PROJECT TITLE :
Low-Complexity Bayesian Estimation of Cluster-Sparse Channels
This paper addresses the problem of channel impulse response estimation for cluster-sparse channels underneath the Bayesian estimation framework. We tend to develop a completely unique low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and therefore the structure of the measurement matrix. It is shown that, because of the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, like underwater acoustic channel impulse responses, can be partitioned into a variety of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response, and therefore the structure of the measurement matrix, all combined, end in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations which will be reused in several clusters. The reduction in computational complexity allows for a a lot of correct implementation of the MMSE estimator. The proposed approach is tested using artificial Gaussian channels, along with simulated underwater acoustic channels. Image-error-rate performance and computation time confirm the superiority of the proposed methodology compared to chose benchmark methods in systems with preamble-based mostly training signals transmitted over cluster-sparse channels.
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