superimposed training-based channel estimation for mimo relay networks

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ID: 205047
2012
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Ranked #64 of 304 articles by views in american journal of physiology endocrinology and metabolism

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Abstract
We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO) amplify-and-forward (AF) one-way relay network (OWRN) to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for the estimation of two sets of flat-fading MIMO channel under random channel parameters and further exploited to design the optimal training vectors. A specific suboptimal channel estimation algorithm is applied in the MIMO AF OWRN using the optimal training sequences, and the normalized mean square error performance for the estimation is provided to verify the Bayesian CRLB results.
Reference Key
xu2012internationalsuperimposed Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xiaoyan Xu;Jianjun Wu;Shubo Ren;Lingyang Song;Haige Xiang
Journal american journal of physiology endocrinology and metabolism
Year 2012
DOI
10.1155/2012/698748
URL
http://dx.doi.org/10.1155/2012/698748
Keywords
electrical engineering electronics wireless telephone industry nuclear engineeringcellular telephone services industry

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