OFDM sparse channel estimation method based on adaptive compressed sensing

A compressed sensing and sparse channel technology, applied in the field of OFDM sparse channel estimation based on adaptive compressed sensing, can solve the problems affecting the reconstruction accuracy and reconstruction quality, and the reduction of reconstruction accuracy, so as to improve the reconstruction accuracy and reconstruction speed, and reduce the system Effects of bit error rate and short reconstruction time

Inactive Publication Date: 2019-04-12
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, since the step size s of this algorithm is fixed, the value of the step size will affect the reconstruction accuracy and reconstruction quality. Therefore, how to improve the reconstruction speed while ensuring a high reconstruction accuracy is a problem to be solved.
The Variable Step Size stagewise Adaptive Matching Pursuit (VSStAMP) algorithm proposed in the literature identifies the stage of variable step size by judging the number of candidate atomic sets, and i

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  • OFDM sparse channel estimation method based on adaptive compressed sensing
  • OFDM sparse channel estimation method based on adaptive compressed sensing
  • OFDM sparse channel estimation method based on adaptive compressed sensing

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[0029] The present invention proposes a new OFDM sparse channel estimation method based on sparsity adaptive compressed sensing, which mainly includes two parts: a system model and an adaptive compressed sensing channel estimation algorithm solution. The specific implementation process of these two parts will be introduced below, and the performance advantages of the sparsity adaptive channel estimation method proposed by the present invention in channel estimation will be proved through simulation experiments.

[0030] The OFDM sparse channel estimation method based on sparsity adaptive compressed sensing comprises the following steps:

[0031] Step (1): Model the OFDM channel estimation problem as the following compressed sensing signal reconstruction problem:

[0032] the y P =X P W P h+n P =Ah+n P

[0033] where: y P =Sy is a P×1 dimension receiving pilot vector, P is the number of pilots, S matrix is ​​made up of P rows corresponding to pilot positions in the N×N u...

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Abstract

The invention discloses an OFDM sparse channel estimation method based on adaptive compressed sensing. The method comprises the following steps of step (1), modeling an OFDM channel estimation problemas a compressed sensing signal reconstruction problem; and step (2), solving the compressed sensing signal reconstruction problem by utilizing an adaptive compressed sensing channel estimation algorithm, and estimating an OFDM sparse channel. The method has the advantages that the OFDM sparse channel can be quickly estimated under a condition that the OFDM system channel sparsity is unknown; andcompared with an OFDM sparse channel estimation method based on an existing sparsity adaptive algorithm, the OFDM sparse channel estimation method based on the algorithm has lower channel estimation mean square error, lower system error bit rate and shorter estimation time.

Description

technical field [0001] The invention relates to the field of channel estimation of communication systems, in particular to an OFDM sparse channel estimation method based on adaptive compressed sensing. Background technique [0002] In a mobile communication system, multipath propagation causes inter-symbol interference (Inter Symbol Interference, ISI) in the received signal, which seriously affects the reliability of the system. OFDM technology is widely used in 4G mobile communications and wireless local area networks due to its strong ability to resist frequency selective fading and ISI and low complexity. In an OFDM system, the acquisition of channel state information (Channel State Information, CSI) is very important, so it is necessary to estimate the CSI. Least squares estimation, least mean square error estimation [2] Although the complexity of these pilot-based channel estimation methods is low, the pilot sequence overhead is very large, and a lot of pilots are nee...

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Application Information

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IPC IPC(8): H04L27/26
CPCH04L27/2692H04L27/2695
Inventor 何雪云费洪涛
Owner NANJING UNIV OF POSTS & TELECOMM
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