30 research outputs found
Estimation of Symmetric Channels for Discrete Cosine Transform Type-I Multicarrier Systems: A Compressed Sensing Approach
The problem of channel estimation for multicarrier communications is addressed. We focus on systems employing the Discrete Cosine Transform Type-I (DCT1) even at both the transmitter and the receiver, presenting an algorithm which achieves an accurate estimation of symmetric channel filters using only a small number of training symbols. The solution is obtained by using either matrix inversion or compressed sensing algorithms. We provide the theoretical results which guarantee the validity of the proposed technique for the DCT1. Numerical simulations illustrate the good behaviour of the proposed algorithm
Ultra Wideband
Ultra wideband (UWB) has advanced and merged as a technology, and many more people are aware of the potential for this exciting technology. The current UWB field is changing rapidly with new techniques and ideas where several issues are involved in developing the systems. Among UWB system design, the UWB RF transceiver and UWB antenna are the key components. Recently, a considerable amount of researches has been devoted to the development of the UWB RF transceiver and antenna for its enabling high data transmission rates and low power consumption. Our book attempts to present current and emerging trends in-research and development of UWB systems as well as future expectations
Structured Compressed Sensing Using Deterministic Sequences
The problem of estimating sparse signals based on incomplete set of noiseless or
noisy measurements has been investigated for a long time from different perspec-
tives. In this dissertation, after the review of the theory of compressed sensing (CS)
and existing structured sensing matrices, a new class of convolutional sensing matri-
ces based on deterministic sequences are developed in the first part. The proposed
matrices can achieve a near optimal bound with O(K log(N)) measurements for
non-uniform recovery. Not only are they able to approximate compressible signals
in the time domain, but they can also recover sparse signals in the frequency and
discrete cosine transform domain. The candidates of the deterministic sequences
include maximum length sequence (or called m-sequence), Golay's complementary
sequence and Legendre sequence etc., which will be investigated respectively. In
the second part, Golay-paired Hadamard matrices are introduced as structured
sensing matrices, which are constructed from the Hadamard matrix, followed by
diagonal Golay sequences. The properties and performances are analyzed in the
following. Their strong structures ensure special isometry properties, and make
them be easier applicable to hardware potentially. Finally, we exploit novel CS
principles successfully in a few real applications, including radar imaging and dis-
tributed source coding. The performance and the effectiveness of each scenario are verified in both theory and simulations