Abstract
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.
Highlights
In wireless communication systems [1,2,3], the performance may be severely degraded because of wireless channel issues
The transmitted signal passes through the communication channel and has faced various impairment issues such as Intersymbol Interference (ISI), Doppler shift, and fading effects
In [26], the Machine Learning (ML) and Neural Networks (NNs) have been used in the Frequency Division Duplexing (FDD) system which is a double selective channel, and the results showed improvements in terms of MiMeSqEr in the prediction of the channel
Summary
In wireless communication systems [1,2,3], the performance may be severely degraded because of wireless channel issues. The resulting data sequence at the output of the source encoder is passed to the channel encoder which adds redundancy in a controlled manner, to help the receiver to detect and correct the channel-induced errors. To critically assess the performance of various channel equalization techniques by performing simulations, the mathematical formulation is presented in [8] for the communication system where they considered sðtÞ to be the transmitted signal. In [10], an estimation technique was presented for the transmitter sending a known sequence of data symbols to the receiver called pilot symbols. In [11], the receiver has no information about the input signal of the channel This technique uses the data symbols for channel estimation by employing the precoding of the symbols at the transmitter. This type of equalizer does not use the filter for equalizing the channel but instead uses the Viterbi algorithm to decode the sequence and chooses the sequence with maximum probability as the output
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