OFDM (Orthogonal Frequency Division Multiplexing) system parametric channel estimation and equalization method based on compressed sensing technology

Abstract

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) system parametric channel estimation and equalization method based on compressed sensing technology. The channel estimation method comprises the following steps: 1) an OFDM frequency domain at a pilot subcarrier receives data, and parameter estimation is carried out on the channel based on a channel parametric model: firstly, according to a MDL (Minimum Description Length) criterion, a multipath number estimation value L<^> of the channel is obtained; then, the ESPRIT (estimation of signal parameters by rotational invariance techniques) technology is used for estimating delay parameters for each path, and multipath delay estimation values tau<^>=[tau<^>1, tau<^>2,..., tau<^> < L<^>>] are obtained; and 2) a parameter estimation result is used for building a redundant dictionary matrix, and a channel response estimation value is solved through a BPDN algorithm. Based on parametric channel modeling, the signal parameter estimation technology is combined with a compressed sensing frame, the channel estimation precision and the communication efficiency are improved, and the communication bit error rate is reduced. Compared with the traditional channel estimation method, the method of the invention has obvious performance advantages.

Description

technical field [0001] The invention relates to the field of communication technology, and mainly relates to a parameterized channel estimation and equalization method of an OFDM system based on compressed sensing technology. Background technique [0002] The performance of the wireless communication system is affected by the wireless channel, and its communication environment is different from the wired communication, which has great uncertainty and randomness. Generally speaking, shadow fading, multipath effect and Doppler effect will affect the wireless channel, making the wireless channel show time selective fading and frequency selective fading. The receiving end of wireless communication needs to compensate the wireless channel, which requires channel estimation to obtain parameters such as channel order, impulse response, and Doppler shift in the wireless channel. The accuracy of channel estimation will directly affect the performance of the entire system, and it is ...

AI Technical Summary

Problems solved by technology

However, like the channel estimation method based on compressed sensing, the channel estimation method using parametric model has a common problem that the computational complexity is too high. Compared with the estimation method, the estimation effect has no obvious advantage

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