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A low complexity iterative channel estimation and equalisation scheme for (data-dependent) superimposed training

By S.M.A. Moosvi, D.C. McLernon, E. Alameda-Hernandez, A.G. Orozco-Lugo and M.M. Lara


Channel estimation/symbol detection methods based on superimposed training (ST) are known to bemore bandwidth efficient than those based on traditional time-multiplexed training. In this paper we present an iterative version of the ST methodwhere the equalised symbols obtained via ST are used in a second step to improve the channel estimation, approaching the performance of the more recent (and improved) data dependent ST (DDST), but now with less complexity. This iterative ST method (IST) is then compared to a different iterative superimposed training method of Meng and Tugnait (LSST).We show via simulations that the BER of our IST algorithm is very close to that of the LSST but with a reduced computational burden of the order of the channel length. Furthermore, if the LSST iterative approach (originally based on ST) is now implemented using DDST, a faster convergence rate can be achieved for the MSE of the channel estimates

Year: 2006
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