Energy Efficiency and Management in Wireless Networks

2013 ◽  
pp. 1-2
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

scholarly journals Throughput–Delay Tradeoff in Interference-Free Wireless Networks With Guaranteed Energy Efficiency

2015 ◽  
Vol 14 (3) ◽  
pp. 1608-1621 ◽  
Author(s):  
Yuzhou Li ◽  
Min Sheng ◽  
Cheng-Xiang Wang ◽  
Xijun Wang ◽  
Yan Shi ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks

scholarly journals Towards A Sustainable Green Design for Next-Generation Networks

2021 ◽  
Author(s):  
Isiaka Ajewale Alimi ◽  
Romilkumar K. Patel ◽  
Akeem O. Mufutau ◽  
Nelson J. Muga ◽  
Armando N. Pinto ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Energy Consumption ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Carbon Dioxide Emission ◽  
Green Energy ◽  
Green Communications ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

Abstract The evolution in the Information and Communications Technologies industry results in excessive energy consumption and carbon dioxide emission in the wireless networks. In this context, energy efficiency in mobile networks has been attracting considerable attention as green communications and operational expenditures reduction depend on it. Although the Internet of Things is to be supported by devices that are low-energy consuming, the power consumption of the huge number to be connected for several applications and services demand significant attention. To offer insights into green communications, this paper reviews various energy efficiency improvement techniques. Also, we consider a hybrid model in which the main grid power and dynamically harvested green energy from renewable energy sources can be leveraged to support the energy demand of the radio access network. In this regard, we reformulate the energy consumption model and consider an energy-efficient power allocation algorithm for green energy optimization. Numerical results show that with resource allocation algorithm exploitation, the energy efficiency can be enhanced. Besides, the amount of the grid energy consumption can be considerably minimized, resulting in the greenhouse gas emissions reduction in the wireless networks.

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