Wide Range Of Signal-to-noise Ratio Research Articles

Robust adaptive beamforming using a novel signal power estimation algorithm

Recently, many robust adaptive beamforming (RAB) methods based on covariance matrix reconstruction have been proposed. Motivated by the idea, in this paper, a novel and efficient signal power estimator is devised to reconstruct the interference-plus-noise covariance (INC) matrix, with the corresponding RAB algorithm proposed. Firstly, the steering vectors of the incoming sources are derived using the Capon spatial spectrum and known array geometry. Secondly, a set of linear equations is established based on the signal subspace projection, from which the powers of the incoming sources are estimated. Based on the presumed angular sector of the signal-of-interest (SOI), the steering vectors and powers of the SOI and interferences are distinguished, and the INC matrix is then reconstructed. Finally, the beamformer is determined by the estimated INC matrix and SOI steering vector. The proposed algorithm is computationally more efficient than other reconstruction-based methods because there are closed-form solutions for the signal powers. Simulation results indicate that our proposed algorithm performs better than the existing methods at high signal-to-noise ratios (SNRs), and achieves nearly optimal performance across a wide range of SNR.

A Robust RF Fingerprinting Approach Using Multisampling Convolutional Neural Network

With the increasing popularity of the Internet of Things (IoT), device identification, and authentication has become a critical security issue. Recently, radio frequency (RF) fingerprint-based identification schemes have attracted wide attention as they extract the inherent characteristics of hardware circuits which is very hard to forge. However, existing RF fingerprint-based approaches face the problems of unstable region of interest (ROI), high-cost feature design, and incomplete automation. To address these problems, this paper proposes a multisampling convolutional neural network (MSCNN) to extract RF fingerprint from the selected ROI for classifying ZigBee devices. A signal-to-noise ratio (SNR) adaptive ROI selection algorithm is also developed to alleviate the effect of semi-steady behavior of ZigBee devices owing to sleep mode switching. The proposed MSCNN uses multiple downsampling transformations for multiscale feature extraction and classification automatically. To validate and evaluate the performance of our proposed method, we design a testbed consisting of one low-cost universal software radio peripheral (USRP) as the receiver and 54 CC2530 devices as targets for identification. Extensive experiments are conducted to demonstrate the feasibility and reliability of MSCNN both in the line-of-sight (LOS) scenarios and non-LOS (NLOS) scenarios. The classification accuracy is as high as 97% under the LOS scenarios around SNR = 30 dB. Our scheme is robust over a wide range of SNRs under the LOS scenarios as well as under the NLOS scenarios.

Efficient Soft Video MIMO Design to Combine Diversity and Spatial Multiplexing Gain

How to strike a balance between diversity gain and spatial multiplexing gain in a soft video delivery system is an open problem. Due to the power limit, it is especially important to achieve the optimal balance during video transmission in the Internet of Things. In this paper, taking the multisimilarity feature of a soft video system into account, we design an adaptive multiple-input, multiple-output (MIMO) receiver that can utilize nearly optimally either diversity gain or multiplexing gain. At the transmitter, we arrange multisimilar video data according to the space-time coding style and transmit them through multiple antennas. At the receiver, we propose using two decoders, i.e., the multisimilar space-time block coding (Ms-STBC) decoder and the soft MIMO decoder. The decoder to be chosen is determined by the predicted performance gain. We show that the proposed Ms-STBC transmission scheme can be considered a joint source-channel design, which is proposed for a soft video multiantenna delivery system. The relationship between intracodewords similarity and the channel signal-to-noise ratio (SNR) gain is derived. The experimental results demonstrate that the proposed designs can achieve a significant improvement over either individual soft MIMO multiplexing decoding or individual space-time block coding decoding in terms of peak SNR (PSNR) under the condition of a time-varying channel and a wide range of SNR. We can obtain at most 4-dB PSNR gain compared with the soft MIMO system.

Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF

In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial localization method. Using a pre-learned universal NMF dictionary, RT-GCC-NMF operates in a frame-by-frame fashion by associating individual dictionary atoms to target speech or background interference based on their estimated time-delay of arrivals (TDOA). We evaluate RT-GCC-NMF on two-channel mixtures of speech and real-world noise from the Signal Separation and Evaluation Campaign (SiSEC). We demonstrate that this approach generalizes to new speakers, acoustic environments, and recording setups from very little training data, and outperforms all but one of the algorithms from the SiSEC challenge in terms of overall Perceptual Evaluation methods for Audio Source Separation (PEASS) scores and compares favourably to the ideal binary mask baseline. Over a wide range of input SNRs, we show that this approach simultaneously improves the PEASS and signal to noise ratio (SNR)-based Blind Source Separation (BSS) Eval objective quality metrics as well as the short-time objective intelligibility (STOI) and extended STOI (ESTOI) objective speech intelligibility metrics. A flexible, soft masking function in the space of NMF activation coefficients offers real-time control of the trade-off between interference suppression and target speaker fidelity. Finally, we use an asymmetric short-time Fourier transform (STFT) to reduce the inherent algorithmic latency of RT-GCC-NMF from 64 ms to 2 ms with no loss in performance. We demonstrate that latencies within the tolerable range for hearing aids are possible on current hardware platforms.

Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

The development of cognitive radio and radar electronic reconnaissance has put forward an important demand for improving the recognition ability of modulated signals in complex electromagnetic environment. In this paper, we propose a valid radar signal modulation recognition technology under low signal-to-noise ratio (SNR). The recognition technology can recognize 12 different modulation signals, including Costas, LFM, NLFM, BPSK, P1–P4, and T1–T4 codes. First, we propose the image fusion algorithm of non-multi-scale decomposition to fuse images of a single signal with different time–frequency (T–F) methods. Specifically, weights are designed by the principal component analysis, which could combine significative details of T–F images. Second, we adopt transfer learning-based convolutional neural networks and self-training-based stacked autoencoder, which extract the effective information on fusion image, furthermore guarantee the recognition performance. Moreover, multi-feature fusion algorithm is used to fuse features, which reduces redundant information on features and enhances computing efficiency. Finally, the classifier is performed by a classical algorithm called support vector machine. Simulation results show that the average recognition success rate is 95.5% at SNR of −6dB. It is testified that proposed recognition technology possesses good robustness and superiority in RSR with a wide range of SNR.

ARM: Adaptive Random-Selected Multi-Beamforming Estimation Scheme for Satellite-Based Internet of Things

In this paper, we focus on the millimeter-wave (mmWave) band multiple-input and multiple-output (MIMO) channel estimation in the satellite-based Internet of Things (S-IoT). At first, we establish a sparse geometric-based mmWave MIMO channel model between a high throughput satellite (HTS) and multiple terrestrial user equipments (UEs) for the S-IoT downlink system. By exploiting the sparsity inherent to the mmWave band channel, we propose an efficient adaptive random-selected multi-beamforming (ARM) estimation scheme, which can simultaneously estimate the mmWave MIMO channel state information (CSI) for multiple UEs in angle domain. The ARM estimation scheme measures of the CSI between the HTS and multiple UEs by utilizing a series of random combinations of transmitting beamforming vectors and receiving UEs' antennas and the required number of measurements can adaptively reduce as well as the signal-to-noise ratios (SNR) increases. To further improve the performance of ARM estimation scheme, the number of each beamforming been selected for the channel measurement is buffered, and modify the probability of the random selection of beams and UEs. Two improved ARM estimation schemes are proposed, one is ARM forcing adaptation beam selection (ARM-FABS) scheme, and the other is ARM partially estimated beam selection (ARM-PEBS) scheme. The simulation results show that the proposed ARM estimation schemes can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.

A Hybrid Digital-Analog Chaotic Code

Traditional analog chaotic codes suffer from the vulnerability of signs at the receiver, and this situation deteriorates at low signal-to-noise ratio (SNR). In order to solve this problem, this letter proposes a hybrid digital-analog chaotic code (HDACC), which uses chaotic states (analog value) and part of their signs (digital value) together as parity codewords. These signs are further encoded in a digital way. We derived the theoretical decoding mean square error (MSE) of HDACC over additive white Gaussian noise channel and gave the optimal digital/analog codewords allocation. Simulation results demonstrate that the derived theoretical MSE matches very well with simulated MSE curves. Evaluations show that HDACC outperforms existing related works in a wide SNR range.

Evaluation of a near-end listening enhancement algorithm by combined speech intelligibility and listening effort measurements.

Previous studies showed that near-end listening enhancement (NELE) algorithms can significantly improve speech intelligibility in noisy environments. This study investigates the benefit of the NELE algorithm AdaptDRC in normal-hearing listeners at signal-to-noise ratios (SNRs) for which speech intelligibility is at ceiling, by evaluating listening effort for processed and unprocessed speech in the presence of speech-shaped and cafeteria noise. The results suggest that the NELE algorithm is able to reduce listening effort over a wide range of SNRs. Hence, listening effort seems to be applicable for evaluating NELE algorithms over a much wider SNR range than speech intelligibility.

Codebooks design and performance evaluation based on antenna array response and signal to noise ratio for mmWave communication

ABSTRACTCodebook design is a key point in beam-forming which is necessary for millimetre Wave (mmWave) communication to compensate its huge free space path loss. In beam-forming codebook, the transmitter and the receiver co-operate to select the optimum pre-designed code vectors to satisfy robust, efficient, and reliable transmission link between two pairs. In our study, we propose a new codebook structure based on unique phase states. Based on the Array Factor (AF) and Signal-to-Noise Ratio (SNR), the proposed codebook is compared and evaluated with the uniform weighting codebook, circular array codebook, and IEEE 802.15.3c standard codebook. The results show that the proposed codebook achieves comparable SNR and array directivity with the IEEE 802.15.3c, but higher peak and wide dynamic range of SNR and better array directivity compared with the uniform weighting and circular antenna codebooks. Thus, the proposed codebook is expected to improve signal quality and increase user throughput significantly.

NLOS Identification and Machine Learning Methods for Predicting the Outcome of 60GHz Ranging System

Millimeter-wave (MMW) signals in 60GHz band have shown immense potential for accurate range estimation with precise time and multipath resolution. Nonline of sight (NLOS) propagation is a primary factor affecting the range precision. To improve range estimation, an Energy detector (ED) based normalized threshold algorithm which employs a Neural network (NN) is developed on the basis of NLOS identification. The maximum curl and standard deviation of the received energy block values are used to determine NLOS environment and the normalized thresholds for different Signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.

Impact of stimulus-related factors and hearing impairment on listening effort as indicated by pupil dilation

Previous research has reported effects of masker type and signal-to-noise ratio (SNR) on listening effort, as indicated by the peak pupil dilation (PPD) relative to baseline during speech recognition. At about 50% correct sentence recognition performance, increasing SNRs generally results in declining PPDs, indicating reduced effort. However, the decline in PPD over SNRs has been observed to be less pronounced for hearing-impaired (HI) compared to normal-hearing (NH) listeners. The presence of a competing talker during speech recognition generally resulted in larger PPDs as compared to the presence of a fluctuating or stationary background noise. The aim of the present study was to examine the interplay between hearing-status, a broad range of SNRs corresponding to sentence recognition performance varying from 0 to 100% correct, and different masker types (stationary noise and single-talker masker) on the PPD during speech perception. Twenty-five HI and 32 age-matched NH participants listened to sentences across a broad range of SNRs, masked with speech from a single talker (−25 dB to +15 dB SNR) or with stationary noise (−12 dB to +16 dB). Correct sentence recognition scores and pupil responses were recorded during stimulus presentation. With a stationary masker, NH listeners show maximum PPD across a relatively narrow range of low SNRs, while HI listeners show relatively large PPD across a wide range of ecological SNRs. With the single-talker masker, maximum PPD was observed in the mid-range of SNRs around 50% correct sentence recognition performance, while smaller PPDs were observed at lower and higher SNRs. Mixed-model ANOVAs revealed significant interactions between hearing-status and SNR on the PPD for both masker types. Our data show a different pattern of PPDs across SNRs between groups, which indicates that listening and the allocation of effort during listening in daily life environments may be different for NH and HI listeners.

High SNR approximation for performance analysis of two-way multiple relay networks

Signal-to-Noise Ratio (SNR) is the key parameter in the performance evaluation of Two-Way, Amplify-and-Forward, Multiple-Relaying (TAF-MR) networks. This paper introduces new methods that use high SNR (HSNR) level approximations to analyze accurately the Bit Error Rate (BER), optimize the Relay Location (RL), and balance the energy efficiency (EE) and spectral efficiency (SE) of such networks. We consider a flat-fading channel and a strategy for selecting a relay with the highest SNR. Standardized BER expressions are obtained for a wide range of SNRs (low, high, and optimal) to yield more accurate predictions of the BER performance. Thus, by using HSNR, a unified analysis for calculating the Asymptotic BER (ABER) and the exact BER (EBER) performance is developed. The optimal SNR level is obtained by optimizing the power of network sources, which include the relay and user powers. Further, we propose a method for integrating the RL with the balancing of the EE and SE optimally to achieve the best EE improvement. The derived expressions for the methods are validated by simulation.

Precise Error Rate Analysis of Wireless Relay Networks

A new method to analyze the bit error rate (BER) performance of two-hop multiple relay networks is presented in this paper. For this work, a flat fading channel is considered for exchanging signals between two wireless users which communicate with each other through set of bidirectional relays that operate as repeaters (amplify-and-forward). Each user adopts a relay selection scheme to choose the relay with the highest signal-to-noise-ratio (SNR). The proposed method aims to reduce the analysis of the two-hop relay network to the BER analysis of one-hop (direct link) and this allows to improve the asymptotic BER (ABER) performance of two-hop. Thereby, the ABER would be more suitable for wide SNR range. From this ABER analysis, the exact BER (EBER) can be obtained directly and this gives a standard BER analysis for bidirectional relay networks. The analytic expressions for both ABER and EBER are derived and then confirmed by simulation results. It is found that the proposal gives low disparity calculation errors between ABER and EBER. It hence provides an accurate BER analysis for a bidirectional relay networks.

Adaptive Weighted Sensing With Simultaneous Transmission for Dynamic Primary User Traffic

In practical scenarios with random arrival and departure of primary users (PUs), existing simultaneous sensing and transmission schemes allocated the same weight to each sample, and did not consider low signal-to-noise ratio (SNR) situations. This paper proposes an adaptive weighted sensing scheme with simultaneous transmission for dynamic PU traffic. It uses a power function based on the corresponding sampling sequence and could reveal the actual PU state in near real time. The power exponent is further adjusted to the sensing situations to achieve lowest false alarm probability under a certain detection probability constraint. Then, an analytical model considering all possible PU state transitions is developed to evaluate achievable interference, throughput, and energy efficiency. Furthermore, the optimal frame duration yielding both optimal false alarm probability and throughput is computed. After that, a fast search algorithm is proposed to track the optimal duration at an exponential convergence rate. Simulation results are provided to validate the analytical model and demonstrate the improvement in low SNR. The results indicate that the proposed scheme can achieve lower false alarm probability and higher energy efficiency over a wide SNR range than that of the existing weighting schemes, which are based on probability, geometric sequence, and equal weighting.

Extreme learning machine for 60 GHz millimetre wave positioning

Extreme learning machine (ELM) has attracted considerable attention in recent years due to its numerous applications in classification and regression. In this study, the authors investigate the performance of an ELM-based threshold selection algorithm for 60 GHz millimetre wave time of arrival estimation using energy detector (ED). A hybrid metric based on the skewness, kurtosis, standard deviation, and slope of the ED values is employed. The optimal normalised threshold for different signal-to-noise ratios (SNRs) is investigated, and the effects of the integration period and channel model are examined. Performance results are presented which show that the proposed ELM-based algorithm provides high precision and better robustness than existing techniques over a wide range of SNRs for the IEEE 802.15.3c CM1.1 and CM2.1 channel models. Further, the performance is largely independent of the integration period and channel model.

Precoder Designs for the Relay-Aided X Channel Without Source CSI

In this paper, precoder designs are proposed for the wireless X networks aided by a relay and with no or very limited channel state information at the transmitters (CSIT). First, we derive the general feasibility conditions for interference alignment (IA) for a relay-aided symmetric multi-input-multi-output X channel (also called the X relay channel or XRC) without any CSI at the source transmitters. Next, we propose an IA-based joint beamforming approach with very limited feedback to the transmitters (referred to as “IA-LFB” in this paper), which can attain the maximum degrees of freedom (DoF) of the network under arbitrarily low relay power. Finally, we propose an iterative minimum-sum mean-square-error (MMSE) precoder design for the single-input-single-output XRC for throughput improvement in the finite signal-to-noise ratio (SNR) region. The proposed MMSE precoder also shows better bit error rate results than the IA precoder over a wide SNR range. Since the MMSE precoder can achieve only locally optimal solutions, we propose to initialize the iterative MMSE algorithm with the IA-LFB precoder which, unlike random initializations, not only improves the throughput in the finite SNR regions but also preserves the network DoF even under limited relay power.

Psychometric Functions of Dual-Task Paradigms for Measuring Listening Effort.

The purpose of the study was to characterize the psychometric functions that describe task performance in dual-task listening effort measures as a function of signal to noise ratio (SNR). Younger adults with normal hearing (YNH, n = 24; experiment 1) and older adults with hearing impairment (n = 24; experiment 2) were recruited. Dual-task paradigms wherein the participants performed a primary speech recognition task simultaneously with a secondary task were conducted at a wide range of SNRs. Two different secondary tasks were used: an easy task (i.e., a simple visual reaction-time task) and a hard task (i.e., the incongruent Stroop test). The reaction time (RT) quantified the performance of the secondary task. For both participant groups and for both easy and hard secondary tasks, the curves that described the RT as a function of SNR were peak shaped. The RT increased as SNR changed from favorable to intermediate SNRs, and then decreased as SNRs moved from intermediate to unfavorable SNRs. The RT reached its peak (longest time) at the SNRs at which the participants could understand 30 to 50% of the speech. In experiments 1 and 2, the dual-task trials that had the same SNR were conducted in one block. To determine if the peak shape of the RT curves was specific to the blocked SNR presentation order used in these experiments, YNH participants were recruited (n = 25; experiment 3) and dual-task measures, wherein the SNR was varied from trial to trial (i.e., nonblocked), were conducted. The results indicated that, similar to the first two experiments, the RT curves had a peak shape. Secondary task performance was poorer at the intermediate SNRs than at the favorable and unfavorable SNRs. This pattern was observed for both YNH and older adults with hearing impairment participants and was not affected by either task type (easy or hard secondary task) or SNR presentation order (blocked or nonblocked). The shorter RT at the unfavorable SNRs (speech intelligibility < 30%) possibly reflects that the participants experienced cognitive overload and/or disengaged themselves from the listening task. The implication of using the dual-task paradigm as a listening effort measure is discussed.

Energy detector based TOA estimation for MMW systems using machine learning

60 GHz millimeter wave signals can provide precise time and multipath resolution and so have great potential for accurate time of arrival (TOA) and range estimation. To improve TOA estimation, a new energy detector based threshold selection algorithm which employs a neural network is proposed. The minimum slope, kurtosis, and skewness of the received energy block values are used to determine the normalized thresholds for different signal-to-noise ratios (SNRs). The effects of the channel and integration period are evaluated. Performance results are presented which show that the proposed approach provides better precision and is more robust than other solutions over a wide range of SNRs for the CM1.1 and CM2.1 channel models in the IEEE 802.15.3c standard.

Mixture of PLDA for Noise Robust I-Vector Speaker Verification

In real-world environments, noisy utterances with variable noise levels are recorded and then converted to i-vectors for cosine distance or PLDA scoring. This paper investigates the effect of noise-level variability on i-vectors. It demonstrates that noise-level variability causes the i-vectors to shift, causing the noise contaminated i-vectors to form clusters in the i-vector space. It also demonstrates that optimal subspaces for discriminating speakers are noise-level dependent. Based on these observations, this paper proposes using signal-to-noise ratio (SNR) of utterances as guidance for training mixture of PLDA models. To maximize the coordination among the PLDA models, mixtures of PLDA models are trained simultaneously via an EM algorithm using the utterances contaminated with noise at various levels. For scoring, given a test i-vector, the marginal likelihoods from individual PLDA models are linearly combined by the posterior probabilities of the test utterance’s SNR. Verification scores are the ratio of the marginal likelihoods. Results based on NIST 2012 SRE suggest that the SNR-dependent mixture of PLDA is not only suitable for the situations where the test utterances exhibit a wide range of SNR, but also beneficial for the test utterances with unknown SNR distribution. Supplementary materials containing full derivations of the EM algorithms and scoring functions can be found in http://bioinfo.eie.polyu.edu.hk/mPLDA/SuppMaterials.pdf .

Signal Mapping for Bit-Interleaved Coded Modulation Schemes to Achieve Secure Communications

In this letter, we investigate different signal mappings for bit-interleaved coded modulation (BICM) and their effect on the secrecy information rate of a wire-tap channel exhibiting Rayleigh fading. By focusing on BICM schemes using 64 QAM, 16 QAM and (1, 5, 10) signal constellations, we show that the optimal mapping depends on the range of signal-to-noise ratios (SNRs) we are interested in. We propose a new mapping algorithm, called maximum error event (MEE), to optimize the secrecy rate over a wide range of SNRs. At low SNR, MEE mapping achieves a lower secrecy rate than other well known mappings, but at medium-to-high SNRs MEE mapping achieves a significantly higher secrecy rate over a wide range of SNRs. Simulation results on 16 QAM, 64 QAM and (1, 5, 10) constellations are presented to show the achievable secrecy rates of various mappings for BICM from low to high SNRs.