Joint estimation method of dynamic sparse channel

Abstract

A joint estimation method of a dynamic sparse channel is based on OFDM (Orthogonal Frequency Division Multiplexing) wireless communication system, and adopts a channel estimation method based on a pilot frequency. The method comprises the following steps: 1) structuring a dynamic sparse channel model; 2) modeling a time frequency doubly-selective channel by using a complex exponential basis expansion model; 3) acquiring an optimal pilot position distribution of J different OFDM symbols sent at J different times by using a discrete stochastic optimization algorithm, designing a sparse pilot frequency mode and deducing a channel estimation model; 4) estimating a sparsity K (j) at the current time j by using a differential symphysis orthogonal matching pursuit (DSOMP) algorithm; 5) and according to the SOMP (Simultaneous Orthogonal Matching Pursuit) algorithm and the sparsity K (j), estimating a channel characteristic of the dynamic channel at the time j. By adopting the method, the dual-selective fading of the time frequency can be effectively resisted; the channel characteristic of the dynamic channel can be estimated; and the accuracy of the channel estimation and the utilization ratio of the frequency spectrum can be improved.

Description

Technical field: [0001] The invention relates to a channel estimation method for a receiver when a base station and a mobile terminal perform uplink and downlink communication in a time-frequency dual selective fading environment, specifically a joint estimation method for a dynamic sparse channel, which belongs to the technical field of wireless communication . Background technique: [0002] In wireless communication system, Orthogonal Frequency Division Multiplexing (OFDM) system can effectively eliminate frequency selective fading and overcome the influence of multipath fading. But for time-frequency dual-selection channels, it is very important to provide an accurate channel information estimation scheme. [0003] Based on the sparsity of the channel, more and more studies use Compressed Sensing (CS) theory for sparse channel estimation. The CS theory breaks through the limitation of the Nyquist sampling theorem, and can recover the original signal with very few measure...

AI Technical Summary

Problems solved by technology

[0004] However, for a dynamic sparse channel, the delay characteristics of the channel will change dynamically with time, and channel taps with non-zero gain may appear or disappear at a certain moment, resulting in a change in the sparsity of the channel.

The previously proposed channel estimation schemes based on CS and DCS ignore this dynamic change, and take the channel sparsity as a fixed value, which will seriously affect the accuracy of channel estimation.

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