Compressed sensing wireless communication channel estimation method based on sparsity self-adapting

Abstract

The invention belongs to the field of wireless communication channel estimation, particularly relates to a compressed sensing wireless communication channel estimation method based on sparsity self-adapting, which includes the ssteps: (1) collecting demodulated receiving signals and calculating channel response of a pilot frequency position; (2) constructing a measurement matrix phi required by signal reconstruction; (3) calculating an association degree vector and sequencing elements of the vector; (4) calculating second difference vector of a novel association degree vector after sequencing and setting a threshold value I for judging sparsity of signals; (5) estimating sparsity S of channel impulse response; (6) comparing the threshold value I with the last element of a vector D sequentially, and a coefficient value corresponding to the first element larger than the threshold value is the estimated sparsity S of the signals; and (7) reconstructing the signals. The channel estimation method breaks a bottleneck of a traditional compressed sensing algorithm that the sparsity of the signals must be known, and signal reconstruction of sparsity self-adapting is achieved.

Description

Technical field [0001] The invention belongs to the field of wireless communication channel estimation, in particular to multi-carrier sparse channel estimation under the condition of dual selective channels. Background technique [0002] Compressive Sensing (CS) theory is a major breakthrough in the field of applied mathematics and signal processing. It means that when the signal is compressible or has sparsity in a transform domain, the signal can be realized by collecting a small number of signal projections. An exact or approximate reconstruction of . Under this theoretical framework, the sampling rate is no longer determined by the bandwidth of the signal, but by the structure and content of the information in the signal, thus breaking the bottleneck limitation of the traditional Nyquist sampling theorem on the sampling rate. Compressed sensing theory enables the sampling and compression of signals to be performed simultaneously at a low rate, which greatly reduces the...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem that traditional channel estimation techniques cannot accurately estimate sparse channels under dual-selective fading channel conditions, the present invention proposes a multi-carrier system channel estimation method that can reduce the number of pilots and improve the spectrum utilization rate of wireless communication systems

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