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In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate...
For non-sample-spaced multipath channels, multi-path energy leakage leads to an increase in the channel sparsity and detection difficulties. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for the estimation of non-sample-spaced multipath channels. Compared with other greedy algorithms, the most innovative feature of the SAMP algorithm is its capability of signal...
In wireless orthogonal frequency division multiplexing (OFDM) systems, the knowledge of signal-to-noise ratio (SNR) plays an important role for system optimization. Most of the exiting literatures have studied the SNR estimation for perfect synchronization in additive white Gaussian noise (AWGN) channels, or in frequency selective channels. However, the realistic channels are always doubly selective,...
In this paper, a novel rapid varying doubly-selective channel estimator for OFDM systems has been proposed, in which channel is modeled by basis expansion model (BEM). Unlike traditional BEM channel estimation algorithms, in which the comb-pilot is used and the large path delay is not considered, the proposed algorithm uses firstly block-type pilot to estimate the BEM coefficients of fast-varying...
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