Fast and Robust Parametric Estimation of Jointly Sparse Channels

We consider the joint estimation of multipath channels obtained with a set of receiving antennas and uniformly probed in the frequency domain. This scenario fits most of the modern outdoor communication protocols for mobile access or digital broadcasting among others. Such channels verify a Sparse Common Support property (SCS) which was used in a previous paper to propose a Finite Rate of Innovation (FRI) based sampling and estimation algorithm. In this contribution we improve the robustness and computational complexity aspects of this algorithm. The method is based on projection in Krylov subspaces to improve complexity and a new criterion called the Partial Effective Rank (PER) to estimate the level of sparsity to gain robustness. If P antennas measure a K-multipath channel with N uniformly sampled measurements per channel, the algorithm possesses an O(KPNlogN) complexity and an O(KPN) memory footprint instead of O(PN^3) and O(PN^2) for the direct implementation, making it suitable for K << N. The sparsity is estimated online based on the PER, and the algorithm therefore has a sense of introspection being able to relinquish sparsity if it is lacking. The estimation performances are tested on field measurements with synthetic AWGN, and the proposed algorithm outperforms non-sparse reconstruction in the medium to low SNR range (< 0dB), increasing the rate of successful symbol decodings by 1/10th in average, and 1/3rd in the best case. The experiments also show that the algorithm does not perform worse than a non-sparse estimation algorithm in non-sparse operating conditions, since it may fall-back to it if the PER criterion does not detect a sufficient level of sparsity. The algorithm is also tested against a method assuming a "discrete" sparsity model as in Compressed Sensing (CS). The conducted test indicates a trade-off between speed and accuracy.Comment: 11 pages, 9 figures, submitted to IEEE JETCAS special issue on Compressed Sensing, Sep. 201

OFDM Channel Estimation Along with Denoising Approach under Small SNR Environment using SSA

In this paper, a de-noising approach in conjunction with channel estimation (CE) algorithm for OFDM systems using singular spectrum analysis (SSA) is presented. In the proposed algorithm, the initial CE is computed with the aid of traditional linear minimum mean square error (LMMSE) algorithm, and then further channel is evaluated by considering the low rank eigenvalue approximation of channel correlation matrix related to channel using SSA. Simulation results on bit error rate (BER) revealed that the method attains an improvement of 7 dB, 5 dB and 3 dB compared to common LSE, MMSE and SVD based methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into different empirical orthogonal functions (EOFs) based on the singular values. It was established that the correlation coefficients worked well in identifying useful EOFs only up to moderate (SNR geq 12dB). For low SNR<12 dB, kurtosis was found to be a useful measure for identifying the useful EOFs. In addition to outperforming the existing methods, with this de-noising approach, the mean square error (MSE) of channel estimator is further improved approximately 1 dB more in SNR at the cost of computational complexity

Estimation of Sparse MIMO Channels with Common Support

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of SCS channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio

Spectrum control and iterative coding for high capacity multiband OFDM

The emergence of Multiband Orthogonal Frequency Division Modulation (MB-OFDM) as an ultra-wideband (UWB) technology injected new optimism in the market through realistic commercial implementation, while keeping promise of high data rates intact. However, it has also brought with it host of issues, some of which are addressed in this thesis. The thesis primarily focuses on the two issues of spectrum control and user capacity for the system currently proposed by the Multiband OFDM Alliance (MBOA). By showing that line spectra are still an issue for new modulation scheme (MB-OFDM), it proposes a mechanism of scrambling the data with an increased length linear feedback shift register (compared to the current proposal), a new set of seeds, and random phase reversion for the removal of line spectra. Following this, the thesis considers a technique for increasing the user capacity of the current MB-OFDM system to meet the needs of future wireless systems, through an adaptive multiuser synchronous coded transmission scheme. This involves real time iterative generation of user codes, which are generated over time and frequency leading to increased capacity. With the assumption of complete channel state information (CSI) at the receiver, an iterative MMSE algorithm is used which involves replacement of each users s signature with its normalized MMSE filter function allowing the overall Total Squared Correlation (TSC) of the system to decrease until the algorithm converges to a fixed set of signature vectors. This allows the system to be overloaded and user\u27s codes to be quasi-orthogonal. Simulation results show that for code of length nine (spread over three frequency bands and three time slots), ten users can be accommodated for a given QoS and with addition of single frequency sub-band which allows the code length to increase from nine to twelve (four frequency sub-bands and three time slots), fourteen users with nearly same QoS can be accommodated in the system. This communication is overlooked by a central controller with necessary functionalities to facilitate the process. The thesis essentially considers the uplink from transmitting devices to this central controller. Furthermore, analysis of this coded transmission in presence of interference is carried to display the robustness of this scheme through its adaptation by incorporating knowledge of existing Narrowband (NB) Interference for computing the codes. This allows operation of sub-band coexisting with NB interference without substantial degradation given reasonable interference energy (SIR=-l0dB and -5dB considered). Finally, the thesis looks at design implementation and convergence issues related to code vector generation whereby, use of Lanczos algorithm is considered for simpler design and faster convergence. The algorithm can be either used to simplify design implementation by providing simplified solution to Weiner Hopf equation (without requiring inverse of correlation matrix) over Krylov subspace or can be used to expedite convergence by updating the signature sequence with eigenvector corresponding to the least eigenvalue of the signature correlation matrix through reduced rank eigen subspace search