Signal Block Research Articles

BWSAR: A Single-Drone Search-and-Rescue Methodology Leveraging 5G-NR Beam Sweeping Technologies for Victim Localization

Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone’s navigation to emergency areas. Simulation results underscore BWSAR’s efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes.

Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm.

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.

Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems.

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

AbstractThis paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

Joint optimal beam forming and resource allocation in intelligent reflecting surface aided wireless power transfer rate splitting multiple access system

SummaryThe intelligent reflecting surface (IRS) has recently become the most promising technology to achieve maximum beam‐forming gain with a simultaneous wireless information and power transfer (SWIPT) system. Many existing studies perform a single IRS deployment or utilize space division multiple access (SDMA) and non‐orthogonal multiple access (NOMA) schemes. However, the existing schemes face high time complexity and block signal transmission for larger indoor communications. Hence, this article introduces a dual IRS‐aided multiuser SWIPT system by implementing rate‐splitting multiple access (RSMA) systems. To enhance the minimum achievable rate and ensure the minimum harvesting energy (HE), this study proposes a joint successive convex approximation with an integrated optimization algorithm (SCA‐IOA). The proposed algorithm jointly optimizes the reflecting beam forming and power splitting (PS) coefficient of all the users effectively. The simulation results demonstrate the effectiveness of the proposed techniques and produce an outstanding performance for deploying dual IRS and implementing RSMA compared to traditional SDMA and NOMA systems.

Detection performance of NR downlink initial access using synchronization signal block in millimeter-wave bands

Detection performance of NR downlink initial access using synchronization signal block in millimeter-wave bands

A 2D-DOA Sparse Estimation Method with Total Variation Regularization for Spatially Extended Sources

In this paper, a novel two-dimensional direction of arrival (2D-DOA) estimation method with total variation regularization is proposed to deal with the problem of sparse DOA estimation for spatially extended sources. In a general sparse framework, the sparse 2D-DOA estimation problem is formulated with the regularization of extended source characteristics including spatial position grouping, acoustic signal block sparse, and correlation features. An extended sources acoustic model, two-dimensional array manifold and its complete representation, total variation regularization penalty term, and the regularization equation are built, and are utilized to seek the solutions where the non-zero coefficients are grouped together with optimum sparseness. A total variation sparse 2D-DOA estimation model is constructed by combining total variation regularization with LASSO. The model can be easily solved by the convex optimization algorithm, and the solving process can promote the sparsity of the solution on the spatial derivatives and the solution itself. The theoretical analysis results show that the steps of decorrelation processing and angle matching of traditional 2D-DOA estimation methods could be avoided when adopting the proposed method. The proposed method has better robustness to noise, better sparsity, and faster estimation speed with higher resolution than traditional methods. It is promising to provide a coherent sources sparse representation of a non-strictly sparse field.

One-Shot Blind Frequency-Domain Nonlinear Equalization for Millimeter-Wave Fiber-Wireless IFoF Mobile Fronthaul System

Nonlinear equalization (NE) plays a pivotal role in the analog intermediate-frequency over fiber (IFoF) mobile fronthaul (MFH) system demanding a high signal fidelity. This work proposes and experimentally validates a novel blind frequency-domain NE (FD-NE) approach for millimeter-wave (MMW) fiber-wireless (FiWi) IFoF MFH systems. This proposed blind FD-NE scheme relies on the FD two-block cascade Hammerstein NE structure and particular pairs of M-ary phase shift keying (MPSK) and M-ary amplitude shift keying (MASK) subcarriers. It features lower computational complexity, higher transmission capacity, and comparable equalization performances to the widely-employed training-assisted memory polynomial (MP) NE schemes. Moreover, it only requires a small fraction of subcarriers in a single received signal block to be modulation-format constrained, which renders one-shot blind learning of NE coefficients and thus achieves symbol-by-symbol channel tracking. The performance of the proposed NE method is experimentally evaluated in MMW FiWi IFoF MFH systems operating at 5G NR frequency range 2-1 (FR2-1) n258 band (24.25 GHz to 27.50 GHz) and FR2-2 (52.6 GHz to 71 GHz). The obtained experimental results show that our proposal can effectively improve the nonlinearity tolerances of coherent 25-GHz and non-coherent 60-GHz FiWi IFoF MFH systems transmitting multiple 64QAM/256QAM IF OFDM signals.

IPos-5G: Indoor Positioning via Commercial 5G NR CSI

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. Firstly, a module of quality control is applied for CSI preprocessing. Secondly, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Thirdly, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.

Detecting Intelligent Jamming on Physical Broadcast Channel in 5G NR

In fifth generation (5G) new radio (NR), since the synchronization signal blocks are unencrypted during the initial access, an intelligent adversary can detect these signals to obtain the full physical cell identity (PCI) by sniffing, and then use the PCI to attack physical broadcast channel (PBCH) extraction by targeted jamming. Such kind of PBCH intelligent jamming (PBCH-IJ) will cause the failure of the master information block (MIB) decoding, and further result in severe denial of services for those users who want to access this PCI cell. In this letter, we propose to exploit the principal direction of PBCH demodulation reference signal space to detect PBCH-IJ at the user side, which is motivated by the fact that such principal direction under the low mobility scenarios will be significantly impacted if PBCH-IJ happens. Numeric results evaluate and confirm the effectiveness of our detection method.

Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks

The current 5G-NR standard includes the transmission of multiple synchronization signal blocks (SSBs) in different directions to be exploited in beamforming techniques. However, choosing a pair of these beams leads to performance degradation, mainly for the cases where the transmit and receive beams are not aligned, because it considers that only few fixed directions among wide beams are established. Therefore, in this article, we design a new 3GPP-standard- compliant beam pair selection algorithm based on secondary synchronization signal (SSS) angle estimation (BSAE) that makes use of multiple synchronization signal blocks (SSBs) to maximize the reference signal received power (RSRP) value at the receiver. This optimization is performed using the SSSs present in each SSB to perform channel estimation in the digital domain. Afterwards, the combination of those estimations is used to perform equivalent channel propagation matrix estimation without the analog processing effects. Finally, through the estimated channel propagation matrix, the angle that maximizes the RSRP is determined to compute the most suitable beam. The proposed algorithm was evaluated and compared with a conventional beam pair selection algorithm. Ours has better performance results. Furthermore, the proposed algorithm achieved performance close to the optimal performance, where all channel state information (CSI) is available, emphasizing the interest of the proposed approach for practical 5G mmWave mMIMO implementations.

Effects of internal surface roughness and viscous friction on mass flow rate and conductive heat transfer across a pipe element: Simulink Approach

The study, effects of internal surface roughness and viscous friction on mass flow rate and conductive heat transfer across a pipe element using simulink approach was successfully achieved. Block models were used to represent all the elements of pipe flow model. Pipe element was modeled to retain hydraulic diameter of 0.4 m within a length of 100 m. The shape factor and internal surface roughness were chosen to be 80 and 3e+3m, respectively. Initial temperature and pressure were set to 300k or 27ºC and 1 atm. turbulent regime Nusselt number correlation coefficients were 0.023, 0.8, 0.33, 0, and 0. The mass flow rate from the reservoir had initial value of 0.6 kg/s and signal block was adjusted to 1, 6, 5 for slope, start and maximum respectively. With Ode15s solver, simulation was allowed to run for 15seconds. System model mass flow rate, conductive heat transfer and temperature difference were found to be 0.6kg/s (same with initial value), 5.0346J/s and -80K or 193ºC within 15seconds of simulation. Findings depicted the effects of internal surface roughness and viscous friction on mass flow rate and conductive heat transfer. Also, simulation was run with the shape factor and internal surface roughness chosen to be 64 and 1.5e-5m, respectively. Under viscous friction influence, initial temperature and pressure were set to 298k or 25ºC and 1 atm. turbulent regime Nusselt number correlation coefficients were 0.023, 0.8, 0.33, 0, and 0. The mass flow rate from the reservoir had initial value of 0.6 kg/s. Results also showed that the system model mass flow rate, conductive heat transfer and temperature difference were found to be 0.058kg/s, -275J/s and 100K or -173ºC within 15seconds of simulation. Hence, increasing value of internal surface roughness increases conductive heat transfer at a constant mass flow rate of fluid and increasing value of viscous friction, decreases mass flow rate of fluid as well as conductive heat transfer across the pipe wall.

TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.

The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA. We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors. DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. After implementation, the accuracy of each module was evaluated via systematic block-by-block validation. The TorchDIVA model is shown to produce outputs that closely match those of the original DIVA model, with a negligible difference between the two. We additionally present an example of the extensibility of TorchDIVA as a research platform. Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave. A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production. The results indicate improved speech quality metrics in the DiffWave-enhanced output as compared to the baseline. This enhancement would have been difficult or impossible to accomplish in the original Matlab implementation. This proof-of-concept demonstrates the value TorchDIVA can bring to the research community. Researchers can download the new implementation at: https://github.com/skinahan/DIVA_PyTorch.

Adaptive Extended Kalman Filter-Based Fusion Approach for High-Precision UAV Positioning in Extremely Confined Environments

For unmanned aerial vehicle (UAV)-based smart inspection in extremely confined environments, it is impossible for precise UAV positioning with global positioning system, owing to the satellite signal block. Therefore, the ultrawideband (UWB)-based technology has attracted extensive attention under such circumstances. However, due to the unpredictable propagation condition and the time-varying operational environment, the localization performance oscillation caused by the changing measurement noise may lead to the instability of UAV. To mitigate the effects, in this article, a high-precision UAV positioning system which integrates the inertial measurement unit and UWB with the adaptive extended Kalman filter (EKF) is proposed. Compared with the traditional EKF-based approach, the estimated and recorded information from previous processes is exploited to adaptively estimate and further control the estimation of the noise covariance matrices for the performance improvement. Finally, simulations and experiments have been conducted in extremely confined environments. According to the results, the proposed algorithm can significantly improve the position update rate, the median positioning error, the 95 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{th}}$</tex-math></inline-formula> percentile positioning error, and the average standard deviation into 88 Hz, 0.102 m, 0.192 m, and 0.052 m, which is applicable for applications in focused environments.

Performance of LiDAR-SLAM-based PNT with initial poses based on NDT scan matching algorithm

To achieve higher automation level of vehicles defined by the Society of Automotive Engineers, safety is a key requirement affecting navigation accuracy. We apply Light Detection and Ranging (LiDAR) as a main auxiliary sensor and propose LiDAR-based Simultaneously Localization and Mapping (SLAM) approach for Positioning, Navigation, and Timing. Furthermore, point cloud registration is handled with 3D Normal Distribution Transform (NDT) method. The initial guess of the LiDAR pose for LiDAR-based SLAM comes from two sources: one is the differential Global Navigation Satellite System (GNSS) solution; the other is Inertial Navigation System (INS) and GNSS integrated solution, generated with Extended Kalman Filter and motion constraints added, including Zero Velocity Update and Non-Holonomic Constraint. The experiment compares two initial guesses for scan matching in terms navigation accuracy. To emphasize the importance of a multi-sensor scheme in contrast to the conventional navigation method using the stand-alone system, the tests are conducted in both open sky area and GNSS signal block area, the latter might cause Multipath and Non-Line-Of-Sight effects. To enhance the navigation accuracy, the Fault Detection and Exclusion (FDE) mechanism is applied to correct the navigation outcome. The results show that the application of NDT and FDE for INS/GNSS integrated system can not only reach where-in-lane level navigation accuracy (0.5 m), but also enable constructing the dynamic map.

Analysis of a direct separation method based on adaptive chirplet transform for signals with crossover instantaneous frequencies

In many applications, it is necessary to retrieve the sub-signal building blocks of a multi-component signal, which is usually non-stationary in real-world and real-life applications. Empirical mode decomposition (EMD), synchrosqueezing transform (SST), signal separation operation (SSO), and iterative filtering decomposition (IFD) have been proposed and developed for this purpose. However, these computational methods are restricted by the specification of well-separation of the sub-signal frequency curves for multi-component signals. On the other hand, the chirplet transform-based signal separation scheme (CT3S) that extends SSO from the two-dimensional “time-frequency” plane to the three-dimensional “time-frequency-chirp rate” space was recently proposed in our recent work to remove the frequency-separation specification, and thereby allowing “frequency crossing”. The main objective of this present paper is to carry out an in-depth error analysis study of instantaneous frequency estimation and component recovery of the CT3S method.

SSB-Based Signal Processing for Passive Radar Using a 5G Network

This paper presents an alternative processing chain for the passive radar using fifth-generation (5 G) standard technology for broadband cellular networks as illuminators of opportunity. The proposition is to use a 5 G synchronization signal block's (SSB) periodically transmitted modulated pulse in 5G-based passive coherent location (PCL) system processing. Although the SSB periodicity limits the velocity ambiguity, the paper describes a solution to tackle this problem in a single target scenario. The method is advantageous when there is a lack of transmission in the telecommunication channel, and the 5 G SSB is the only existing signal. The paper proposes a signal processing pipeline for 5G-based PCL that is inspired by passive radars using non-cooperative pulse radar as an illumination source. The method has been validated using simulated and real-life 5 G data measurements. The results presented in the paper show the possibility of detecting a moving target with a lack of data transmission in the 5 G network, using only the SSB when the classical passive radar signal processing fails. The presented results prove the possibility for a significant increase of 5 G network-based PCL utilization in short-range applications.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity of fifth generation (5G) networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation (4G) network technologies that demand always for varied control and data signalling based on control base station (CBS) and data base station (DBS). Hence, this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network. As the new radio (NR) based 5G network is configured to transmit signal blocks for every 20 ms, the proposed algorithm implements withstanding capacity of on or off based energy switching, which in-turn operates in wide range control by carrying out reduced computational complexity. The proposed Wide range of control for base station in green cellular network using sleep mode for switch (WGCNS) algorithm toon and off the base station will work in heavy load with neighbouring base station. For reducing the overhead duration in air, heuristic versions of the algorithm are proposed at the base station. The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings. The proposed algorithm reduces 40% to 83% of residual energy based on the traffic pattern of the urban scenario.

An Enhanced Group Key-Based Security Protocol to Protect 5G SON Against FBS

Network operators are attempting many innovations and changes in 5G using self-organizing networks (SON). The SON operates on the measurement reports (MR), which are obtained from user equipment (UE) and secured against malware and userspace programs. However, the synchronization signal block that the UE relies on to measure the wireless environment configured by a base station is not authenticated. As a result, the UE will likely gauge the wrong wireless environment configured by a false base station (FBS) and transmit the corresponding MR to the serving base station, which poisons the data used for 5G SONs. Therefore, the serving base stations must verify the authenticity of the MR. The 3GPP has advocated numerous solutions for this issue, including the use of public key certificates, identity-based keys, and group keys. Although the solution leveraging group keys have better efficiency and practicality than the other two, they are vulnerable to security threats caused by key leaks via insiders or malicious UE. In this paper, we analyze these security issues and propose an improved group key protocol that uses a new network function, called a broadcast message authentication network function (BMANF), which validates broadcasted messages on behalf of the UE. The protocol operates in two phases: initial and verification. During the initial phase, the 5G core network distributes a shared secret key to the BMANF and UE, allowing the latter to request an authentication ticket from the former. During the verification phase, the UE requests the BMANF to validate the broadcasted messages received from base stations using the ticket and its corresponding shared key. For evaluation, we formally verified the proposed protocol, which was then compared with alternative methods in terms of computing cost. As a result, the proposed protocol fulfills the security requirements and shows a lower overhead than the alternatives.

Narrowband Internet-of-Things to Enhance the Vehicular Communications Performance

The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband Internet-of-Things to enhance the robustness of the vehicular communication system. Accordingly, the system’s QoS is enhanced. This enhancement is based on proposing two parts to cover the latency and the harmonics issues, in addition to proposing a distributed antenna configuration for the moving vehicles under a machine learning benchmark, which uses the across-entropy algorithm. The proposed environment has been simulated and compared to the state-of-the-art work performance. The simulation results verify the proposed work performance based on three different parameters; namely the latency, the mean squared error rate, and the transmitted signal block error rate. From these results, the proposed work outperforms the literature; at the probability of 10−3, the proposed work reduces the peak power deficiency by almost 49%, an extra 23.5% enhancement has been attained from the self-interference cancellation side, and a bit error rate enhancement by a ratio of 31%.