research-article

Recurrent neural network based channel estimation technique for STBC coded MIMO system over Rayleigh fading channel

Authors:

Parismita Gogoi,

Kandarpa Kumar SarmaAuthors Info & Claims

CUBE '12: Proceedings of the CUBE International Information Technology Conference

Pages 294 - 298

https://doi.org/10.1145/2381716.2381771

Published: 03 September 2012 Publication History

Get Access

Abstract

Artificial Neural Networks (ANN)s, due to their high degree of adaptability, can be used to model nonlinear phenomenon of channel estimation. In this work, a channel estimation technique based on Recurrent Neural Network (RNN) has been proposed as an alternative to pilot based channel estimation technique for STBC- MIMO systems over Rayleigh fading channels. Learning property of ANN is fully exploited for decoding the degraded symbols over severely faded channel. This technique is found to be more bandwidth efficient compared to pilot-based channel estimation techniques. Simulated results in terms of bit error rates (BER) vs. signal to noise ratio (SNR) depict the effectiveness of the learning capability of ANNs for the task of channel estimation over wireless fading channel.

References

[1]

T. S. Rappaport, 1997. Wireless Communications - Principles and Practice, Pearson Education.

Digital Library

Google Scholar

[2]

T. M. Duman and Ali Ghrayeb, 2007. "Coding for MIMO Communication System," John Wiley and sons.

Digital Library

Google Scholar

[3]

S. M. Alamouti, 1998. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, vol. 16, issue 8, pp. 1451--1458.

Digital Library

Google Scholar

[4]

V. Tarokh, N. Seshadri, and A. R. Calderbank, 1998. Space time codes for high data rate wireless communication: Performance analysis and code construction, IEEE Transactions on Information Theory, vol. 44, issue 2, pp. 744--765.

Digital Library

Google Scholar

[5]

V. Tarokh, H. Jafarkhani, and A. R. Calderbank. 1999. Space time block codes from orthogonal designs. IEEE transactions on Information Theory, vol. 45, issue 5, pp. 744--765.

Digital Library

Google Scholar

[6]

K. Burse et. al. 2010. Channel Equalization Using Neural Networks: A review. IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, vol. 40, No. 3, pp. 352--357.

Digital Library

Google Scholar

[7]

Siu, S., Gibson, G. J., & Cowan, C. F. N. 1990. Decision feedback equalization using neural network structures and performance comparison with standard architecture. Proceedings of the Institution of Electrical Engineers, 137(pt. 1), 221--225.

Google Scholar

[8]

Zhang, L., & Zhang, X. 2007. MIMO channel estimation and equalization using three-layer neural network with feedback. Tsinghua Science and Technology, 12(6), 658--662.

Crossref

Google Scholar

[9]

Ciffikli C. et. al., 2009. Artificial neural network channel estimation based on levenberg-marquardt for OFDM systems, Wireless Personal Communication, vol. 51, pp. 221-229, DOI= 10.1007/s11277-008-9639-2.

Crossref

Google Scholar

[10]

Nawaz S. J. et. al., 2009. Neural network based mimo-ofdm channel equalizer using comb-type pilot arrangement, International Conference on Future Computer and Communication, pp. 36--41.

Digital Library

Google Scholar

[11]

S. Haykin, 1999, Neural networks: A comprehensive foundation, (2nd ed.), prentice-hall.

Digital Library

Google Scholar

[12]

P. Gogoi and K. K. Sarma. 2012. STBC coded MISO and MIMO set-up in frequency selective wireless fading channels for BPSK and QPSK modulation schemes, proceedings of 2^nd national Conference on Computational Intelligence and Signal Processing, pp 157--162.

Google Scholar

Cited By

View all

Mai ZChen Y(2024)A CSI Entropy-Based Estimation Algorithm and Modified Recurrent Neural Networks for Time-Varying Non-Stationary ChannelsIEEE Transactions on Vehicular Technology10.1109/TVT.2023.333970673:5(6614-6625)Online publication date: May-2024
https://doi.org/10.1109/TVT.2023.3339706
Manasa BP. V(2022)Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systemsInternational Journal of Communication Systems10.1002/dac.515235:10Online publication date: 6-Apr-2022
https://doi.org/10.1002/dac.5152
Wang ZPu FYang XChen NShuai YYang R(2020)Online LSTM-Based Channel Estimation for HF MIMO SC-FDE SystemIEEE Access10.1109/ACCESS.2020.30103598(131005-131020)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3010359
Show More Cited By

Index Terms

Recurrent neural network based channel estimation technique for STBC coded MIMO system over Rayleigh fading channel
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

Recommendations

Pilot-assisted channel estimation for STBC-based wireless MIMO-OFDM systems
IWCMC '07: Proceedings of the 2007 international conference on Wireless communications and mobile computing

With equipping multiple antennas at both transmitter and receiver ends, the desired signals in the OFDM wireless communications could be transmitted and received through multiple uncorrelated channels for achieving twofold benefits and high flexibility. ...
Analysis of the Alamouti STBC MIMO System With Spatial Division Multiplexing Over the Rayleigh Fading Channel
The Alamouti space-time block coding (STBC) multiple input multiple output (MIMO) system with spatial division multiplexing (SDM) is different from the pure SDM MIMO system in transmitting groups of Alamouti STBC symbols. Because of the Alamouti STBC, the ...
Estimation of STBC-MIMO Radio Channel at 2.4 GHz in Underground Mine Gallery
IPAC '15: Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication

This paper investigates the radio link performance of wireless system combining multiple-input multiple-output (MIMO) and space time block coding (STBC) techniques, while implementing the least-squares (LS) and the minimum mean square error (MMSE) ...

Comments

Information & Contributors

Information

Published In

CUBE '12: Proceedings of the CUBE International Information Technology Conference

September 2012

879 pages

ISBN:9781450311854

DOI:10.1145/2381716

General Chair:
Vidyasagar Potdar
Curtin University, Australia
,
Program Chair:
Debajyoti Mukhopadhyay
Maharashtra Institute of Technology, India

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CUBE '12

Sponsor:

CUOT

CUBE '12: CUBE International IT Conference & Exhibition

September 3 - 5, 2012

Pune, India

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
309
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mai ZChen Y(2024)A CSI Entropy-Based Estimation Algorithm and Modified Recurrent Neural Networks for Time-Varying Non-Stationary ChannelsIEEE Transactions on Vehicular Technology10.1109/TVT.2023.333970673:5(6614-6625)Online publication date: May-2024
https://doi.org/10.1109/TVT.2023.3339706
Manasa BP. V(2022)Swarm intelligence‐based deep ensemble learning machine for efficient channel estimation in MIMO communication systemsInternational Journal of Communication Systems10.1002/dac.515235:10Online publication date: 6-Apr-2022
https://doi.org/10.1002/dac.5152
Wang ZPu FYang XChen NShuai YYang R(2020)Online LSTM-Based Channel Estimation for HF MIMO SC-FDE SystemIEEE Access10.1109/ACCESS.2020.30103598(131005-131020)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3010359
Kaya KGündüz Öğüdücü Ş(2020)Deep Flexible Sequential (DFS) Model for Air Pollution ForecastingScientific Reports10.1038/s41598-020-60102-610:1Online publication date: 25-Feb-2020
https://doi.org/10.1038/s41598-020-60102-6
Gogoi PSarma K(2018)Kalman filter and semi-blind technique-based channel estimation for coded STBC multi-antenna set-ups in faded wireless channelsInternational Journal of Information and Communication Technology10.1504/IJICT.2014.0579766:1(86-108)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.1504/IJICT.2014.057976

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Pilot-assisted channel estimation for STBC-based wireless MIMO-OFDM systems

Analysis of the Alamouti STBC MIMO System With Spatial Division Multiplexing Over the Rayleigh Fading Channel

Estimation of STBC-MIMO Radio Channel at 2.4 GHz in Underground Mine Gallery