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Ng, Yin Hoe
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Electrical & Electronics Engineering

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An ANN enabled joint power allocation and base station switching system for EE heterogeneous networks Euttamarajah, Shornalatha; Ng, Yin Hoe; Tan, Chee Keong
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7262

Abstract

In recent years, dynamic and complex development in wireless communication in network models or environments led to more tedious and complicated resource management issues (i.e., power allocation and base station switching (BSS)). Conventional solutions often suffer from delays and degraded network service quality. Due to the ability of machine learning in analyzing huge volumes of data and automatically adapt to environmental changes, it emerges as a highly sought-after technique. In this work, we propose a machine learning approach based on feed-forward neural network (FFNN) to predict the active BS sets and estimate the power allocation to each user equipment (UE) within the active BSs for energy-efficiency (EE) maximization of a coordinated multi-point (CoMP-enabled) cellular system with hybrid-powered transmitting nodes in a HetNet-based architecture. By training the neural network model efficiently using a regression-based supervised learning technique that employs various backpropagation algorithms, almost similar EE performance (less than 5% difference) can be achieved with significantly reduced computational complexity and delay compared to the traditional methods, such as the well-known dual decomposition and brute force techniques. The effects of various hyper parameters and back-propagation algorithms are also investigated. Our results demonstrate that the proposed framework is a promising solution for establishing a fully green and intelligent network.
A study on the impact of layout change to knowledge distilled indoor positioning systems Mazlan, Aqilah; Ng, Yin Hoe; Tan, Chee Keong
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7157

Abstract

Convolutional neural networks (CNN)-based indoor positioning systems (IPS) have gained significant attention over the past decade due to their ability to provide precise localization accuracy. However, the use of CNNs in these systems comes with a higher computational cost. To tackle this issue, recent studies have introduced knowledge distilled positioning schemes to mitigate the computational burden. Despite the clear possibility of performance degradation due to signal fluctuations, there remains a lack of investigation into the performance of knowledge distilled and CNN based indoor positioning schemes in dynamic indoor environment. To fill this research gap, this paper investigates the practicality of implementing knowledge distilled-based indoor positioning schemes in real-world by analyzing the impact of indoor layout change on these schemes. Results demonstrate that in the case of layout change, the knowledge distilled-based indoor positioning schemes without teaching assistant can still achieve good performance, with an improvement of 11.56% in average positioning error compared to simple CNN model, while taking only 49.05% of the complex CNN model’s execution time. However, the knowledge distilled-based indoor positioning scheme with teaching assistant fails under the same condition as the inclusion of teacher assistant leads to increased error in modeling the received signal strengths (RSS) and locations relationship.
Co-Authors Euttamarajah, Shornalatha Mazlan, Aqilah Tan, Chee Keong