Abstract
In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.
Highlights
With the rapid development of wireless communication technology, the demand for high-speed data transmission has increased rapidly
Aiming at nonlinearity and the characteristics of time series of fading channels of Orthogonal frequency division multiplexing (OFDM) system, we propose an improved twin support vector regression (TSVR), projection wavelet weighted twin support vector regression (PWWTSVR) algorithm based on wavelet transform
TSVR is effective for channel estimation, and its regression performance has been verified by the works of Charrada and
Summary
With the rapid development of wireless communication technology, the demand for high-speed data transmission has increased rapidly. Xu and Wang (2014) proposed K-nearest neighbor (KNN)-based weighted twin support vector regression, which uses local information of data to improve prediction accuracy, KNN-based methods are suitable for clustering sample regression, but not for time series such as channel estimation. An efficient projection wavelet weighted twin support vector regression (PWWTSVR) was proposed in our work (Wang et al 2019), which introduces a weight matrix based on wavelet transform and suitable for dealing with time-series data. The improved TSVR is adopted for the first time to estimate doubly selective wireless channel parameters in OFDM system This method solves the problem that the performance of most traditional estimation methods is degraded by linear assumptions. 2. Aiming at nonlinearity and the characteristics of time series of fading channels of OFDM system, we propose an improved TSVR, PWWTSVR algorithm based on wavelet transform.
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