. Also, by referring to the allocation of continuous resources based on SLA, we extended the proposed algorithm to common flow/power control and operational power optimization algorithm to achieve optimal energy efficiency along with ensuring user's throughput limitations. Also, simulation results show that the proposed controlled power/flow optimization approach can significantly increase energy efficiency compared to conventional designs using network topology adjustment capability.