High Signal-to-noise Ratio Cases Research Articles

Robust Adaptive Beamforming based on Automatic Variable Loading in Array Antenna

Diagonal loading technology is widely used in array antenna beamforming because of its simple method, low computational complexity and the ability to improve the robustness of beamformer. On this basis, this paper proposes a robust adaptive beamforming method based on automatic variable loading technology. The automatic variable loading matrix (AVLM) of the method is composed of two parts. The non-uniform loading matrix dominants when the input signal-to-noise ratio (SNR) is small, effectively control the influence of noise disturbance without affecting the ability of array antenna to suppress interference. The variable diagonal loading matrix dominants when the input SNR is high to improve the output performance of array antenna. Simulated results show that compared to other methods, the proposed method has better output performance for both low and high input SNR cases.

Closed expression of source signal's DOA information in sensor array

In this study, a novel closed expression of direction of arrival (DOA) information (DI) in a single-source scenario is proposed, which is more convenient than the theoretical expression to evaluate the performance of DOA estimation. Firstly, the authors give a uniform linear array system model, where the DI is expressed as the mutual information between the source and the received signal with complex additive white Gaussian noise. Then the posterior probability density function of DOA is deduced. Moreover, by dividing the integral interval into a signal part and a noise part, a novel closed approximate expression of DI is derived. It contains the posteriori entropy in low and high signal-to-noise ratio (SNR) cases and the normalised probability. The normalised probability is obtained through a normalisation procedure on the basis of the correlation functions. As special cases, the closed expression of DI in low and high SNR cases is given and some interesting relationships between physical quantities are revealed. Finally, numerical results are presented to verify the effectiveness of the proposed approximate closed expression.

Comparison of Geometrically Shaped 32-QAM and Probabilistically Shaped 32-QAM in a Bandwidth-Limited IM-DD System

We use the generalized pair-wise optimization (PO) algorithm to optimize the geometrically shaped 32-ary quadrature amplitude modulation (GS-32-QAM) constellation and adopt the probabilistic fold shaping (PFS) scheme to generate the probabilistically shaped 32-QAM (PS-32-QAM) format. We present a detailed comparison among the application of GS-32-QAM, PS-32-QAM and uniform 32-QAM in an intensity modulation/direct detection (IM/DD) system with a bandwidth-limited digital-to-analog converter (DAC). We experimentally demonstrate the 32-QAM discrete Fourier transform-spread (DFT-S) discrete multi-tone (DMT) signal transmission over 1-km standard single mode fiber (SSMF) in an IM/DD system with a net data rate of 108.29-Gb/s/λ, and achieve 0.6 dB and 0.9 dB receiver sensitivity gain using PS and GS constellations, respectively. In the high signal to noise ratio (SNR) case with a net bit rate of 69.61-Gb/s/λ, the PS-32-QAM DMT signal can provide the receiver sensitivity gain of 0.5 dB while the receiver sensitivity gain of the GS-32-QAM DMT signal is 0.4 dB, compared to the uniform 32-QAM DMT signal. Although PS-32-QAM achieves better receiver sensitivity gain than GS-32-QAM in the high SNR case, the proposed GS-32-QAM format outperforms PS-32-QAM in the low SNR case.

Detection performance analysis of hybrid MIMO radar

This study focuses on the detection performance analysis of a notional hybrid MIMO radar, which can be seen as a combination of the phased-array and distributed MIMO radars. The signal model of the proposed hybrid MIMO radar is re-derived using the Rayleigh target model. In the sense of Neyman–Person principle, the optimal detector is developed. Then, the detection performance bound is derived based on the relative entropy. Theoretical derivation and numerical examples show that, for most common signal-to-noise ratio (SNR) cases, the detection performance of the hybrid MIMO is better than the conventional phased-array and MIMO radars. Furthermore, for the high SNR case, the proposed system configuration with larger number of sub-arrays performs better detections, while in the low SNR case, that with smaller number of sub-arrays performs better detections.

New fast multi‐user beam training scheme based on compressed sensing theory for millimetre‐wave communication

Compensating for the considerable propagation loss, millimetre-wave communication systems adopt large antenna arrays and beamforming technologies to obtain high antenna gains. The beamforming training is rather time-consuming especially in multi-user cases. To reduce the training overhead, a new fast multi-user training scheme based on Compressed Sensing theory is proposed. This scheme includes a multi-user training algorithm and a corresponding semi-fixed semi-random codebook design strategy. The base station (BS) simultaneously broadcasts the training pilots to all user equipments (UEs). Then all UEs independently conduct the proposed training algorithm from channel responses. Finally, all UEs send back the training results to the BS. Compared to the exhaustive search algorithm's training overhead ξ = K r K t , the cost of the proposed scheme is only O ( ξ ) = log ⁡ ( K r K t ) , where K r and K t denote the number of beam patterns in the receiver and the transmitter. Simulation results show that the proposed scheme achieves excellent performance in high SNR (signal-to-noise ratio) cases with only 3 − 5 % performance difference from the theoretical bound. And in contrast with other Compressed Sensing schemes with random codebooks, the proposed scheme reduces the sensitivity to transmission SNR and improves approximately 3 dB performance in low SNR cases.

Multiuser precoding scheme and achievable rate analysis for massive MIMO system

We analyze the downlink multiuser precoding of massive multiple input multiple output (MIMO) system, where the base station (BS) has ideal channel state information (CSI) and adopts three types of different linear precoding schemes, i.e., maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean squared error (MMSE). Under a Rayleigh fading channel, we attain the exact expressions on the achievable rate for these three precoding schemes. Moreover, we provide several insights on the achievable rates and reveal the relation of the number of BS antennas, the number of users, and the input signal-to-noise ratio (SNR) with the achievable rates respectively. It is found in general that the achievable rate increases with the number of BS antennas and the input SNR. To be more specific, the MRT precoding scheme is much inferior to the ZF and MMSE precoding schemes and tends to be at a fixed rate at the high SNR case. On the contrary, the MRT precoding scheme outperforms ZF precoding schemes at the low SNR case. Moreover, the total achievable rate always does not increase with the number of users and the optimal number of users always exists for the ZF and MMSE precoding schemes.

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

Abstract A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

Improved Wideband DOA Estimation Using Modified TOPS (mTOPS) Algorithm

Test of orthogonality of projected subspaces (TOPS) estimates directions of arrival of wideband sources by exploiting orthogonality between signal and noise subspaces in spectral domain. TOPS performs well at mid signal-to-noise ratio (SNR) range, but fares poorly at high SNR and noise-free cases. The TOPS pseudospectrum often exhibits spurious peaks at all SNR levels. This paper attempts to explain the cause of poor performance, and proposes suitable modifications to extend the effectiveness of TOPS from low SNR to noise-free case. The proposed modified-TOPS (mTOPS) achieves reduction in spurious peaks by incorporating signal-subspace projection instead of null-space projection used in TOPS. It also uses trace as performance metric, instead of loss of rank used in TOPS. The effectiveness of mTOPS has been studied via simulations.

Robust and Efficient Frequency Estimator for Undersampled Waveforms Based on Frequency Offset Recognition.

This paper proposes an efficient frequency estimator based on Chinese Remainder Theorem for undersampled waveforms. Due to the emphasis on frequency offset recognition (i.e., frequency shift and compensation) of small-point DFT remainders, compared to estimators using large-point DFT remainders, it can achieve higher noise robustness in low signal-to-noise ratio (SNR) cases and higher accuracy in high SNR cases. Numerical results show that, by incorporating a remainder screening method and the Tsui spectrum corrector, the proposed estimator not only lowers the SNR threshold of detection, but also provides a higher accuracy than the large-point DFT estimator when the DFT size decreases to 1/90 of the latter case.

Robust and rapid converging adaptive beamforming via a subspace method for the signal‐plus‐interferences covariance matrix estimation

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal-to-interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.

Sparse LMS/F algorithms with application to adaptive system identification

AbstractStandard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with lp‐LMS and l0‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

Data-Aided SNR Estimation in Time-Variant Rayleigh Fading Channels

This paper addresses the data-aided (DA) signal-to-noise ratio (SNR) estimation for constant modulus modulations over time-variant flat Rayleigh fading channels. The time-variant fading channel is modeled by considering the Jakes' model and the first order autoregressive (AR1) model. Closed-form expressions of the Cramer-Rao bound (CRB) for DA SNR estimation are derived for known and unknown fast fading Rayleigh channels parameters cases. As special cases, the CRBs over slow and uncorrelated fading Rayleigh channels are derived. Analytical approximate expressions for the CRBs are derived for low and high SNR. These expressions that enable the derivation of a number of properties that describe the bound's dependence on key parameters such as SNR, channel correlation and sample number. Since the exact maximum likelihood (ML) estimator is computationally intensive in the case of fast-fading channels, two approximate ML estimator solutions are proposed for high and low SNR cases in the case of known channel parameters. The performances of theses estimators are examined analytically in terms of means and variances. In the presence of unknown channel parameters, a high SNR ML estimator based on the AR1 correlation model is derived. It is shown that the ML estimates of the SNR parameter and unknown channel parameters may be obtained in a separable form. Finally, simulation results illustrate the performance of the estimator and confirm the validity of the theoretical analysis.

Near optimal MIMO detection with reduced search space

A multi-input multi-output (MIMO) detection scheme that requires considerable low complexity but still achieves the near optimal performance is proposed. The fundamental idea of the proposed MIMO detection scheme consists of two points: 1) the computational complexity is restrained by a complexity limit in low signal-to-noise ratio (SNR) region; 2) while in high SNR region, the complexity is significantly reduced by the proposed search space method. Comparing with existing fixed-complexity techniques of MIMO detection (e.g., K-best sphere detector and reduced-search maximum-likelihood (RS ML) detection), the significant benefit of proposed detection scheme is that less computational power will be spent for the given data rate, or the throughput of detector can be increased for high SNR cases. According to the simulation results, the near optimal performance can be obtained while the detection complexity is kept considerable small.

An Adaptive Moments Estimation Technique Applied to MST Radar Echoes

Abstract An adaptive spectral moments estimation technique has been developed for analyzing the Doppler spectra of the mesosphere–stratosphere–troposphere (MST) radar signals. The technique, implemented with the MST radar at Gadanki (13.5°N, 79°E), is based on certain criteria, set up for the Doppler window, signal-to-noise ratio (SNR), and wind shear parameters, which are used to adaptively track the signal in the range–Doppler spectral frame. Two cases of radar data, one for low and the other for high SNR conditions, have been analyzed and the results are compared with those from the conventional method based on the strongest peak detection in each range gate. The results clearly demonstrate that by using the adaptive method the height coverage can be considerably enhanced compared to the conventional method. For the low SNR case, the height coverage for the adaptive and conventional methods is about 22 and 11 km, respectively; the corresponding heights for the high SNR case are 24 and 13 km. To validate the results obtained through the adaptive method, the velocity profile is compared with global positioning system balloon sounding (GPS sonde) observations. The results of the adaptive method show excellent agreement with the GPS sonde measured wind speeds and directions throughout the height profile. To check the robustness and reliability of the adaptive algorithm, data taken over a diurnal cycle at 1-h intervals were analyzed. The results demonstrate the reliability of the algorithm in extracting wind profiles that are self-consistent in time. The adaptive method is thus found to be of considerable advantage over the conventional method in extracting information from the MST radar signal spectrum, particularly under low SNR conditions that are free from interference and ground clutter.

Performance of RCPC codes with soft-decision decoding over Nakagami-m fading channels

The analytical upper bounds on the pairwise error probability of rate compatible punctured convolutional (RCPC) codes, using coherent BPSK signals over slow frequency nonselective Nakagami-m fading channels with AWGN, are evaluated. With perfect channel state information (CSI) assumption, we use a direct integral with the Nakagami-m probability density function to obtain a closed form upper bound. For the case without CSI, we find an approximated upper bound for the high SNR cases and the approximation can be justified for signal to noise ratio (SNR) E/sub s//N/sub 0/ /spl Gt/ 1.5 dB.