Gray modeling-based OFDM narrow-band slow-fading slowly time-varying channel estimation method

Abstract

The invention discloses a gray modeling-based orthogonal frequency division multiplexing (OFDM) narrow-band slow-fading slowly time-varying channel estimation method, which is mainly used for solving the tracking estimation problem of a slow-fading slowly time-varying channel under a high-speed mobile communication environment. The technical key point comprises the following steps of: acquiring time domain channel information corresponding to pilot frequency according to the known pilot frequency information; respectively establishing an amplitude gray GM (1,1) model and a phase gray GM (1,1) model to obtain an amplitude estimation value and a phase estimation value of a time domain channel coefficient corresponding to an unknown data frequency component; performing complex vector integration and fast Fourier transform (FFT) on the amplitude estimation value and the phase estimation value to obtain a frequency domain channel gain coefficient corresponding to the unknown data frequency component; and feeding the estimated frequency domain channel gain coefficient back to an OFDM receiver so as to realize dynamic channel tracking. The invention verifies the feasibility of the gray modeling in the field of time-domain two-dimensional described channel estimation; and compared with the traditional method, the method has the advantages that: the method has low computation and high frequency spectrum utilization rate, is easy to implement and has good estimation performance on the narrow-band slow-fading slowly time-varying channel.

Description

technical field [0001] The invention belongs to the field of wireless communication signal processing, and relates to a method for estimating a slow fading channel in a mobile transmission environment, which can be used to solve the problem that the channel coefficient of the slow fading channel in the future wireless communication industry has a slight change within a frame time Estimation and application problems. Background technique [0002] In a normal OFDM link, after the serial-to-parallel conversion of the input bit sequence is completed, the time-domain sampling sequence of the OFDM modulated signal is obtained through modulation mapping and IFFT transformation, and the cyclic prefix is ​​added, and then the signal is transmitted to the In the spatial channel: at the receiving end, the received signal is serial-to-parallel converted, the guard interval is removed, and then FFT is performed to obtain the received signal in the frequency domain. This process is due to...

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Problems solved by technology

The main disadvantage of this type of method is that the algorithm complexity is extremely high, and it is difficult to apply in actual engineering.

[0007] (2) Bai Binfeng and others proposed a Wiener minimum mean square error channel tracking algorithm in "A Wiener LMS channel tracking algorithm in OFDM system. Journal of Electronics and Information Technology, 2005, 27(11): 1699-1703" and prediction algorithm, this method adopts the method of polynomial design Wiener filter, designs a prediction filter in the frequency domain, substitutes the predicted value of the channel into the algorithm for iteration, realizes channel tracking symbol by symbol, and improves the algorithm in tracking fast-changing The performance under channel conditions realizes the reliable tracking and prediction of the channel response in the frequency domain of the system, but the algorithm assumes that the responses of each subcarrier are not correlated with each other during design, resulting in a decrease in algorithm performance

This method assumes that the channel changes with the sub-block OFDM symbol as the time unit within a frame time, but it is still approximately static within the same OFDM symbol, so the complexity of the algorithm is reduced, but it cannot reflect the Time-varying characteristics of slow fading channels

[0009] The above estimation methods involving time-varying slow fading channels, the Kalman filter algorithm and the Wiener LMS channel tracking algorithm have high complexity and difficult engineering implementation; the complexity of the time-domain RLS adaptive filtering algorithm has decreased compared with the previous two, However, it still does not fundamentally reflect the time-varying nature of slow fading channels, and cannot be well applied in high-speed mobile communication environments.

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