Browsing by Author "Poor, H. V."

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Sparse channel estimation and equalization for OFDM-based underwater cooperative systems with amplify-and-forward relaying
(IEEE, 2016-01-01) Panayırcı, E.; Şenol, H.; Uysal, Murat; Poor, H. V.; Electrical & Electronics Engineering; UYSAL, Murat
This paper is concerned with a challenging problem of channel estimation and equalization for amplifyand- forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in sparse underwater acoustic (UWA) channels. The sparseness of the channel impulse response and prior information for the non-Gaussian channel gains, modeled by an exact continuous Gaussian mixture (CGM), are exploited to improve the performance of the channel estimation algorithm. The resulting novel algorithm initially estimates the overall sparse complex-valued channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The effective time-domain non- Gaussian noise is approximated well as a Gaussian noise in the frequency-domain, where the estimation takes place. An efficient and low complexity algorithm is developed based on the combinations of the MP and the maximum a posteriori probability (MAP) based space-alternating generalized expectationmaximization technique, to improve the estimates of the channel taps and their locations in an iterative way. Computer simulations show that the UWA channel is estimated very effectively and the proposed algorithm exhibits excellent symbol error rate and channel estimation performance.
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Sparse channel estimation for OFDM-based underwater acoustic systems in rician fading with a new OMP-MAP algorithm
(IEEE, 2019-03-15) Panayirci, E.; Altabbaa, M. T.; Uysal, Murat; Poor, H. V.; Electrical & Electronics Engineering; UYSAL, Murat
In this paper, a new channel estimation algorithm is proposed that exploits channel sparsity in the time domain for an orthogonal frequency division multiplexing (OFDM)-based underwater acoustical (UWA) communications systems in the presence of Rician fading. A path-based channel model is used, in which the channel is described by a limited number of paths, each characterized by a delay, Doppler scale, and attenuation factor. The resulting algorithm initially estimates the overall sparse channel tap delays and Doppler shifts using a compressed sensing approach, in the form of the orthogonal matching pursuit (OMP) algorithm. Then, a computationally efficient and novel channel estimation algorithm is developed by combining the OMP and maximum a posteriori probability (MAP) techniques for estimating the sparse complex channel path gains whose prior densities have complex Gaussian distributions with unknown mean and variance vectors, where a computationally efficient maximum likelihood algorithm is proposed for their estimation. Monte Carlo simulation results show that the mean square error and symbol error rate performances of the OMP-MAP algorithm uniformly outperforms the conventional OMP-based channel estimation algorithm, in case of uncoded OFDM-based UWA communications systems.