Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting inter-symbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, e.g., orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which can not only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The propose method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that propose method can improve the estimation performance when comparing with conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa

UNDERDETERMINED SIGNAL REPRESENTATION VIA LINEAR PROJECTIONS USING BINARY SPARSE MATRICES- SIGNAL COMPRESSION

This paper presents and studies analytically a new compressive sensing (CS) approach with the aim of bringing this technique closer to successful commercialization in image sensor circuits. Unlike existing CS techniques that use random measurement matrices (RMM) to encode a signal given in form of a vector of discrete samples, the proposed technique utilizes carefully chosen custom measurement matrices. In CS measurement operation, RMM are often used to achieve small coherence between the measurement matrix and the sparse representation bases. However, when applied in practice, RMM based CS designs typically lead to complicated hardware design and thus have a large circuit overhead to obtain random summations. The proposed custom measurement matrix achieves about the same level of incoherence as the RMMs, but results in a dramatically simplified CS measurement circuit, improving both energy efficiency and circuit scalability, and thus the attractiveness of this technique for industrial commercialization. The proposed method is evaluated analytically in terms of Peak Signal to Noise Ratio (PSNR), a measure for the quality of the reconstructed compared to the original signal. Matlab simulations are also conducted to evaluate the effectiveness of the proposed technique, and to compare simulated and estimated PSNRs. Finally, the proposed technique is extended to two-dimensional projections with the aim of further improving signal quality, in particular with high compression rates. A newly formulated minimization problem is proposed to combine the projections in both dimensions to a single optimization problem