Neural Networks for Energy-Efficient Self Optimization of eNodeB Antenna Tilt in 5G Mobile Network Environments

Qureshi, Muhammad Nauman; Shahid, Muhammad Khalil; Tiwana, Moazzam Islam; Haddad, Majed; Ahmed, Irfan; Faisal, Tarig

doi:10.1109/access.2022.3181595

Cited by 3 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [ 191 ], artificial neural network (ANN) and stochastic learning based optimization algorithms are used to optimise the antenna tilt. Network coverage and capacity are enhanced by using the ANN algorithm.…”

Section: 5g Coverage Enhancement Techniquesmentioning

confidence: 99%

A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges

Sudhamani

Roslee

Tiang

et al. 2023

Sensors

View full text Add to dashboard Cite

Fifth generation (5G) is a recent wireless communication technology in mobile networks. The key parameters of 5G are enhanced coverage, ultra reliable low latency, high data rates, massive connectivity and better support to mobility. Enhanced coverage is one of the major issues in the 5G and beyond 5G networks, which will be affecting the overall system performance and end user experience. The increasing number of base stations may increase the coverage but it leads to interference between the cell edge users, which in turn impacts the coverage. Therefore, enhanced coverage is one of the future challenging issues in cellular networks. In this survey, coverage enhancement techniques are explored to improve the overall system performance, throughput, coverage capacity, spectral efficiency, outage probability, data rates, and latency. The main aim of this article is to highlight the recent developments and deployments made towards the enhanced network coverage and to discuss its future research challenges.

show abstract

Section: 5g Coverage Enhancement Techniquesmentioning

confidence: 99%

A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges

Sudhamani

Roslee

Tiang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, ANN are known as a powerful modeling tool and have been widely studied and used in the design and optimization of RF circuits such as filters [13] [14], sensor [15], loop antenna [16], dual and multi-band antennas [17]- [21], ultra wideband antenna [22], antenna in dielectric environment [23], dielectric resonators [24] [25], base station antenna tilting [26], Yagi Uda antenna [27] and array antennas [28]- [34]. Through a training process, the ANN are capable to learn, map and generalize complex nonlinear relationship between synthesis variables and their corresponding electromagnetic (EM) responses.…”

Section: Introductionmentioning

confidence: 99%

A Global Optimization Method for Wideband and Small Supergain Arrays Design Using Artificial Neural Network

Touhami,

Collardey,

Sharaiha

2023

IEEE Open J. Antennas Propag.

View full text Add to dashboard Cite

This paper introduces an efficient approach for compact, wideband and supergain arrays design using artificial neural network (ANN) based optimization. The proposed method optimize at the same time the distance inter-elements of the array antenna, its input impedance as well as its directivity. Such global optimization considerably improves the performances of superdirective arrays in terms of gain, bandwidth and efficiency. The proposed method is used afterwards to synthesize a three-elements array using two different unit elements. The comparison of the achieved performances and the Harrington limit reveals that the developed antennas can be qualified as supergain antennas. To our knowledge, this is the first demonstration of a wideband supergain array with more than two elements in the open literature. To validate the results, a prototype was manufactured and measured. The measurements show that the antenna has a wide impedance bandwidth of 22.6 %, a peak directivity of 8.3 dBi and a total efficiency greater than 80%.

show abstract