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UWB wireless channels are a prime example of long and sparse channel impulse response (CIR).
In this paper, the problem of inter symbol interference (ISI) sparse channel estimation in wireless communication with the application of compressed sensing is investigated.
Real-time simulation results clearly show that the ISL0 algorithm can estimate the ISI sparse channel much better in both signal noise ratio (SNR) and compression levels.
However, they never considered any application on sparse channel estimation.
WWCB for the correlated time varying sparse channel is illustrated in Fig. 10.
Section 3 discusses various adaptive sparse channel estimation methods using different LMS-based algorithms.
We find that iterative receiver processing including sparse channel estimation leads to impressive performance gains.
We can use MP, OMP, and BP to estimate the sparse channel using the training sequence.
In Section 2, the overall system model is described, and the sparse channel model is presented.
The Bayesian learning channel estimation methods have been developed to reconstruct the sparse channel.
Assume that an input training signal x t) is used to probe the unknown sparse channel.
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