Partial sparse multi-path channel estimation using &amp;#x2113;&lt;inf&gt;1&lt;/inf&gt;-regularized LS algorithm

Abstract-Accurate channel state information (CSI) is necessary for coherent detection in amplify and forward (AF) broadband cooperative communication systems. Based on the assumption of ordinary sparse channel, efficient sparse channel estimation methods have been investigated in our previous works. However, when the cooperative channel exhibits partial sparse structure rather than ordinary sparsity, our previous method cannot take advantage of the prior information. In this paper, we propose an improved channel estimation method with partial sparse constraint on cooperative channel. At first, we formulate channel estimation as a compressive sensing problem and utilize sparse decomposition theory. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over ordinary sparse channel estimation methods.

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Improved channel estimation with partial sparse constraint for AF cooperative communication systems

Gui

Peng

Adachi

2012

2012 18th Asia-Pacific Conference on Communications (APCC)

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Indirect Channel Sensing for Cognitive Amplify-and-Forward Relay Networks

Liu¹,

Wan²

2011

Preprint

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Partial sparse multi-path channel estimation using ℓ<inf>1</inf>-regularized LS algorithm

Cited by 2 publications

References 8 publications

Improved channel estimation with partial sparse constraint for AF cooperative communication systems

Improved channel estimation with partial sparse constraint for AF cooperative communication systems

Indirect Channel Sensing for Cognitive Amplify-and-Forward Relay Networks

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