Bayesian Compressive Sensing For Sparse Channel Estimation in MISO/Multiuser OFDM Systems

Bayesian Compressive Sensing For Sparse Channel Estimation in MISO/Multiuser OFDM Systems
Author(s):
Ashwini Ashok, Shiny.C.
Keywords:
MIMO, OFDM, Sparse channel estimation, Compressive sensing, MSE, BER
Abstract
In recent years, the emergence of new technologies such as MIMO have made the spectral efficiency of communication systems of high concern. For large scale MISO/multi user OFDM systems, accurate channel estimation is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. This paper proposes a Bayesian compressive sensing method for channel estimation where a MISO OFDM signal model having interference free region of the received training sequence is developed. This signal model is further used for sparse channel estimation, utilizing the CS reconstruction algorithm and prior statistical knowledge of the channel. The Bayesian CS based channel estimation method results in an optimization with far fewer samples than conventional LS and Sparse channel estimation techniques. A comparative study on the parameters like Mean Square Error (MSE) and Bit Error Rate (BER) demonstrates that the proposed scheme outperforms conventional methods in terms of both MSE and BER. This project is implemented in MATLAB 2014a
Article Details
Unique Paper ID: 146704 Publication Volume & Issue: Volume 5, Issue 1 Page(s): 881 - 886
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