Performance evaluation of machine learning-based channel equalization techniques: New trends and challenges

Hassan, Shahzad and Tariq, Noshaba and Naqvi, Rizwan Ali and Rehman, Ateeq Ur and Kaabar, Mohammed K. A. (2022) Performance evaluation of machine learning-based channel equalization techniques: New trends and challenges. Journal of Sensors, 2022. ISSN 1687-725X, DOI

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Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Item Type: Article
Funders: None
Uncontrolled Keywords: Wireless communication systems; Channel equalization; Network performance
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 02 Aug 2022 04:31
Last Modified: 02 Aug 2022 04:31

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