Pilot-based low complexity compressed sensing channel estimation method

Abstract

The invention discloses a pilot-based low complexity compressed sensing channel estimation method. The method comprises the following steps: setting pilot positions based on a randomized distributionprinciple according to a compression sensing algorithm, performing a cross-correlation operation on the local pilot signals and the received pilot signals at the receiving end, estimating the channelsparsity and delay distribution of non-zero sparsity paths by using the cross-correlation operation result, using the obtained estimated value as a preset value of the compressed sensing algorithm andan initial value of the iterative operation, obtaining a channel estimation result by combining the low complexity compressed sensing reconstruction algorithm, and completing the DFT-based time domain interpolation algorithm for the estimation of the channel information of data positions. The method uses compressed sensing channel estimation for complex wireless channels to reduce pilot overheadand improve spectrum utilization, uses randomized pilot position distribution to reconstruct signals with higher probability, and uses cross-correlation operation of pilot signals at the receiving endto estimate the delay distribution of channel sparsity and non-zero sparse paths, which can reduce the number of iterations and greatly reduce the complexity of the algorithm.

Description

technical field [0001] The invention relates to a pilot-based low-complexity channel estimation method realized by compressed sensing technology aimed at the complex wireless channel environment in the future mobile communication system. Background technique [0002] The wireless channel is highly time-varying and random, and the characteristics of the wireless channel are very complex due to different propagation environments. It also has the property of selective fading in the frequency domain and time domain. In the future mobile communication system, the communication network will have more significant heterogeneous and distributed features, the arrangement of cells will be closer, and the situation of wireless transmission channels will become more complicated. Therefore, the above application scenarios put forward higher requirements on the channel estimation algorithm of the wireless communication system. [0003] Among the currently commonly used channel estimation ...

AI Technical Summary

Problems solved by technology

[0009] The present invention proposes a method for compressive sensing channel estimation based on pilots or training sequences, which solves many problems in the application of traditional channel estimation algorithms under complex channel conditions in future mobile communications

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