M.G. Zia (Iraq)
OFDM, adaptive filters, channel estimation
Superimposed pilot aided channel estimation attracts interest due to its potential spectral efficiency. Unfortunately, its channel estimation performance is affected by the embedded data. To mitigate the embedded data effects, adaptive filtering based channel estimation with superimposed training is proposed for orthogonal frequency division multiplexing (OFDM) system. The performance of four different adaptive algorithms is evaluated and compared through simulation. As demonstrated in simulations, the proposed scheme with recursive least squares (RLS) algorithm achieves better performance among the other considered algorithms.
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