Channel models for ultrawideband personal area networks
IEEE Wireless Communications
Low-complexity ML timing acquisition for UWB communications in dense multipath channels
IEEE Transactions on Wireless Communications
Clustered ML Channel Estimation for Ultra-Wideband Signals
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
Characterization of ultra-wide bandwidth wireless indoor channels: a communication-theoretic view
IEEE Journal on Selected Areas in Communications
Channel estimation for ultra-wideband communications
IEEE Journal on Selected Areas in Communications
Improved channel estimation based on compressed sensing for pulse ultrawideband communication system
WASA'10 Proceedings of the 5th international conference on Wireless algorithms, systems, and applications
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One of the most attractive features of ultra-wideband impulse radio is the collection of rich multipath with the transmission of ultra-short pulses. Exploiting the rich multi-path diversity with channel estimating Rake receivers enables significant energy capture, higher performance and flexibility than suboptimal receivers. Although data-aided (DA) maximum likelihood (ML) channel estimator shows a promising performance, its implementation is restricted by the Nyquist sampling criterion. The emerging theory of compressed sensing (CS) describes a novel framework to jointly compress and detect a sparse signal with fewer samples than the traditional Nyquist criterion. In this paper, we propose a CS-ML channel estimator which combines the compression framework of CS for sampling rate reduction while retaining the noise statistics formulation of ML to achieve a reliable performance. Simulation assessment indicates that, with far fewer measurements, the performance of our proposed scheme supersedes that of the l1-norm minimization estimator of CS and can be as close as the ML, but with a reduction in complexity.