Low SNR Regime Research Articles

Estimating Heart Rate Variability in Challenging Low SNR Regimes Using Wearable Magnetocardiography Sensors

Estimating Heart Rate Variability in Challenging Low SNR Regimes Using Wearable Magnetocardiography Sensors

Denoising coherent Doppler lidar data based on a U-Net convolutional neural network.

The coherent Doppler wind lidar (CDWL) has long been thought to be the most suitable technique for wind remote sensing in the atmospheric boundary layer (ABL) due to its compact size, robust performance, and low-cost properties. However, as the coherent lidar exploits the Mie scattering from aerosol particles, the signal intensity received by the lidar is highly affected by the concentration of aerosols. Unlike air molecules, the concentration of aerosol varies greatly with time and weather, and decreases dramatically with altitude. As a result, the performance of the coherent lidar fluctuates greatly with time, and the detection range is mostly confined within the planetary boundary layer. The original data collected by the lidar are first transformed into a spectrogram and then processed into radial wind velocities utilizing algorithms such as a spectral centroid. When the signal-to-noise ratio (SNR) is low, these classic algorithms fail to retrieve the wind speed stably. In this work, a radial wind velocity retrieving algorithm based on a trained convolutional neural network (CNN) U-Net is proposed for denoising and an accurate estimate of the Doppler shift in a low-SNR regime. The advantage of the CNN is first discussed qualitatively and then proved by means of a numerical simulation. Simulated spectrum data are used for U-Net training and testing, which show that the U-Net is not only more accurate than the spectral centroid but also achieves a further detection range. Finally, joint observation data from the lidar and radiosonde show excellent agreement, demonstrating that the U-Net-based retrieving algorithm has superior performance over the traditional spectral centroid method both in accuracy and detection range.

User pairing impact on the performance of hybrid beamforming NOMA system

This paper proposes a new user pairing technique for a power domain nonorthogonal multiple access (NOMA) deploying fully connected and subconnected hybrid beamforming (HBF) structures for a typical urban microcell downlink of line-of-sight (LOS) and non-line-of-sight (NLOS) surroundings. NOMA system’s configurations are set up for two (multiple input single output) users per cluster down- linking base station equipped with 128 antennas. HBF processing adopts phased zero forcing (P-ZF) for both fully connected (HBFNOMA) structure (FCS) and sub-connected (HBF-NOMA) structure (SCS) precoders’ optimization, and successive interference cancellation zero forcing (SIC-ZF) schemes to optimize the SCS-HBF-NOMA precoder exploiting dynamic power allocation. The new users’ pairing exploits users’ distance to the base station, namely near and far clusters different from the benchmark angle of arrival- based users’ pairing. The proposed users’ pairing and the precoding schemes’ impact are investigated for finite-resolution HBF structures operating in LOS and NLOS surroundings. The execution under New York University (NYU) mmW channel model is explored for two users in a cluster with different angles of arrival. Results show that the users’ pairing based on AoA performs better than the newly proposed users’ pairing. However, the proposed users’ pairing scheme performs better than their corresponding OMA counterparts, which still make them beneficial for multiple access technique and a scenario, where one of the far cluster users need to access the base station for high data rate service and vice versa. Finally, the modified Liang processing scheme for quantizing SCS-HBF-NOMA precoder is capable of mitigating the quantization error arising from the NLOS environment and the nature of sub-connected (HBF) structure under a low SNR regime.

Enhanced Signal Area Estimation in Radio-Communication Spectrograms Based on Morphological Image Processing

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method SemMixed to harden the ESC against SemAdv attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our MobileSC can even be more robust than classical block-wise communication systems in the low SNR regime.

Signal area estimation based on deep learning

In many practical application scenarios, radio communication signals are commonly represented as a spectrogram, which represents the signal strength measured at multiple discrete time instants and frequency points within a specific time interval and frequency band, respectively. In the context of spectrum occupancy measurements, the notion of Signal Area (SA) is defined as the rectangular region in the time–frequency domain where a signal is assumed to be present. Signal Area Estimation (SAE) is an important functionality in spectrum-aware wireless systems where spectrum usage monitoring is required. However, the conventional approaches to SAE have a limited estimation accuracy, in particular at low SNR. In this work, a novel technique for SAE is proposed using Deep Learning based on Artificial Neural Network (DL-ANN) for enhanced extraction of SA information from radio spectrograms. The performance of the proposed DL-ANN method is evaluated both with software simulations and hardware experiments, and the results are compared with several conventional methods from the literature, showing significant performance improvements. A key feature of the proposed method is the improvement in the SAE accuracy compared to other existing methods (in particular in the low SNR regime) and the capability to extract the location of the detected SAs automatically. Overall, the proposed technique is a promising solution for the automatic processing of radio spectrograms in spectrum-aware wireless systems.

Hybrid Analog and Digital Beamforming for RIS-Assisted mmWave Communications

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.

Compressive Sensing-Based Beam Alignment Schemes for Time-Varying Millimeter-Wave Channels

This paper considers the implementation of compressive sensing (CS) approaches for beam alignment (BA) in multiuser millimeter wave (mmWave) MIMO systems. We particularly consider wideband time-varying channels in the practical low SNR regime. We examine two different time scales for beam-switching in the BA training phase at both the base station (BS) and the user equipment (UE). We also compare different time scales for running the CS algorithm at the UE, with their corresponding overhead and complexity. We propose an overarching trial-based protocol that re- initializes the BA process at particular times. We also propose a new approach to designing the CS sensing matrix (SM), based on a deterministic construction. Rows of our proposed SM are Kronecker product decomposable, making it ideal for the BA problem. We show that when block-based beam switching is employed in combination with running the CS algorithm Every Epoch (CS-EE), our proposed SM gives superior performance compared to the other approaches. Moreover, our proposed overarching trial-based protocol enhances the performance even further. We also show that running the CS algorithm Every Block (CS-EB) outperforms CS-EE at the cost of higher complexity and overhead.

Research on Blind Recognition Algorithm of Channel Coding Based on One-Dimensional Convolutional Neural Network Under the Low SNR Regime

Research on Blind Recognition Algorithm of Channel Coding Based on One-Dimensional Convolutional Neural Network Under the Low SNR Regime

Multiple RISs Assisted Cell-Free Networks With Two-Timescale CSI: Performance Analysis and System Design

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(M)$ </tex-math></inline-formula> is achieved, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

Capacity Performance Analysis of 73 GHz Frequency Band for 5G Technology

Small cells, millimeter waves (mmW), and massive multiple-input multiple-output (MIMO) deployments have emerged as key technologies for mobile systems in the fifth generation (5G). However, a very few studies have been done on combining these three technologies into the cellular systems. In this paper, the authors provide an in-depth capacity analysis for the integrated small cells of mmW systems. Small cells are deployed for enhancing the capacity. It turns out that mmW signals are responsive to blockages, leading the line of sight (LOS) and non-line of sight (NLOS) conditions to have very different path loss rules. They divide power research into low signal-to-noise (SNR) and high SNR regimes based on signal-to-interference plus noise ratio. In the noise-dominated (low-SNR regime), the capacity analysis is derived by the simplest assumptions of the Shannon-Hartley theorem. The results of this study show that under NLOS and LOS scenarios, mmW frequency and distance between the user equipment and base station decrease logarithmically for system capacity.

The Double-Sided Information Bottleneck Function.

A double-sided variant of the information bottleneck method is considered. Let be a bivariate source characterized by a joint pmf . The problem is to find two independent channels and (setting the Markovian structure ), that maximize subject to constraints on the relevant mutual information expressions: and . For jointly Gaussian and , we show that Gaussian channels are optimal in the low-SNR regime but not for general SNR. Similarly, it is shown that for a doubly symmetric binary source, binary symmetric channels are optimal when the correlation is low and are suboptimal for high correlations. We conjecture that Z and S channels are optimal when the correlation is 1 (i.e., ) and provide supporting numerical evidence. Furthermore, we present a Blahut–Arimoto type alternating maximization algorithm and demonstrate its performance for a representative setting. This problem is closely related to the domain of biclustering.

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2−2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Compressed Random Access for Noncoherent Massive Machine-Type Communications With Energy Modulation

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">posteriori</i> (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

Alternate Distributed Beamforming with Power Allocation for Decode-and-Forward Multirelay Systems Using Buffers

In this paper, a novel transmission policy referred as alternate distributed beamforming (ADB) for buffer-aided half-duplex (HD) multirelay systems is proposed, in which the relays are divided into two groups and activated alternately in their transmitter and receiver modes, with one group receiving the same information broadcast from the source while the other group simultaneously transmitting the common messages to the destination via distributed beamforming in each time slot. It is worth noting that the relays used for reception and transmission are determined without the instantaneous channel state information (CSI). Theoretical analysis of the achievable throughput of the proposed scheme in Rayleigh fading channels is provided, and the approximate closed-form expressions are derived for multiantenna relay scenario, with single-antenna relay scenario as a special case. Moreover, the asymptotic expressions for the maximum achievable throughput of the proposed scheme and corresponding optimal power allocation in the high- and low-SNR regimes are derived as well. Numerical results in accordance with theoretical analysis demonstrate that the proposed policy achieves a significant improvement in the maximum achievable throughput compared with benchmark schemes. It is shown that for the ADB scheme, the almost or exact symmetric relay grouping may achieve the best performance in the high-SNR regime. It is also shown that increasing the number of antennas equipped at each relay is better than increasing the number of relays equipped with a single antenna when the total number of antennas at the relays is fixed.

An Improved Sliding Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction

The development of the digital radio frequency memory (DRFM) has led to the interrupted sampling repeater jamming (ISRJ) becoming increasingly popular in electronic countermeasure (ECM). It is coherent with the emitted signal and extremely limits radar target detection which significantly obstructs radar electronic countercountermeasure (ECCM). In this paper, we study the ISRJ suppression for pseudo random code continuous wave (PRC-CW) radars. First, the relationship between the ISRJ and the radar waveform is obtained by analyzing the ISRJ principle. Second, the intermittent feature of the ISRJ with matched filter sliding is discussed and used to determine the retransmitted sampled slices (RSS). Third, the jamming signal is reconstructed using the minimum residual criterion and excluded from the echo signal. In the proposed method, we make the most of the information of the jamming signal for improving the anti-ISRJ performance in the low SNR regimes. This information pertains to the amplitude of the jamming signal being considerably higher than that of the real target signal. Fourth, utilizing this characteristic of the jamming signal, we propose an improved sliding matched filter method based on the RSS reconstruction. Last, numerical simulations illustrate the effectiveness of the proposed method and validation of the theoretical analysis.

Likelihood Maximization and Moment Matching in Low SNR Gaussian Mixture Models

AbstractWe derive an asymptotic expansion for the log‐likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal‐to‐noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation‐Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo‐electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.

Novel Cooperative Automatic Modulation Classification Using Vectorized Soft Decision Fusion for Wireless Sensor Networks.

Cooperative automatic modulation classification (CAMC) using a swarm of sensors is intriguing nowadays as it would be much more robust than the conventional single-sensing-node automatic modulation classification (AMC) method. We propose a novel robust CAMC approach using vectorized soft decision fusion in this work. In each sensing node, the local Hamming distances between the graph features acquired from the unknown target signal and the training modulation candidate signals are calculated and transmitted to the fusion center (FC). Then, the global CAMC decision is made by the indirect vote which is translated from each sensing node’s Hamming-distance sequence. The simulation results demonstrate that, when the signal-to-noise ratio (SNR) was given by ≥ , our proposed new CAMC scheme’s correct classification probability could reach up close to . On the other hand, our proposed new CAMC scheme could significantly outperform the single-node graph-based AMC technique and the existing decision-level CAMC method in terms of recognition accuracy, especially in the low-SNR regime.

Adaptive Dijkstra’s Search Algorithm for MIMO detection

Employing Maximum Likelihood (ML) algorithm for signal detection in a large-scale Multiple-Input- Multiple-Output (MIMO) system with high modulation order is a computationally expensive approach. In this paper an adaptive best first search detection algorithm is proposed. The proposed Adaptive Dijkstra’s Search (ADS) algorithm exploits the resources available in the search procedure to reduce the required number of nodes to be visited in the tree. A tunable parameter is used to control the number of the best possible candidate nodes required. Unlike the conventional DS, the ADS algorithm results in signal detection with low computation complexity and quasi-optimal performance for systems under low and medium SNR regimes. Simulation results demonstrate a 25% computational complexity reduction, compared to the conventional DS.

Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

Spectrograms are a common form of time-frequency representation of wireless communication signals. In many practical scenarios spectrograms need to be processed to identify accurately the time-frequency region occupied by each individual radio transmission, which in this work is referred to as Signal Area (SA). Several methods have been proposed in the literature for Signal Area Estimation (SAE), however their performance degrades significantly in the low SNR regime. In this context, this work proposes a novel approach for SAE based on the use of two well-known techniques from the field of image processing, namely edge detection and flood fill. Edge detection is first employed to identify the edges of potential SAs and flood fill is then used to fill the area inside the estimated edges in order to produce a more accurate estimation of the SAs present in a spectrogram. The performance of three popular edge detection methods (gradient magnitude, Laplacian of Gaussian and Canny) is assessed both with simulations and experimental data. The obtained results show that the proposed strategy can improve significantly the performance of existing SAE methods in the low SNR regime (with estimation accuracy improvements up to 38&#x2013;45&#x0025; within the SNR interval from &#x2212;20 dB to &#x2212;10 dB) when used as a pre/post-processing stage, thus improving their sensitivity and effectively extending their overall SNR range of operation.