Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Fundamentals of wireless communication
Fundamentals of wireless communication
Acoustic propagation considerations for underwater acoustic communications network development
ACM SIGMOBILE Mobile Computing and Communications Review
New algorithms for designing unimodular sequences with good correlation properties
IEEE Transactions on Signal Processing
Construction of unimodular sequence sets for periodic correlations
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Signal Synthesis and Receiver Design for MIMO Radar Imaging
IEEE Transactions on Signal Processing - Part II
Multiuser detection in a horizontal underwater acoustic channelusing array observations
IEEE Transactions on Signal Processing
Cooperative positioning in underwater sensor networks
IEEE Transactions on Signal Processing
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Effective training sequences and reliable channel estimation algorithms are essential for enhancing the performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). Also, effective interference cancellation schemes are crucial for reliable symbol detection. In this paper, the problem of designing MIMO training sequences is considered. Moreover, we present a sparse learning via iterative minimization (SLIM) algorithm for enhanced channel estimation and reduced computational complexity. Furthermore, RELAXBLAST, a linear minimum mean-squared error based symbol detection scheme, is implemented efficiently by exploiting the conjugate gradient method and diagonalization properties of circulant matrices. The proposed MIMO UAC techniques are evaluated using both simulated and experimental examples.