Low SNR Conditions Research Articles

Channel Independent Precoder for OFDM-Based Systems Over Fading Channels

In this paper we propose an independent channel precoder for orthogonal frequency division multiplexing (OFDM) systems over fading channels. The design of the precoder is based on the information redistribution of the input modulated symbols among the output precoded symbols. The proposed precoder decreases the variance of the instantaneous noise power at the receiver produced by the channel variability. The employment of an interleaver together with a precoding matrix whose size does not depend on the number of data carriers in an OFDM symbol allows different configurations of time-frequency diversity which can be easily adapted to the channel conditions. The precoder is evaluated with a modified Zero Forcing (ZF) equalizer whose maximum gain is constrained by means of a clipping factor. Thus, the clipping factor limits the noise power transfer in the receiver deprecoding block in low SNR conditions.

Level set fiber bundle segmentation using spherical harmonic coefficients

Classifying brain white matter fibers into bundles is of growing interest in neuroscience. Quantification of diffusion characteristics inside a fiber bundle provides new insights for disease evolutions, therapy effects, and surgical interventions. In this paper, we present a novel method for segmenting fiber bundles using spherical harmonic coefficients (SHC) that describe diffusion signal obtained from High Angular Resolution Diffusion Imaging (HARDI) protocols. Based on SHC, we define a similarity measure and use it as a speed function term in level set framework. We show advantages of the proposed measure over similarity measures based on Diffusion Tensor Imaging (DTI) indices. Without any assumptions about diffusion model, we deal with diffusion signal instead of orientation distribution function (ODF) calculated using complicated mathematics, inaccurate simplifications, and time-consuming implementation. By applying the proposed algorithm on synthetic data, its superior accuracy and robustness in low SNR conditions are shown. Application of the proposed method on real HARDI MRI data also illustrates its superior performance, especially in heterogeneous diffusion areas with low traditional diffusion anisotropies.

DOA estimation based on fourth-order cumulants with unknown mutual coupling

Most of the existing direction of arrival (DOA) estimation algorithms with mutual coupling are generally based on the second-order statistics while assuming that the noise is additive white Gaussian noise. In this paper, a method based on fourth-order cumulants (FOC) is proposed for DOA estimation with uniform linear array (ULA) in the presence of unknown mutual coupling. The method can be applied when non-Gaussian signals coexist with unknown colored Gaussian noise. The unknown mutual coupling can be blindly compensated by the inherent mechanism of the proposed method and the DOA of signals can be accurately estimated without any calibration sources. Since we use the FOC, the Gaussian noise is greatly suppressed and we can obtain a good estimation performance in low SNR condition. Simulation results demonstrate the effectiveness of our method.

Downlink Cooperative Wireless Spatial Multiplexing System

We investigate and propose the utilization of regenerative and non-regenerative relaying terminals in downlink cooperative MIMO communications, such as in base-station/router-relay-user transmission under different schemes. The source is equipped with multiple antennas, while the relays and destination are single-antenna terminals. From the source to the relays, symbols are transmitted using MIMO spatial-multiplexing technique. Depending on the type of relaying scheme, the relays either fully decode or amplify the received signal before retransmitting it to the destination using simple TDM transmission or Alamouti's space-time coding. We show that the proposed system realizes MIMO performance in single-antenna system environment, and performance-wise it is superior to existing transmission schemes, especially in low-SNR conditions. Furthermore, the proposed system is shown to give a diversity order of N - M + 1, similar to that of MIMO V-BLAST system.

A Novel Cooperative Relaying Network Scheme with Inter-Relay Data Exchange

In this paper, we propose a novel scheme of cooperative relaying network based on data exchange between relays before forwarding their received data to destination. This inter-relay data exchange step is done during an additional middle-slot in order to enhance the transmit signals from relays to the destination under low transmit power condition. To reduce the propagation errors between relays as well as the required transmit power during this data exchange, only the relay possessing the highest SNR is engaged into exchanging data by forwarding its received signal to the other relays. As for the remaining non-selected relays, i.e. with low SNR, the transmitted signal is estimated by using both signals received separately at different time slots (i.e., 1st and 2nd slot) from source and the 'best' selected relay, respectively, emulating virtual antenna array where appropriate weights for the antenna array are developed. In addition, we investigate distributed transmit beamforming and maximum ratio combining at the relays and the destination, respectively, to combine coherently the received signals. At the relay optimal location and for low SNR condition, the proposed method has significant better outage behavior and average throughput than conventional methods using one or two time slots for transmission.

Second and fourth order statistics-based reduced polynomial rooting direction finding algorithms

Polynomial rooting direction finding (DF) algorithms are a computationally efficient alternative to search-based DF algorithms and are particularly suitable for uniform linear arrays (ULA) of physically identical elements provided mutual interaction among the array elements can be either neglected or compensated for. A popular polynomial rooting algorithm is Root-MUSIC (RM) wherein, for an N-element array, the estimation of the directions of arrivals (DOA) requires the computation of the roots of a 2 N−2-order polynomial for a second order (SO) statistics- and a 4 N−4-order polynomial for a fourth order (FO) statistics-based approach, wherein the DOA are estimated from L pairs of roots closest to the unit circle, when L signals are incident on the array. We derive SO- and FO statistics reduced polynomial rooting (RPR) algorithms capable to estimate L DOA from L roots only. We demonstrate numerically that the RPR algorithms are at least as accurate as the RM algorithms. Simplified algebraic structure of RPR algorithms leads to better performance than afforded by RM algorithms in saturated array environment, especially in the case of FO methods when number of incident signals exceeds number of elements and under low SNR and/or small sample size conditions.

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

Energy-based VAD with grey magnitude spectral subtraction

In this paper, we propose a novel voice activity detection (VAD) scheme for low SNR conditions with additive white noise. The proposed approach consists of two parts. First, a grey magnitude spectral subtraction (GMSS) is applied to remove additive noise from a given noisy speech. By this doing, an estimated clean speech is obtained. Second, the enhanced speech by the GMSS is segmented and put into an energy-based VAD to determine whether it is a speech or non-speech segment. The approach presented in this paper is called the GMSS/EVAD. Simulation results indicate that the proposed GMSS/EVAD outperforms VAD in G.729 and GSM AMR for the given low SNR examples. To investigate the performance of the GMSS/EVAD for real-life background noises, the babble and volvo noises in the NOISEX-92 database are under consideration. The simulation results for the given examples indicate that the GMSS/EVAD is able to handle appropriately for the cases of the babble noise with the SNR above 10 dB and the cases of the volvo noise with SNR 15 dB and up.

ML iterative soft-decision-directed (ML-ISDD): a carrier synchronization system for short packet turbo coded communication

Modern communication systems are required to provide services based on high data rates burst-mode packet-data transmission, capable of operating at very low SNR conditions. Turbo codes enable the operation at low SNR, close to the Shannon limit. However, carrier frequency and phase synchronization, needed for optimal coherent performance of the receiver, still remains a problem in low SNR and short bursts conditions. This paper proposes a new carrier synchronization method, the Maximum-Likelihood Iterative-Soft-Decision-Directed (ML-ISDD), which uses the turbo-decoder soft decisions to improve the carrier synchronization performance at low SNR values. The ML-ISDD method operates iteratively and jointly with the turbo decoder, enhancing both the turbo-decoder and the synchronization performance. The ML-ISDD method has been shown by simulation to significantly increase the allowed initial frequency and phase uncertainty region, thus allowing the use of very short training sequences for initial carrier synchronization.

Parameter Adjustment for a Dynamic Programming Track-before-Detect-Based Target Detection Algorithm

This paper addresses the problem of tracking a dim moving point target in a sequence of IR images. The proposed tracking system, based on the track-before-detect (TBD) approach, is designed to track and detect dim maneuvering targets froman image sequence under low SNR conditions; a dynamic programming algorithm (DPA) is used to process the frames in the sequence. This paper deals with the practical issues of setting the parameters of our algorithm to maximize tracking capability. In our flexible approach, we are able to accommodate different levels of noise and different target velocities and mobilities. Our algorithm is tested on real data to determine its efficacy.

A new MMSE channel estimation algorithm for OFDM systems

In this report, we propose a new MMSE channel estimation algorithm for OFDM systems. It is well-known that time domain maximum likelihood estimators (MSEs) show highly accurate impulse response estimation by using the time domain long preamble of the OFDM frame. On the other hand, the impulse response estimation based on the minimum mean square error (MMSE) criterion achieves superior channel estimation in low SNR conditions; however, it requires prior statistical information such as delay profiles of channels. In addition, the computational complexity becomes large because of the inverse matrix calculation. In order to overcome these issues, we propose to estimate the power delay profile of the channel by using the MLE, and to suppress the increase of the computational complexity for the matrix inverse calculation by applying the conjugate gradient method without degradation of the PER performance.

Robust Signal Subspace Speech Classifier

A speech model inspired by the signal subspace approach was recently proposed as a speech classifier with modest results. The method entails, in general, the assemblage of a set of subspace trajectories that consist of the right singular vectors of measurement matrices of the signal under consideration. Given an unknown signal, a simple distortion measure then applies in the classification procedure to pick the best matched class prototype. This letter examines the issue of robustness in the subspace classification scheme. Borrowing an important result on noisy measurement matrices, this letter formally establishes the notion of robustness in subspace classification and proceeds to propose a class of robust distortion measures for signal subspace models. Simulation results of subspace classifiers implementing the new distortion measures in an isolated digit speech recognition problem reveal no degradation in recognition accuracy, even under low SNR conditions.

Interference Excision in Spread Spectrum Communications Using Adaptive Positive Time-Frequency Analysis

This paper introduces a novel algorithm to excise single and multicomponent chirp-like interferences in direct sequence spread spectrum (DSSS) communications. The excision algorithm consists of two stages: adaptive signal decomposition stage and directional element detection stage based on the Hough-Radon transform (HRT). Initially, the received spread spectrum signal is decomposed into its time-frequency (TF) functions using an adaptive signal decomposition algorithm, and the resulting TF functions are mapped onto the TF plane. We then use a line detection algorithm based on the HRT that operates on the image of the TF plane and detects energy varying directional elements that satisfy a parametric constraint. Interference is modeled by reconstructing the corresponding TF functions detected by the HRT, and subtracted from the received signal. The proposed technique has two main advantages: (i) it localizes the interferences on the TF plane with no cross-terms, thus facilitating simple filtering techniques based on thresholding of the TF functions, and is an efficient way to excise the interference; (ii) it can be used for the detection of any directional interferences that can be parameterized. Simulation results with synthetic models have shown successful performance with linear and quadratic chirp interferences for single and multicomponent interference cases. The proposed method excises the interference even under very low SNR conditions of dB, and the technique could be easily extended to any interferences that could be represented by a parametric equation in the TF plane.

On-line Gaussian mixture modeling in the log-power domain for signal-to-noise ratio estimation and speech enhancement

We present on-line Gaussian mixture modeling (GMM) in the log-power domain of actual noisy speech and its applications to segmental signal-to-noise ratio (SNR) estimation and speech enhancement. The basic idea in this method is the use of conventional two-component GMM modeling in the log-power domain to estimate the distributions of noise and noisy speech subspaces in each speech segment of a length of 0.5–2 s. Given the subspace distributions, the statistical estimation method is adopted in the applications. For the segmental SNR estimation, the average speech level is estimated from noisy speech using a nonlinear moment of modeled distributions. This method is suitable under real conditions, when neither reference signals nor speech activity is available, and is shown to be more robust and accurate than conventional methods, particularly under low-SNR conditions. The proposed GMM model is extended to the multiband log-power domains for noise estimation. We use long-term information, which is obtained by GMM modeling in each segment of 0.5 s, to update the local distributions of noise and noisy speech power at each actual time–frequency index. The cumulative distribution function equalization (CDFE) is then used to estimate the noise and subtract it from the noisy speech power. The advantage of the CDFE method for noise estimation is that the estimation is given in the logarithmic domain without any approximation. The proposed speech enhancement is tested using the AURORA-2J database. We also compare the proposed method to the conventional minimum statistic and quantile-based noise estimation. The proposed method is found to be superior to the conventional in the speech recognition rate over most noise environments and shown to provide very good compromise between speech enhancement and speech recognition performance.

TCP performance over wireless MIMO channels with ARQ and packet combining

Multiple-input multiple-output (MIMO) wireless communication systems that employ multiple transmit and receive antennas can provide very high-rate data transmissions without increase in bandwidth or transmit power. For this reason, MIMO technologies are considered as a key ingredient in the next generation wireless systems, where provision of reliable data services for TCP/IP applications such as wireless multimedia or Internet is of extreme importance. However, while the performance of TCP has been extensively studied over different wireless links, little attention has been paid to the impact of MIMO systems on TCP. This paper provides an investigation on the performance of modern TCP systems when used over wireless channels that employ MIMO technologies. In particular, we focus on two representative categories of MIMO systems, namely, the BLAST systems and the space-time block coding (STBC) systems, and how the ARQ and packet combining techniques impact on the overall TCP performance. We show that, from the TCP throughput standpoint, a more reliable channel may be preferred over a higher spectral efficient but less reliable channel, especially under low SNR conditions. We also study the effect of antenna correlation on the TCP throughput under various conditions.

Robust decision-feedback equalization with a noncausal filter for ATSC DTV receivers

A robust decision-feedback equalization method for ATSC DTV receivers is presented. Under severe channels, such as 0-dB ghost or a distributed transmission environment, decision-feedback equalizers (DFEs), which have been commonly used in ATSC DTV receivers, suffer from error propagation. To improve the robustness to the error propagation, we incorporate a channel-matched filter (CMF) and an intelligent sheer (IS). The use of the CMF prior to the DFE makes severe channels manageable in the DFE, The IS, a trellis decoder with a trace-back depth of 1, enhances a correct-decision probability in the DFE, especially under low SNR conditions. Adoption of the CMF and the IS clearly enhances the robustness of the DFE but may degrade the output SNR due to unwanted pre-ghosts caused by channel-matched filtering. We employ a noncausal all-pass filter operating in reversed time to preserve the robustness acquired by the CMF and the IS without the degradation of the output SNR. The noncausal filter changes an input channel with pre-ghosts into an approximately minimum-phase channel without pre-ghosts. Simulation results show that the proposed equalization scheme achieves the more stable convergence and the higher SNRs than the existing DFEs.

Bifurcation analysis and control in Turbo decoding algorithm

Turbo Codes can approach the Shannon limit very closely with the help of its special iterative decoding algorithm. This paper establishes a nonlinear dynamic system to analyze the relationship between Turbo decoding output and the number of iterations. Here, the number of iterations is taken as the time axis, decoding output as the state variable, SNR and information bits N as system parameters. It is shown that with SNR increasing, the decoding algorithm undergoes three stages, namely the indecisive fix-point, singular region and unequivocal fix-point. Bifurcations occur during the transformation from the indecisive fix-point to the singular region. It is first proposed that fold, period doubling and Neimark-Sacker bifurcation all have the possibility to occur, depending on the value of N. In the singular region, phase trajectories may appear as period-two, period-three, quasiperiod and chaos. This paper first observed and confirmed the existence of period-three and chaos. Singular region deteriorates the performance of Turbo codes under low SNR. This paper proposes a time-delay feedback control method to stablize the fix-point. Simulation results show that this method achieves 0.1—0.3 dB improvement for Turbo codes under low SNR condition.

Adaptive equalization of time-varying MIMO channels

This paper addresses the problem of adaptive multiple-input multiple-output (MIMO) equalization of time-varying channels (TVC). The proposed technique is based on Kalman filtering and decision feedback equalization. The time-varying channels can be estimated and tracked using a Kalman filter type of algorithm. An equalization technique for MIMO channels is derived based on a novel decision feedback equalizer structure. Moreover, we present a method to estimate the measurement and state noise variances used by the Kalman filter because they are crucial parameters in obtaining a reliable performance. The performance of the algorithm is investigated in simulations using realistic channels generated based on the COST 207 model. The results show that the algorithm performs well even in low SNR conditions or in difficult channel conditions.

Negentropy based voice-activity detection for noise estimation in very low SNR condition

The spectral organization in a noisy speech is different during noise-only portions and speech portions. Such chaotic difference can be tracked by using negentropy as a measure. Accordingly, a Voice Activity Detector (VAD) based on the negentropy measure of magnitude of spectral components of the pseudo-stationary segments of speech is proposed and has been used for the estimation of noise statistics from the noisy speech signal in very low SNR conditions. The results show the effectiveness and usefulness of the proposed method under very noisy conditions.

Technique for target detection and rangingbased on broadband LPI radars

A processing technique for improving the ranging and detection capabilities of LPI radars and the measured performance of an experimental system are presented. The precision of the measured distance is very high even under low SNR conditions. The associated detection algorithm also exhibits good performance in noisy environments.