| Title |
Performance Analysis of a Compressive Sensing?based Channel Estimation Algorithm in a Noisy Channel |
| Authors |
Ami Munshi(Ami Munshi) ; Srija Unnikrishnan(Srija Unnikrishnan) |
| DOI |
https://doi.org/10.5573/IEIESPC.2019.8.3.227 |
| Keywords |
Channel estimation ; Compressive sensing ; Sparsity ; FFT ; BER ; SISO ; OFDM |
| Abstract |
Taking forward Compressive Sensing (CS)-based channel estimation, the emphasis of this paper is to analyze the performance of CS-based channel estimation by varying the Fast Fourier Transform (FFT) size (number of subcarriers) employed in Orthogonal Frequency Division Multiplexing (OFDM). By selecting an optimal FFT size, it is possible to bring the Bit Error Rate (BER) down to zero at a particular Signal-to-Noise Ratio (SNR) for both the Least Square (LS) channel estimation technique and the least square with compressive sensing (LS-CS) channel estimation technique. The main benefit is that in the LS-CS technique, we need to sense only a very small percentage of the total channel coefficients, whereas in LS we must sense all channel coefficients, making the LS-CS technique much more efficient without any significant effect on the result. We also observe that by increasing the FFT size, performance of the system in terms of BER can be significantly improved, even when the channel is very noisy. |