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Multiuser detection in hybrid Non-Orthogonal Multiple Access (NOMA) using machine learning
Author(s)
Motsaathebe, Mogomotsi Daphney
Date Issued
2024
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
The growing popularity of network applications has created a higher need for limited radio spectrum
resources due to the increasing number of users varying for access to these finite resources. Efficiently
managing the utilization of spectrum becomes increasingly crucial as demand rises. One effective
strategy to tackle this challenge involves employing multiple access techniques. Among these
approaches Non-Orthogonal Multiple Access (NOMA) emerges as a method, in this context. NOMA
enables several users to utilize the frequency band through power domain multiplexing, which greatly
boosts spectrum efficiency when compared to traditional orthogonal methods. However as more users
access the limited radio spectrum, the task of multiple user detection (MUD) becomes more complicated
leading to challenges in effectively distinguishing and decoding signals from various users. Dealing with
these complexities requires techniques that can address non linearities and interference present, in these
systems.
Artificial Neural Networks (ANNs) are one of the ways to handle these problems in an efficient manner.
ANNs have the capacity of handling complex non-linear relationships can substantially enhance the
accuracy for Multiple User (MU) in NOMA systems. This dissertation presents an ANN model
explicitly for MUD in NOMA networks. Our method capitalizes on the power of deep neural networks
to discern multi-user signals and filters them out which alleviated disadvantages in classical detection
methods. Through training, the ANN recognizes the patterns and characteristics of received signals.
This new method improves detection accuracy and spectrum utilization. The integration of ANNs with
NOMA systems is a good innovation in wireless communication technology providing better
performance for massive user scenarios.
The ANN-based multi-user detector performance has been carefully tested using simulated experiments.
The results show that at epoch 2, the proposed technique has a mean square error (MSE) of 0.023103,
thereby proving the efficiency of the neural network in solving the multi-user identification problem.
This level of performance will give an idea of the capability of ANNs in solving the complexity
introduced by NOMA systems and will act as a benchmark for future improvements. The efficiency of
ANN in this context therefore shows that it can boost detection accuracy and optimize spectrum use,
hence rendering a valuable edge over other previous methods. The research outcome obtained in support
of the efficiency of ANN-based approaches and has established a concrete platform for further
enhancement and use.
resources due to the increasing number of users varying for access to these finite resources. Efficiently
managing the utilization of spectrum becomes increasingly crucial as demand rises. One effective
strategy to tackle this challenge involves employing multiple access techniques. Among these
approaches Non-Orthogonal Multiple Access (NOMA) emerges as a method, in this context. NOMA
enables several users to utilize the frequency band through power domain multiplexing, which greatly
boosts spectrum efficiency when compared to traditional orthogonal methods. However as more users
access the limited radio spectrum, the task of multiple user detection (MUD) becomes more complicated
leading to challenges in effectively distinguishing and decoding signals from various users. Dealing with
these complexities requires techniques that can address non linearities and interference present, in these
systems.
Artificial Neural Networks (ANNs) are one of the ways to handle these problems in an efficient manner.
ANNs have the capacity of handling complex non-linear relationships can substantially enhance the
accuracy for Multiple User (MU) in NOMA systems. This dissertation presents an ANN model
explicitly for MUD in NOMA networks. Our method capitalizes on the power of deep neural networks
to discern multi-user signals and filters them out which alleviated disadvantages in classical detection
methods. Through training, the ANN recognizes the patterns and characteristics of received signals.
This new method improves detection accuracy and spectrum utilization. The integration of ANNs with
NOMA systems is a good innovation in wireless communication technology providing better
performance for massive user scenarios.
The ANN-based multi-user detector performance has been carefully tested using simulated experiments.
The results show that at epoch 2, the proposed technique has a mean square error (MSE) of 0.023103,
thereby proving the efficiency of the neural network in solving the multi-user identification problem.
This level of performance will give an idea of the capability of ANNs in solving the complexity
introduced by NOMA systems and will act as a benchmark for future improvements. The efficiency of
ANN in this context therefore shows that it can boost detection accuracy and optimize spectrum use,
hence rendering a valuable edge over other previous methods. The research outcome obtained in support
of the efficiency of ANN-based approaches and has established a concrete platform for further
enhancement and use.
Additional information
Thesis (MEng (Electrical Engineering))--Cape Peninsula University of Technology, 2024
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