Blind equalization and estimation of channel using artificial neural networks

Naveed, A.; Qureshi, Ijaz Mansoor; Cheema, Tanweer Ahmad; Jalil, Abdul

doi:10.1109/inmic.2004.1492869

Cited by 14 publications

(21 citation statements)

References 17 publications

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“…The following minimum phase channel with five taps is considered [20] Random data generation at the input account for the presence of 3ˑ10 5 symbols drawn from a QPSK constellation, 10 5 being total number of data symbols and 2ˑ10 5 training symbols. The SNR is varied from 0 to 30 dB with an increment of 2 dB.…”

Section: Simulation Results and Configuration Settingsmentioning

confidence: 99%

“…By following the same model given in [20], ( ) is the vector of length applied at the input layer. The estimated vector ̂( ) is compared with ( ) and the resulting error vector is written as…”

Section: B Implementing Neural Networkmentioning

confidence: 99%

“…Each node, representing a basic element of the neural network, is called neuron. As given in [20], the output of a neuron is given by…”

Section: B Implementing Neural Networkmentioning

confidence: 99%

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Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Ranhotra

Kumar

Magarini

et al. 2017

2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)

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Abstract-In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI).To reduce the effect of ISI different channel equalization algorithms are used. Complex equalization algorithms allow for achieving the best performance but they do not meet the requirements for implementation of real-time detection at low complexity, thus limiting their application. In this paper, we present different blind and non-blind equalization structures based on Artificial Neural Networks (ANNs) and, also, we analyze their complexity versus performance. The simulated network is based on multilayer feedforward perceptron ANN, which is trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network to improve the convergence speed. Simulation results demonstrate that the implementation of equalizers using ANN provides an upper hand over the performance and computational complexity with respect to conventional methods.

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Section: Simulation Results and Configuration Settingsmentioning

confidence: 99%

Section: B Implementing Neural Networkmentioning

confidence: 99%

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Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Ranhotra

Kumar

Magarini

et al. 2017

2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN)

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“…4. Another work of similar nature is available as cited in [5] where blind channel equalization and estimation is performed using ANN. This work discusses application of ANN mainly with a time invariant SISO channel.…”

Section: Application Of Feedforward Ann and Mlp In Wireless Communicamentioning

confidence: 99%

Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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The proliferation of number of users in a limited wireless spectrum have raised the levels of inter symbol interference (ISI) and have also contributed towards probable degradation of quality of service (QoS). The key challenges faced by upcoming wireless communication systems is to provide high-data-rate wireless access with better QoS. Also, the fast shrinking spectrum for such communication have necessitated the development of methods to increase spectral efficiency. Multiple input multiple output (MIMO) wireless technology is a viable option in such a situation and is likely to be able to meet the demands of these ever-expanding mobile networks. Many researchers have explored this field over a considerable period of time. A sizable portion of the research have been on the application of traditional statistical methods in such areas. Over the years, soft computational tools like artificial neural network (ANN), fuzzy systems and their combinations have received attention in the diverse segments of wireless communication. This is because of the fact that these are learning based systems. These learn from the environment, retain the knowledge and use it subsequently. This paper highlights some of the important application areas in wireless communication which have reported the use of soft computing tools in wireless communication that are in circulation in open literature.

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“…Further, these networks of different architectures have found successful application in channel estimation problems because of their nonlinear characteristics. ANN is proposed as a channel estimator for QPSK and QAM constellation, respectively in [5] and [4].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

Charrada

Aziz²

2016

TMLAI

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In this paper we apply and assess the performance of support vector machine regression (SVR) and artificial neural network (ANN) channel estimation algorithms to the reference signal structure standardized for LTE Downlink system. SVR and ANN where applied to estimate real channel environment such as vehicular A channel defined by the International Telecommunications Union (ITU) in the presence of nonlinear impulsive noise. The proposed algorithms use the information provided by the received reference symbols to estimate the total frequency response of the time variant multipath fading channel in two phases. In the first phase, each method learns to adapt to the channel variations, and in the second phase it predicts all the channel frequency responses. Finally, in order to evaluate the capabilities of the designed channel estimators, we provide performance of SVR and ANN, which is compared with traditional Least Squares (LS) and Decision Feedback (DF). The simulation results show that SVR has a better accuracy than other estimation techniques.

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Blind equalization and estimation of channel using artificial neural networks

Cited by 14 publications

References 17 publications

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Application of Soft Computing Tools in Wireless Communication—A Review

Support Vector Machine Regression and Artificial Neural Network for Channel Estimation of LTE Downlink in High-Mobility Environments

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