Wide Range Of Signal-to-noise Ratio Research Articles

A comparative study of deep learning and iterative algorithms for joint channel estimation and signal detection in OFDM systems

Joint channel estimation and signal detection (JCESD) is crucial in orthogonal frequency division multiplexing (OFDM) systems, but traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain. Hence, the development of a robust and low-complexity model that can deliver excellent performance across a wide range of SNRs is highly desirable. In this paper, we aim to establish a benchmark where traditional algorithms and DL methods are validated on different channel models, Doppler, and SNR settings, particularly focusing on the semi-blind setting. In particular, we propose a new DL model where the backbone network is formed by unrolling the iterative algorithm, and the hyperparameters are estimated by hypernetworks. Additionally, we adapt a lightweight DenseNet to the task of JCESD for comparison. We evaluate different methods in three aspects: generalization in terms of bit error rate (BER), robustness, and complexity. Our results indicate that DL approaches outperform traditional algorithms in the challenging low-SNR setting, while the iterative algorithm performs better in high-SNR settings. Furthermore, the iterative algorithm is more robust in the presence of carrier frequency offset, whereas DL methods excel when signals are corrupted by asymmetric Gaussian noise.

Trade-Off Between Positioning and Communication for Millimeter Wave Systems With Ziv-Zakai Bound

In this paper, we investigate the trade-off between positioning and communication for an integrated positioning and communication (IPAC) millimeter wave system. First, in terms of the positioning in the IPAC system, the Cramér-Rao bound (CRB) is commonly used as a performance metric. Unfortunately, the CRB is only tight in a certain region for the high signal-to-noise ratio (SNR). To compensate for this deficiency, we derive the Ziv-Zakai bound (ZZB) for the IPAC system by exploiting the a priori delay information extracted from both the time delay parameter and the channel amplitude attenuation. Further, we derive the expected CRB (ECRB) and the weighted CRB (WCRB) of the system for comparisons. Second, we analyze the trade-off between positioning and communication for this IPAC system based on the derived ZZB. Specifically, we aim to optimize the power allocation to maximize the achievable data rate subject to the ZZB and total transmit power constraints. Numerical results show that the ZZB provides a tighter and more reasonable bound for the minimum mean square error (MMSE) estimator over the wide range of SNRs compared to the ECRB and WCRB. Moreover, the trade-off between positioning and communication is revealed via changing critical parameters by simulations.

Microwave photonic radar system with improved SNR performance utilizing optical resonant amplification and random Fourier coefficient waveforms.

A microwave photonic (MWP) radar system with improved signal-to-noise ratio (SNR) performance is proposed and experimentally demonstrated. By improving the SNR of echoes through properly designed radar waveforms and resonant amplification in the optical domain, the proposed radar system can detect and image weak targets that were previously hidden in noise. Echoes with a common low-level SNR obtain high optical gain and the in-band noise is suppressed during resonant amplification. The designed radar waveforms, based on random Fourier coefficients, reduce the effect of optical nonlinearity while providing reconfigurable waveform performance parameters for different scenarios. A series of experiments are developed to verify the feasibility of the SNR improvement of the proposed system. Experimental results show a maximum SNR improvement of 3.6 dB with an optical gain of 28.6 dB for the proposed waveforms over a wide input SNR range. From a comparison with linear frequency modulated signals in microwave imaging of rotating targets, significant quality enhancement is observed. The results confirm the ability of the proposed system to improve SNR performance of MWP radars and its great application potential in SNR-sensitive scenarios.

An improved TOA estimation algorithm based on denoised MVDR for B5G positioning

This paper proposes an improved minimum variance distortionless response (MVDR) based TOA estimation algorithm for 5G NR signals under multipath environments. The proposed algorithm achieves high resolution by exploiting a large number of subcarriers of 5G signals and reduces the dimension of the covariance matrix involved in MVDR substantially by utilizing a novel smoothing scheme. Since MVDR requires a relatively high signal-to-noise ratio (SNR), a denoising method is used to improve the TOA estimation performance. Simulation results show that the proposed algorithm achieves much higher resolution than the Bartlett beamformer (BF) and the TOA estimation accuracy remains high over a wide range of SNRs.

Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system.

We have proposed and demonstrated a denoising and extraction convolutional neural network (DECNN) composed of 1D denoising convolutional autoencoder (DCAE) and 1D residual attention network (RANet) modules to extract temperature and strain simultaneously in a Brillouin optical time-domain analysis (BOTDA) system. With DCAE for high-fidelity denoising and RANet for accurate and robust information extraction, integrated denoising and extraction of both temperature and strain have been realized for the first time under a single CNN framework. Both simulation and experiment have been conducted to statistically analyze the performance of the proposed scheme and compare it with the conventional equation solving method (CESM), which show that DECNN has large noise tolerance and robustness over a wide range of temperature/strain and signal-to-noise ratio (SNR) conditions. The mean standard deviation (SD) and root mean square error (RMSE) of the temperature/strain extracted by DECNN over a wide range of SNRs are only 0.2°C/9.7µɛ and 2°C/32.3µɛ at the end of 19.38 km long sensing fiber, respectively. At a relatively low SNR of 8.8 dB, DECNN shows 196 times better temperature/strain uncertainty and 146 times faster processing speed when compared with CESM.

Lightweight deep convolutional neural network for background sound classification in speech signals.

Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed with background embedded in real-world human speech signals. Thus, this work proposes a lightweight deep convolutional neural network (CNN) in conjunction with spectrograms for an efficient background sound classification with practical human speech signals. The proposed model classifies 11 different background sounds such as airplane, airport, babble, car, drone, exhibition, helicopter, restaurant, station, street, and train sounds embedded in human speech signals. The proposed deep CNN model consists of four convolution layers, four max-pooling layers, and one fully connected layer. The model is tested on human speech signals with varying signal-to-noise ratios (SNRs). Based on the results, the proposed deep CNN model utilizing spectrograms achieves an overall background sound classification accuracy of 95.2% using the human speech signals with a wide range of SNRs. It is also observed that the proposed model outperforms the benchmark models in terms of both accuracy and inference time when evaluated on edge computing devices.

Recurrent Neural Network Equalization for Wireline Communication Systems

Satisfying the demand for ever increasing data rates in wireline communication systems is challenging due to high frequency-dependent channel insertion loss causing significant inter-symbol interference (ISI). This motivates the use of ADC-based receiver architectures that allow for digital equalizer implementations. However, the large tap count feed-forward equalizers (FFEs) required at high data rates causes noise enhancement that can degrade signal-to-noise ratio (SNR) and high complexity is required to implement multi-tap decision feedback equalizers (DFEs). Moreover, analog front-end (AFE) nonlinearity can limit the performance of these traditional FFE-DFE architectures. This brief investigates an alternative approach with recurrent neural network (RNN) equalization based on long short-term memory (LSTM) and gate recurrent unit (GRU) cells. 64 Gb/s PAM4 modeling results show that the RNN topologies offer significant BER improvement over a wide SNR range for a channel with 30 dB loss at 16 GHz and enable operation over a channel with a large notch at 10 GHz that the traditional FFE-DFE topology cannot support. Synthesis results utilizing a 22 nm FinFET process yield only a 34% and 29% increase in receiver power consumption for the LSTM and GRU implementations, respectively, to enable this improved performance.

Noninvasive estimation of aortic pressure waveform based on simplified Kalman filter and dual peripheral artery pressure waveforms

Background and ObjectiveAortic pressure (Pa) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the Pa waveform using dual peripheral artery pressure waveforms. MethodsPa waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed Pa waveforms were compared. ResultsThe proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs. ConclusionThe simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool.

Multi-Carrier Signal Recognition Method Based on Multi-Feature Input and Hybrid Training Neural Network

In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.

Analysis and Design of Analog Fountain Codes for Short Packet Communications

In this paper, we focus on the design and analysis of the Analog Fountain Code (AFC) for short packet communications. We first propose a density evolution (DE) based framework, which tracks the evolution of the probability density function of the messages exchanged between variable and check nodes of AFC in the belief propagation decoder. Using the proposed DE framework, we formulate an optimisation problem to find the optimal AFC code parameters, including the weight-set, which minimises the bit error rate at a given signal-to-noise ratio (SNR). Our results show the superiority of our AFC code design compared to existing designs of AFC in the literature and thus the validity of the proposed DE framework in the asymptotically long block length regime. We then focus on selecting the precoder to improve the performance of AFC at short block lengths. Simulation results show that lower precode rates obtain better realised rates over a wide SNR range for short information block lengths. We also discuss the complexity of the AFC decoder and propose a threshold-based decoder to reduce the complexity.

Novel deep learning framework for wideband spectrum characterization at sub-Nyquist rate

Limited availability and high auction cost of the sub-6 GHz spectrum led to the introduction of spectrum-sharing in 5G networks. This demands base stations with the capability of automatic wideband spectrum characterization (AWSC) to identify available vacant spectrum and parameters (carrier frequency, modulation scheme, etc.) of occupied bands. Since WSC at Nyquist sampling (NS) is area and power-hungry and conventional statistical AWSC performs poorly at a low signal-to-noise ratio (SNR), we propose a novel sub-Nyquist sampling (SNS) based deep-learning framework. It is a single unified pipeline that accomplishes two tasks: (1) Reconstruct the signal directly from the sub-Nyquist samples, and (2) Identify the occupancy status and modulation scheme of all bands. The proposed non-iterative approach based reconstruction provides the occupancy status of all bands in a single forward pass leading to significant improvement in execution time over state-of-the-art iterative methods. In addition, the proposed approach does not need complex signal conditioning between reconstruction and characterization. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based characterization and its performance approaches NS based framework with an increase in SNR without compromising computation complexity. A single unified deep-learning framework makes the proposed method a good candidate for reconfigurable platforms.

Automatic modulation classification using modulation fingerprint extraction

An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.

Accurate frequency estimation of multiple complex and real sinusoids based on iterative interpolation

Frequency estimation of multiple complex and real sinusoids in noise has attracted wide attention in many fields. In this paper, two frequency domain parametric methods are proposed to estimate the frequencies of multiple complex and real sinusoids accurately. The first method for complex sinusoids adopts Discrete Fourier Transform (DFT) and interpolation algorithm to obtain the coarsely estimated frequencies, and then the interpolation is used iteratively to refine the estimated frequencies. In each step, the estimated frequency components are subtracted to reduce spectrum leakage. The second method for multiple real sinusoids, which is modified based on the first one, focuses on reducing the spectrum leakage caused by negative frequencies and then employs iterative interpolation to refine the estimated frequencies. Simulation results show that two proposed methods have lower mean square error (MSE) and signal-to-noise ratio (SNR) breakdown threshold than other frequency domain parametric methods, and are capable of achieving the Cramér-Rao lower bound (CRLB) for both multiple complex and real signals under a wide range of SNR.

On the ASER performance of SC receiver with RQAM and HQAM over [formula omitted]-[formula omitted] fading

In this paper, we consider a multi-branch selection combining receiver over independent and non-identically distributed κ-μ fading channels. We derive new exact expressions for moment generating function of output signal-to-noise-ratio (SNR), average symbol error rate (ASER) of coherent rectangular and hexagonal quadrature amplitude modulations, and ASER of non-coherent frequency shift-keying modulation schemes. Further, an asymptotic ASER expression is also derived to determine the diversity order of the system. The analytical results are validated using Monte-Carlo simulations for a wide range of SNRs and fading parameters. Finally, the impact of channel parameters and the number of branches on the ASER performance is also studied.

Experimental Evaluation of the Generalized Signal Droop Model With SNR Measurements Including Transponder Implementation Penalty

We experimentally assess the accuracy of a constant output power generalized droop (COP-GD) model combined with transponder implementation penalty to predict transmission performance for single-core long-haul optical transmission systems operating at low channel launch powers and high baud rates. The droop model includes Guided Acoustic Waveguide Brillouin Scattering (GAWBS) and nonlinear interference (NLI) effects along with amplifier noise for signal-to-noise ratio (SNR) calculation in the context of long-haul systems with constant output power amplifiers. We show how the model accuracy is decreased if one of the modeling components is removed, namely, either the droop effect, GAWBS or the transponder implementation penalty. We show that inclusion of implementation penalty and GD is important for accurate performance evaluation, moreover, the transponder implementation penalty has impact both at low and high SNR ends. We compare the model including both effects to experimental transmission data over a wide link SNR range for the first time and demonstrate excellent agreement between modeling predictions and experimental results.

Robust EEG-Based Decoding of Auditory Attention With High-RMS-Level Speech Segments in Noisy Conditions

The attended speech stream can be detected robustly, even in adverse auditory scenarios with auditory attentional modulation, and can be decoded using electroencephalographic (EEG) data. Speech segmentation based on the relative root-mean-square (RMS) intensity can be used to estimate segmental contributions to perception in noisy conditions. High-RMS-level segments contain crucial information for speech perception. Hence, this study aimed to investigate the effect of high-RMS-level speech segments on auditory attention decoding performance under various signal-to-noise ratio (SNR) conditions. Scalp EEG signals were recorded when subjects listened to the attended speech stream in the mixed speech narrated concurrently by two Mandarin speakers. The temporal response function was used to identify the attended speech from EEG responses of tracking to the temporal envelopes of intact speech and high-RMS-level speech segments alone, respectively. Auditory decoding performance was then analyzed under various SNR conditions by comparing EEG correlations to the attended and ignored speech streams. The accuracy of auditory attention decoding based on the temporal envelope with high-RMS-level speech segments was not inferior to that based on the temporal envelope of intact speech. Cortical activity correlated more strongly with attended than with ignored speech under different SNR conditions. These results suggest that EEG recordings corresponding to high-RMS-level speech segments carry crucial information for the identification and tracking of attended speech in the presence of background noise. This study also showed that with the modulation of auditory attention, attended speech can be decoded more robustly from neural activity than from behavioral measures under a wide range of SNR.

Distance Estimation Based on Deep Convolutional Neural Network Using Ultra-Wideband Signals

Recently, high accuracy localization technique is required to provide indoor location services. The purpose of this paper is to propose a distance estimation technique based on deep convolutional neural network (DCNN) for indoor environments. Among distance estimation techniques based on wireless communication signals, the use of ultra-wideband (UWB) signals has the advantage of high accuracy in the time domain. The proposed distance estimation method uses UWB signals and proposes a new DCNN-based distance estimator. The superiority of the proposed method is confirmed through computer simulation. Widely used conventional distance estimators are based on the power threshold. The threshold is determined by signal to noise ratio (SNR) of the received signal. The arrival time of the received signal that exceeds the threshold is considered as the time-of-arrival (ToA) and the distance between transmitter and receiver is obtained from the ToA. On the other hand, the proposed distance estimator requires only the received signal without SNR estimation, which make the proposed technique simpler to implement. According to computer simulation, the conventional method is highly sensitive to SNR and distance. In contrast, the proposed method shows less than 2 m root mean square error (RMSE) performance in a wide range of SNR and the RMSE performance is not degraded in long distances. The proposed distance estimator shows excellent distance estimation performance at low SNR and long distance, so it can be applied to indoor localization system of large indoor space and can be used for precise location service.

DQN-Based Adaptive Modulation Scheme Over Wireless Communication Channels

In this letter, to improve data rate over wireless communication channels, we propose a deep Q network (DQN)-based adaptive modulation scheme by using Markov decision process (MDP) model. The proposed algorithm makes the reinforcement learning agent to select rate region boundaries as the states, which divide signal-to-noise ratio (SNR) range into rate regions. The simulation results show that spectral efficiency can be improved on the average by 0.5395 bps/Hz in wide SNR range.

New approximate and asymptotic closed-form expressions for the outage probability and the average BER of MIMO-FSO system with MRC diversity technique over Gamma–Gamma fading channels with generalized pointing errors

This paper studies the outage probability (OP) and the average bit-error rate (BER) of multiple-input multiple-output (MIMO) free-space optical (FSO) system with maximal ratio combining (MRC) diversity technique over Gamma–Gamma (GG) fading channels with generalized pointing errors modeled by the Beckmann distribution. A new approximate closed-form probability density function (PDF) at high signal-to-noise ratio (SNR) for MRC scheme is derived by inverse Laplace transformation method. Based on the developed PDF, new approximate and asymptotic closed-form expressions for the OP and the average BER are derived, respectively. Then, the OP and the average BER performance of the MIMO-FSO system with MRC scheme at high SNR are analyzed with different parameters, including number of transmitters and receivers, receiver aperture sizes, nonzero boresight, and different jitters. Finally, numerical results and Monte Carlo simulations corroborate the accuracy of the derived approximate and asymptotic expressions over a wide range of SNR.

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.