Machine Learning for Joint Channel Equalization and Signal Detection

Zhang, Lin; Yang, Lie‐Liang

doi:10.1002/9781119562306.ch12

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…In terms of channel equalization, in [12] deep learning was used for channel equalization and several deep neural network-based equalizers were presented and compared with traditional equalization methods. Better error performance than traditional equalization methods was obtained.…”

Section: B Related Workmentioning

confidence: 99%

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

Zheng¹,

Chen²,

Yang³

2021

IEEE Trans. Cogn. Commun. Netw.

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A canonical wireless communication system consists of a transmitter and a receiver.The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. Specifically, we design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure and propose to use multiple binary classifiers at the final classification layer to achieve multi-bit information stream recovery. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under noise, RF impairments, multipath fading, and cochannel interference.

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Section: B Related Workmentioning

confidence: 99%

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

Zheng¹,

Chen²,

Yang³

2021

IEEE Trans. Cogn. Commun. Netw.

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“…A DL-based neural network followed a comprehensive overview of various equalizers for channel equalization. The work was evaluated for the OFDM system, demonstrating the better BER performance of deep learning architecture over variable SNR levels ranging from 5dB to 30dB as compared to existing machine learning techniques [23]. Ji et al (2020) had postulated an approach to address interference in multipath propagation using a blind equalization based on Deep Learning (DL) architecture.…”

Section: Et Al (2020) Proposed An M-ary Differential Chaos Shiftmentioning

confidence: 99%

Improved Channel Equalization using Deep Reinforcement Learning and Optimization

Katwal

Bhatia

2018

ICST Transactions on Scalable Information Systems

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“…In order to reduce the computational costs of traditional equalizers, machine learning-based equalizers have been introduced to mitigate intersymbol interference. Channel equalization can be viewed as a classification problem, where the equalizer is designed as a decision device with the motivation to classify the transmitted signals as accurately as possible (Zhang and Yang, 2020). Gibson et al introduced an adaptive equalizer employing a neural network architecture based on multilayer perceptrons (MLP) to counter intersymbol interference on linear channels with Gaussian white noise (Gibson et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

Zhou,

Zhang,

et al. 2024

Front. Mar. Sci.

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IntroductionIn shallow-water environments, the reliability of underwater communication links is often compromised by significant multipath effects. Some equalization techniques such as decision feedback equalizer, and deep neural network equalizer suffer from slow convergence and high computational complexity.MethodsTo address this challenge, this paper proposes a simplified decision feedback Chebyshev function link neural network equalizer (SDF-CFLNNE). The structure of the SDF-CFLNNE employs Chebyshev polynomial function expansion modules to directly and non-linearly transform the input signals into the output layer, without the inclusion of hidden layers. Additionally, it feeds the decision signal back to the input layer rather than the function expansion module, which significantly reduces computational complexity. Considering that, in the training phase of neural networks, the random initialization of weights and biases can substantially impact the training process and the ultimate performance of the network, this paper proposes a chaotic sparrow search algorithm combining the osprey optimization algorithm and Cauchy mutation (OCCSSA) to optimize the initial weights and thresholds of the proposed equalizer. The OCCSSA utilizes the Piecewise chaotic population initialization and combines the exploration strategy of the ospreywith the Cauchy mutation strategy to enhance both global and local search capabilities. RseultsSimulations were conducted using underwater multipath signals generated by the Bellhop Acoustic Toolbox. The results demonstrate that the performance of the SDFCFLNNE initialized by OCCSSA surpasses that of CFLNN-based and traditional nonlinear equalizers, with a notable improvement of 2-6 dB in terms of signal-to-noise ratio at a bit error rate (BER) of 10−4 and a reduced mean square error (MSE). Furthermore, the effectiveness of the proposed equalizer was validated using the lake experimental data, demonstrating lower BER and MSE with improved stability. DiscussionThis underscores thepromise of employing the SDFCFLNNE initialized by OCCSSA as a promising solution to enhance the robustness of underwater communication in challenging environments.

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Machine Learning for Joint Channel Equalization and Signal Detection

Cited by 8 publications

References 51 publications

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

Improved Channel Equalization using Deep Reinforcement Learning and Optimization

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

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