An Improved Threshold Wavelet Denoising LS Channel Estimation Algorithm Based on IoT
HaoLin Wang, Fanqin Zhou, Peng Yu, Shaohua Liu · 2020
In order to resolve the issue of high-reliability communication over long distances, the latest Internet of Things (IoT) technology plays a key role. Effective channel estimation is the key to the overall system implementation. Aiming at the problem that the Least Square (LS) estimation algorithm in IoT is affected by noise and estimation accuracy is relatively poor. To solve this problem, an improved LS estimation algorithm in view of wavelet denoising is proposed. At first, the improved algorithm uses the LS algorithm to perform the initial estimation of the channel, and then shifts to the wavelet domain for threshold denoising. By improving the denoising threshold function, the noise is better eliminated and the estimation accuracy is improved. The bit error rate (BER)and the mean squared error (MSE) of the algorithm were simulated by MATLAB. The simulation consequent indicates that the performance of channel estimation algorithm in the paper is notably better than LS estimation algorithm, LS based on DFT denoising, and soft threshold wavelet denoising algorithm. And compared with the soft threshold wavelet denoising algorithm, the SNR of the improved algorithm is increased by about 2 dB for the same BER.