Minimum Euclidean Distance Research Articles

Justification of the Empirical Expression for Assessing the Noise Immunity of Quadrature Modulation Signals

An approach to the substantiation of the empirical formula for calculating the bit error probability of quadrature modulation signals in terms of the average signal energy and the minimum Euclidean distance is considered. An analytical description of quadrature synthesis signals in the time and frequency continuum is presented. An approach to assessing the noise immunity of quadrature modulation signals from the standpoint of the indicator of the average signal energy and the Euclidean distance is considered. The equivalence of various well-known approaches to the analytical calculation of the bit error probability is shown. Graphic materials are presented, as well as simulation results. Empirically substantiated is a universal expression for assessing the noise immunity of receiving quadrature modulation signals, based on the differences in the average energy value and the value of the minimum Euclidean distance. Its generality with known expressions is shown.

Performance improvement of non-orthogonal multiple access with a 3D constellation and a 2D IFFT modulator.

In this paper, we propose a performance improvement of non-orthogonal multiple access (NOMA) with a three-dimensional (3D) constellation and a two-dimensional Inverse Fast Fourier Transform IFFT modulator (2D-IFFT) for the passive optical network (PON). Two kinds of 3D constellation mapping are designed for the generation of a three-dimensional NOMA (3D-NOMA) signal. Higher-order 3D modulation signals can be obtained by superimposing signals of different power levels by pair mapping. Successive interference cancellation (SIC) algorithm is implemented at the receiver to remove interference from different users. Compared with the traditional two-dimensional NOMA (2D-NOMA), the proposed 3D-NOMA can increase the minimum Euclidean distance (MED) of constellation points by 15.48%, which enhances the bit error rate (BER) performance of the NOMA. The peak-to-average power ratio (PAPR) of NOMA can be reduced by 2 dB. A 12.17 Gb/s 3D-NOMA transmission over 25 km single-mode fiber (SMF) is experimentally demonstrated. The results show that at the bit error rate (BER) of 3.8 × 10-3, the sensitivity gain of the high-power signals of the two proposed 3D-NOMA schemes is 0.7 dB and 1 dB compared with that of 2D-NOMA under the condition of the same rate. Low-power level signal also has 0.3 dB and 1 dB performance improvement. Compared with 3D orthogonal frequency-division multiplexing (3D-OFDM), the proposed 3D-NOMA scheme could potentially expand the number of users without obvious performance degradation. Due to its good performance, 3D-NOMA is a potential method for future optical access systems.

Power allocation for offset spatial modulation

AbstractOffset spatial modulation (OSM) is an attractive variant in the family of spatial modulation technology, which is capable of reducing the frequency of radio frequency chain switching in practical implementations. This paper elaborates on the benefits of adaptive power allocation (PA) conceived for OSM systems. First, a closed‐form solution of the optimal PA algorithm is derived for the case of two transmit antennas, which maximizes the minimum Euclidean distance (ED) between the received signal points. Moreover, based on the above solutions, a low‐complexity iterative PA algorithm is proposed for larger numbers of transmit antennas, by exploiting the EDs of a few dominant error vectors. Furthermore, for striking a flexible trade‐off between the bit error performance and complexity, an iterative convex approximation based PA algorithm is also developed. Finally, simulation results are presented to show that the proposed PA algorithms provide considerable improvements on the original OSM in terms of the bit error rate.

Delay Optimization of Conventional Non-Coherent Differential CPM Detection

The conventional non-coherent differential detection of continuous phase modulations (CPM) is quite robust to channel impairments such as phase and Doppler shifts. Its implementation is on top of that simple. It consists in multiplying the received baseband signal by its conjugate version delayed by one symbol period. However it suffers from a signal-to-noise ratio gap compared to the optimum coherent detection. In this letter, we improve the error rate performance of the conventional differential detection by using a delay higher than one symbol period. We derive the trellis description as well as the branch and cumulative metrics that take into account a delay of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> symbol periods. We then determine an optimized delay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K_{\mathrm {opt}}$ </tex-math></inline-formula> based on the minimum Euclidean distance between two differential signals for some popular CPM formats. The optimized values are confirmed by error rate simulations.

MSMC-TTS: Multi-Stage Multi-Codebook VQ-VAE Based Neural TTS

This paper aims to improve neural TTS with vector-quantized, compact speech representations. We propose a Vector-Quantized Variational AutoEncoder (VQ-VAE) based feature analyzer to encode acoustic features into sequences with different time resolutions, and quantize them with multiple VQ codebooks to form the Multi-Stage Multi-Codebook Representation (MSMCR). The TTS system, MSMC-TTS, is proposed to predict better speech via this representation. In prediction, the multi-stage predictor is trained to map the input text sequence to MSMCRs in stages, by minimizing Euclidean distance and “triplet loss”. In synthesis, the neural vocoder converts ground-truth or predicted MSMCRs into speech waveforms. The proposed system is trained with single-speaker TTS datasets and tested in various scenarios for comprehensive evaluation. In TTS evaluation, MSMC-TTS obtains MOS of 4.34 and 4.10 on English and Chinese datasets, which significantly outperforms VITS with scores of 3.78 and 3.90. Meanwhile, compared with Mel-Spectrograms, the domain discrepancy between prediction and ground truth is lower in MSMCRs with the higher Domain-classification Error Rate (DER). Furthermore, this system shows lower modeling complexity and data size requirements, preserving excellent performance even with fewer model parameters or training data. The noticeable improvement in analysis-synthesis and TTS from multiple codebooks and stages also validate them as vital components in seeking a more profitable speech representation and building high-performance neural TTS.

FAULT DETECTION OF WIND TURBINE PITCH CONNECTION BOLTS BASED ON TSDAS-SMOTE WITH XGBOOST

For the problem of class-imbalance in the operation monitoring data of wind turbine (WT) pitch connecting bolts, an improved Borderline-SMOTE oversampling method based on “two-step decision” with adaptive selection of synthetic instances (TSDAS-SMOTE) is proposed. Then, TSDAS-SMOTE is combined with XGBoost to construct a WT pitch connection bolt fault detection model. TSDAS-SMOTE generates new samples by “two-step decision making” to avoid the problem of class–class boundary blurring that Borderline-SMOTE tends to cause when oversampling. First, the nearest neighbor sample characteristics are perceived by the fault class samples in the first decision step. If the characteristics of this fault class sample are different from the characteristics of all its nearest neighbor samples, the fault class sample is identified as interference and filtered. Second, the faulty class samples in the boundary zone are extracted as synthetic instances to generate new samples adaptively. Finally, the normal class samples in the boundary zone are used to perceive the unqualified new generated samples in the boundary zone based on the minimum Euclidean distance characteristics, and these unqualified samples are eliminated. For the second step of decision making, since the first step decision removes some of the newly generated samples, the remaining fault class samples without interference samples and boundary zone samples are used as synthetic instances to continue adaptively generating new samples. Thus, a balanced data set with clear class–class boundary zone is obtained, which is then used to train a WT pitch connection bolt fault detection model based on the XGBoost algorithm. The experimental results show that compared with six popular oversampling methods such as Borderline-SMOTE, Cluster-SMOTE, [Formula: see text]-means-SMOTE, etc., the fault detection model constructed by the proposed oversampling method is better than the compared fault detection models in terms of missed alarm rate (MAR) and false alarm rate (FAR). Therefore, it can well achieve the fault detection of large WT pitch connection bolts.

On Practical RIS-Aided OFDM With Index Modulation

Reconfigurable intelligent surface (RIS)-aided communication has been regarded as one of the promising technologies for future wireless networks with regards to controlling the propagation environment and enhancing the quality of the end-to-end communication links. In this study, considering the importance of index modulation (IM) and RIS-aided schemes in 6G networks, first, an RIS-aided orthogonal frequency division multiplexing (OFDM)-IM system design is proposed. Previous studies focusing on RIS-aided OFDM systems are based on an ideal reflection model, where the constant amplitude and variable phase shift of the RIS are independent of the frequency of the incoming signal. However, in realistic applications, this ideal RIS model may not be accurate since both the phase shift and amplitude of RIS elements depend on the frequency, which makes their effect significant, especially for wideband systems. Therefore, this study primarily considers a practical RIS model through designing the RIS reflection coefficients for OFDM-IM systems. In order to further enhance the bit error rate (BER) performance of the RIS-aided OFDM-IM system, an exhaustive search-based optimization algorithm that maximizes the average minimum Euclidean distance (AMED) within the subblocks is also proposed. Moreover, extensive computer simulation results are produced in order to illustrate the performance improvement of the proposed algorithm over the reference algorithms in the literature. The proposed AMED-based reflection coefficient design algorithm provides better BER performance for RIS-aided OFDM-IM systems concerning the presented algorithms.

A Reference Point-Based Evolutionary Algorithm Solves Multi and Many-Objective Optimization Problems: Method and Validation.

The integration of a decision maker's preferences in evolutionary multi-objective optimization (EMO) has been a common research scope over the last decade. In the published literature, several preference-based evolutionary approaches have been proposed. The reference point-based non-dominated sorting genetic (R-NSGA-II) algorithm represents one of the well-known preference-based evolutionary approaches. This method mainly aims to find a set of the Pareto-optimal solutions in the region of interest (ROI) rather than obtaining the entire Pareto-optimal set. This approach uses Euclidean distance as a metric to calculate the distance between each candidate solution and the reference point. However, this metric may not produce desired solutions because the final minimal Euclidean distance value is unknown. Thus, determining whether the true Pareto-optimal solution is achieved at the end of optimization run becomes difficult. In this study, R-NSGA-II method is modified using the recently proposed simplified Karush-Kuhn-Tucker proximity measure (S-KKTPM) metric instead of the Euclidean distance metric, where S-KKTPM-based distance measure can predict the convergence behavior of a point from the Pareto-optimal front without prior knowledge of the optimum solution. Experimental results show that the algorithm proposed herein is highly competitive compared with several state-of-the-art preference-based EMO methods. Extensive experiments were conducted with 2 to 10 objectives on various standard problems. Results show the effectiveness of our algorithm in obtaining the preferred solutions in the ROI and its ability to control the size of each preferred region separately at the same time.

Design Low Complexity SCMA Codebook Using Arnold’s Cat Map

In 5G wireless communications, sparse code multiple access (SCMA) – a multi-dimensional codebook based on a specific category of the non-orthogonal multiple access (NOMA) technique - enables many users to share non-orthogonal resource components with a low level of detection complexity. The multi-dimensional SCMA (MD-SCMA) codebook design presented in this study is based on the constellation rotation and interleaving method. Initially, a subset of the lattice Z 2 is used to form the mother constellation’s initial dimension. The first dimension is then rotated to produce other dimensions. Additionally, interleaving is employed for even dimensions to enhance fading channel performance. Arnold’s chaotic cat map is proposed as the interleaving method to reduce computational complexity. Performance of the SCMA codebook based on interleaving is evaluated by comparing it with selected codebooks for SCMA multiplexing. The metrics used for performance evaluation purposes include bit error rate (BER), peak to average power ratio (PAPR), and minimum Euclidean distance (MED), as well as complexity. The results demonstrate that the suggested codebook with chaotic interleaving offers performance that is equivalent to that of the conventional codebook based on interleaving. It is characterized by lower MED and higher BER compared to computer-generated and 16-star QAM codebook design approaches, but its complexity is lower than that of the conventional codebook based on interleaving.

Optimal finite alphabet scheme for NOMA uplink channels

AbstractThe design of an optimal non‐orthogonal multiple access (NOMA) transmission scheme with finite alphabet inputs for a typical two‐user uplink wireless communication system is investigated in which each terminal is equipped with a single antenna. Each of the two users utilises a four quadrature amplitude modulation (4‐QAM) constellation to transmit information data to a common base station, and the receiver employs a maximum likelihood (ML) detector to jointly estimate both transmitted signals. Assuming the availability of channel state information at both the transmitters and the receiver, it is aimed to design a pair of scalar beamformers for the two users such that the minimum Euclidean distance between elements of the received sum‐constellation is maximised subject to the power constraints on the users. A thorough consideration of all the different conditions results in the derivation of a closed‐form optimal beamformer design. As well, examination of the construction of sum‐constellation resulted from the optimum design directly leads to the unique decoding of the original transmitted signal of each user. To facilitate practical implementation, a fast decoding procedure of the optimum NOMA scheme is further developed. The corresponding theoretic probability of ML detection error is also derived. The theoretical development of an optimum sum‐constellation for the basic 2‐user 4‐QAM system provides a solid platform for the derivation of an optimum sum‐constellation for a K‐user and/or M‐QAM system. Indeed, a simple development of the basic sum‐constellation map facilitates such extensions. Numerical simulations not only demonstrate that the performance of the fast decoder agrees closely with the theoretical analysis, but also verify that it is superior in performance to other existing NOMA designs for the same system under high signal‐to‐noise ratio.

Signal Vector Design for Quadrature Index Modulation

In this paper, we propose a new technique of quadrature index modulation with signal vector design (QIM-SSD), which can directly transmit a three-dimensional (3D) constellation symbol and expand an antenna index (AI) bit. The basic principle of QIM-SSD is as follows: two components of a mapped 3D signal symbol are first modulated on the upper and lower parts of transmit antennas (TAs) by two specific AI vectors with two blocks of AI bits, respectively, resulting in a space vector symbol. The remaining one component is modulated on one TA activated by the specific AI vector with one block of AI bits, then forming another space vector symbol. Furthermore, based on the resulted two space vector symbols, an additional bit is used to determine the real and imaginary parts of a complex transmit vector. The research of this paper is summarized as follows: firstly, the system model of QIM-SSD is introduced in detail. Then, the minimum Euclidean distance and the average bit error probability (BEP) are analyzed. With assumption of perfect channel estimation and maximum likelihood (ML) detection, simulation results demonstrate that the average BEP performance of QIM-SSD is better than that of the conventional QIM with three dimensional constellation and QSM at the same spectral efficiency by MATLAB simulator.

A Fast and Robust Heterologous Image Matching Method for Visual Geo-Localization of Low-Altitude UAVs

Visual geo-localization can achieve UAVs (Unmanned Aerial Vehicles) position during GNSS (Global Navigation Satellite System) denial or restriction. However, The performance of visual geo-localization is seriously impaired by illumination variation, different scales, viewpoint difference, spare texture, and computer power of UAVs, etc. In this paper, a fast detector-free two-stage matching method is proposed to improve the visual geo-localization of low-altitude UAVs. A detector-free matching method and perspective transformation module are incorporated into the coarse and fine matching stages to improve the robustness of the weak texture and viewpoint data. The minimum Euclidean distance is used to accelerate the coarse matching, and the coordinate regression based on DSNT (Differentiable Spatial to Numerical) transform is used to improve the fine matching accuracy respectively. The experimental results show that the average localization precision of the proposed method is 2.24 m, which is 0.33 m higher than that of the current typical matching methods. In addition, this method has obvious advantages in localization robustness and inference efficiency on Jetson Xavier NX, which completed to match and localize all images in the dataset while the localization frequency reached the best.

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

This letter presents an enhanced design of multi-dimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain non-orthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems.

Beamforming Design for Integrated Sensing and Communication Systems With Finite Alphabet Input

Joint beamforming for integrated sensing and communication (ISAC) is an efficient way to combine two functionalities in a system at the waveform level. This letter proposes a beamforming design for ISAC systems with finite alphabet signaling. We formulate a problem to maximize the minimum Euclidean distance (MMED) among noise-free received signal vectors under a sensing constraint and a given power constraint. To tackle the formulated optimization problem, we transform it into a semi-definite programming (SDP) and solve it by the semi-definite relaxation (SDR) method. Comprehensive comparisons with existing schemes show that our proposed beamforming offers lower symbol error rate (SER), higher mutual information, and also better sensing performance than existing ISAC beamforming designs.

Dynamic Gesture Recognition of A-Mode Ultrasonic Based on the DTW Algorithm

At present, the research on gesture recognition based on ultrasonic signals is mostly about static gestures. Compared with static gestures, dynamic gestures reflect human intent more intuitively. However, the speed difference of dynamic gestures will cause a mismatch in time series. To tackle this problem, we proposed a dynamic gesture recognition scheme based on the dynamic time warping (DTW) algorithm. First, the ultrasonic signals of the forearm muscles were acquired by four A-mode ultrasonic transducers. Second, the original signals underwent Gaussian filtering, feature extraction, normalization, and principal component analysis (PCA) dimensionality reduction to obtain the main feature information and convert it into time series. Finally, the time series of dynamic gestures were matched based on the DTW algorithm, and the minimum Euclidean distance between different time series was calculated for prediction. We tested five dynamic gestures on ten subjects. The experiments were conducted for a total of ten rounds, and only one round was used as the training set. Results reveal that using the max feature can get an accuracy of 88.0% ± 5.9%. In addition, the energy feature (87.5% ± 6.4%) and the mean feature (85.2% ± 6.9%) also performed well. This research realizes the dynamic gesture recognition of A-mode ultrasonic for the first time, proving the potential of ultrasonic signals, and providing a wider range of use scenarios.

Generalized Nearest Neighbor Decoding

It is well known that for Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between a codeword and the received channel output vector, is the maximum likelihood solution and hence capacity-achieving. Nearest neighbor decoding remains a convenient and yet mismatched solution for general channels, and the key message of this paper is that the performance of nearest neighbor decoding can be improved by generalizing its decoding metric to incorporate channel state dependent output processing and codeword scaling. Using generalized mutual information, which is a lower bound to the mismatched capacity under independent and identically distributed codebook ensemble, as the performance measure, this paper establishes the optimal generalized nearest neighbor decoding rule, under Gaussian channel input. Several restricted forms of the generalized nearest neighbor decoding rule are also derived and compared with existing solutions. The results are illustrated through several case studies for fading channels with imperfect receiver channel state information and for channels with quantization effects.

Performance Enhancement of Cluster-Based Ad Hoc On-Demand Distance Vector Routing in Vehicular Ad Hoc Networks

Recently, extensive research has focused on the design of clustering algorithms for grouping vehicles in a set of clusters in Vehicular Ad Hoc Networks (VANETs). However, due to the dynamic nature of VANETS, frequently connected or detached nodes threaten the stability of the network. When these nodes are clustered heads (CHs), the impact of these disruptions on network performance is even worse. Therefore, the stability of clusters is an important issue that should be maintained to enhance the performance of the network with minimum data loss. In this article, a novel clustering approach is proposed, which is based on the average weight concept of three main attributes, namely, the geostationary coordinates (x, y) and drop density. The average of the above-mentioned attributes is calculated and compared with the values of other vehicles’ attributes. Those vehicles having minimum average values are dropped and not to be considered in the cluster formation. The cluster members having minimum Euclidean distance are considered as Cluster Head (CH) until a certain checkpoint is not attained. The checkpoints are created in order to generate the social score of the vehicles. Once the social score is attained, a multiobjective framework is developed, which considers minimizing the Euclidian distance and maximizing the social score. The social score is generated based on the Quality of Service (QoS) attained attributes, and the data transmission is performed using Ad Hoc On-Demand Distance Vector (ADOV) routing protocol. A comparison of the proposed work and existing work has also been undertaken to demonstrate the improvement in the proposed work. As a result, throughput, PDR, network longevity, and energy consumption have been improved by 28.27%, 20.9%, 23.12%, and 27.48 percent, respectively. Extensive simulations show that the proposed scheme, unlike other popular clustering algorithms, can significantly increase the stability of the network by extending the life of the cluster head.

A Low-Complexity Codebook Design Scheme for SCMA Systems Over an AWGN Channel

As a promising wireless access technique, sparse code multiple access (SCMA) directly maps the input bits to the transmitted multidimensional codewords according to the SCMA codebook. In general, SCMA codebook optimization and detector simplification are independent of each other. In this article, we propose a simplified SCMA codebook with a separable structure for implementing a low-complexity detector. For codebook design, a separable codebook structure is proposed to simplify the maximization of the minimum Euclidean distance (MED). With this structure, multiple one-dimensional complex codebooks corresponding to one resource element (RE) are determined by maximizing the MED of the superimposed codewords. Then, the entire codebook set is obtained based on a structure indicator matrix and the separable codebook structure. The proposed codebook has a larger MED than obtained by existing codebooks. Benefiting from the separable codebook structure, the signals transmitted over each RE can be independently used to recover the corresponding input bit information. Therefore, we propose a parallel maximum a posteriori (P-MAP) detector consisting of several low-complexity MAP detectors to reduce the complexity of the conventional multiuser message passing (MP) detector in SCMA. At the receiver, each RE is assigned a low-complexity MAP detector. Simulation results show that the proposed codebook outperforms the known optimal codebook over an AWGN channel. Moreover, the run time of the P-MAP detection algorithm is reduced by 99.8% compared to the Log-MPA algorithm. With the proposed codebook design scheme and the P-MAP detector, an SCMA unit can be separated into two independent non-SCMA units, which significantly increases system flexibility.

Eigendecomposition-Precoded Faster-Than-Nyquist Signaling With Index Modulation

In this paper, we propose a precoded faster-than-Nyquist (FTN) signaling technique for time-domain single-carrier index-modulated symbol transmission. More precisely, eigenvalue decomposition (EVD) precoding is adopted for the FTN transmission of data bits modulated by single-carrier time-domain index modulation (IM). While the FTN scheme increases the spectral efficiency and data rate by increasing the density of transmit symbols, the time-domain IM works towards the same objective while maintaining symbol sparsity. We analytically derive the constrained capacity of the proposed system. Our simulation results show that the proposed scheme has better bit error ratio (BER) performance than the conventional FTN-IM scheme, particularly for the scenario of a higher packing ratio. With the proposed scheme, a 2.5 dB performance gain is observed at the BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> , where the packing ratio is 0.7 and the roll-off factor is 0.5 in the channel-uncoded scenario. We further analyze our scheme’s performance by characterizing its minimum Euclidean distance and inter-symbol interference. Furthermore, we present a three-stage serially concatenated iterative detection architecture for the proposed EVD-FTN-IM signaling scheme. It is demonstrated in our simulations that a near-capacity BER performance is achievable in the proposed turbo-coded FTN-IM scheme.

Indoor Localization for Intrusion Detection and Monitoring RSSI Based

Abstract: A real time machine for Intrusion detection &amp; monitoring primarily based on wireless sensor network technology is designed by using the use of the mote that's developed &amp; designed in as the communication module inside the network. This dissertation tells tool-free Passive Localization with RSSI. Primary objective of this dissertation is to layout D.F.P Localization that can be redeployable, reconfigurable, smooth to apply, &amp; operates in actual time. The embedded intrusion detection algorithm is designed so that it may cope with the limited assets, in terms of computational energy &amp; available memory area, of the microcontroller unit (MCU) determined inside the nodes &amp; various demanding situations &amp; problem confronted during the real check mattress deployment &amp; additionally proposed solutions to conquer. We come up with an opportunity algorithm primarily work on minimum Euclidean distance classifier the output suggests that localization accuracy of the method is accelerated while usage of the proposed algorithm.