Code Domain Research Articles

Voice Activity Detection Optimized by Adaptive Attention Span Transformer

Voice Activity Detection (VAD) is a widely used technique for separating vocal regions from audio signals, with applications in voice language coding, noise reduction, and other domains. While various strategies have been proposed to improve VAD performance, such as ACAM, DCU-10, and Tr-VAD, these approaches often suffer from common limitations, including being unsuitable for long audio and being time-consuming. To address these issues, we propose a new method called AAT-VAD, which integrates an adaptive width attention learning mechanism into the classic transformer framework. Our approach involves extracting Mel-scale Frequency Cepstral Coefficients (MFCC) from the Mel scale frequency domain, adding a masking function to each transformer attention head, and inputting the features processed by the transformer encoder layer into the classifier. Experimental results indicate that our method achieves a 12.8% higher F1-score than DCU-10 and a 0.6% higher F1-score than Tr-VAD under different noise interferences. Furthermore, the average detection cost function (DCF) value of our method is only 14.3% of DCU-10 and 92.4% of Tr-VAD, and the test time of AAT-VAD is only 37.4% of that of Tr-VAD for the same noisy vocal mixed audio.

Reconstruction Layout Optimization of Multivariety and Small Batch Workshop in Aerospace Industry.

To cope with the problems of frequent mold changes, long production cycles and serious logistics crossings in workshop of aerospace enterprise. First, a manufacturing cell layout planning method based on the feature bit code domain method and K-Means++ is proposed to realize the accurate division of manufacturing cells. Then, a multiobjective optimization method of dynamic reconstruction layout based on improved fruit fly optimization algorithm (IFOA) is proposed to solve the reconstruction layout optimization of the production workshop problem with the optimization objectives of logistics cost, reconstruction cost, loss cost, and cell integrated area. Finally, plant simulation software is applied to simulate the workshop layout before and after optimization. The simulation results show that the logistics cost of the workshop cell layout after optimization is reduced by 8.7%, the utilization rate of the workshop area is improved by 5.2%, and the value-added rate of products is increased by 6.6%, which verifies the effectiveness and feasibility of the proposed model and method.

A comparative study of adversarial training methods for neural models of source code

Adversarial training has been employed by researchers to protect AI models of source code. However, it is still unknown how adversarial training methods in this field compare to each other in effectiveness and robustness. This study surveys and investigates existing adversarial training methods, and conducts experiments to evaluate these neural models’ performance in the domain of source code. First, we examine the process of adversarial training to identify four dimensions that could be used to classify different adversarial training methods into five categories, which are Mixing Directly, Composite Loss, Adversarial Fine-tuning, Min–max + Composite Loss, and Min–max. Second, we conduct empirical evaluations of these classified adversarial training methods under two tasks (i.e., code summarization and code authorship attribution) to determine their performance of effectiveness and robustness. Experimental results indicate that the performance of certain combinations of adversarial training techniques (i.e., min–max with composite loss, or directly-sample with ordinary loss) would be much better than other combinations or other techniques used alone. Our experiments also reveal that the model’s robustness of defensive methods can be enhanced by using diverse input data for adversarial training, and that the number of fine-tuning epochs has little or no impact on model’s performance.

User Selection and Pairing for Future Power Domain Non-Orthogonal Multiple Access (PD-NOMA) using Deep Learning Techniques

The next-generation wireless networks and communications such as 5G/6G offers various benefits such as low latency, high data rates, and improvement in user numbers with increased base station capacity and quality of service. These advantages are obtained from the increasing receiver complexity through the non-orthogonal multiple access (NOMA) of users. It is the promising radio access approach used to enhance next-generation wireless communications. Among the techniques of NOMA such as power and code domain, this paper concentrates on power domain NOMA. The user in the network for transmission is selected using a deep learning approach called deep neural network (DNN). This user selection results are the training data and the loss function is modified for the selection of users that could meet the constraint the selected user cannot be in both strong and weak groups. The user aggregation/user pairing among the sub-channels is performed through the exhaustive analysis using particle swarm optimization (PSO). The usage of DNN-PSO enables the transmitter and required minimum uplink and downlink transmitting power and guaranteed for Quality of Service of each user. The simulation and comprehensive statistical evaluation are performed with the comparative analysis of energy efficiency and maximum achievable rate with the given spectrum efficiency (SE) of PD-NOMA. The proposed model ensures reduced latency, increased throughput, less energy, achievable data rate, user fairness and increased reliability and quality of service.

Synaptic Activity and Hardware Footprint of Spiking Neural Networks in Digital Neuromorphic Systems

Spiking neural networks are expected to bring high resources, power, and energy efficiency to machine learning hardware implementations. In this regard, they could facilitate the integration of Artificial Intelligence in highly constrained embedded systems, such as image classification in drones or satellites. If their logic resource efficiency is widely accepted in the literature, their energy efficiency still remains debated. In this article, a novel high-level metric is used to characterize the expected energy efficiency gain when using Spiking Neural Networks (SNN) instead of Formal Neural Networks (FNN) for hardware implementation: Synaptic Activity Ratio (SAR). This metric is applied to a selection of classification tasks including images and 1D signals. Moreover, a high-level estimator for logic resources, power usage, execution time, and energy is introduced for neural network hardware implementations on FPGA, based on four existing accelerator architectures covering both sequential and parallel implementation paradigms for both spiking and formal coding domains. This estimator is used to evaluate the reliability of the Synaptic Activity Ratio metric to characterize spiking neural network energy efficiency gain on the proposed dataset benchmark. This study led to the conclusion that spiking domain offers significant power and energy savings in sequential implementations. This study also shows that synaptic activity is a critical factor that must be taken into account when addressing low-energy systems.

Exploiting hybrid non‐orthogonal multiple access in satellite–terrestrial systems with randomly deployed users

In this letter, a hybrid non-orthogonal multiple access (NOMA) scheme for satellite–terrestrial systems is proposed, which is capable of integrating both power domain NOMA (PD-NOMA) and code domain NOMA (CD-NOMA) design modes. The stochastic geometry method is considered to model the position of the terrestrial NOMA user. Specifically, the closed-form expressions of exact outage probability with imperfect interference cancellation are derived. In order to get more insights, the asymptotic outage probabilities for high SNR conditions are also discussed. Numerical results are provided to confirm the validity of theoretical analysis and reveal the effects of critical parameters on system performance.

The effect of quantized ETF, grouping, and power allocation on non-orthogonal multiple accesses for wireless communication networks

Nonorthogonal multiple access (NOMA) is a significant technology in radio resource sharing and it has been recognized as a favorable method in fifth-generation (5G) wireless networks to meet the requirements of system capacity, service latency, and user connectivity. Many schemes for NOMA have been proposed in the last few years. such as transmitter linear spreading-based NOMA as a code domain, as well as a linear minimum mean square error (LMMSE), parallel interference cancellation (PIC), and serial interference cancellation (SIC) with power allocation and grouping as a power domain at the receiver side for uplink NOMA. This work aims to evaluate the performance of multiple types of linear spreading-based NOMA schemes. Simulations are achieved for the error-rate performance evaluation of these NOMA schemes, received signal after detection, and received signal and effect of every user on the other. Evaluating the performance of these technologies with comparison is also achieved through using grouping and power allocation. Simulations are achieved for the sum rate and spectral efficiency. For the future, 5G NOMA development, an equiangular tight frame (ETF) is suggested for improving performance and suggests grouping with 64qam-quantized Grassmannian for improving performance favorite about grouping with Generalized welch-bound equality (GWBE)

CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC.

With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.

MMW-NOMA: An Uplink NOMA Communication System Based on the Node Pairing Algorithm of Maximum and Minimum Weight in Underwater Acoustic Networks

In orthogonal multiple access (OMA) communication systems, resources are allocated to numerous users based on time, frequency, or code domains. The low bandwidth of the underwater acoustic network limits the number of nodes that can be supported by the OMA system, due to limited resources. The innovative concept of nonorthogonal multiple access (NOMA) provides a solution that offers more nodes and increases spectral efficiency. As a promising technique, it optimizes power allocation through channel characteristics and adopts serial interference cancellation algorithms to decode the signal at the receiver. This paper introduces an underwater communication system based on the node pairing algorithm of maximum and minimum weight (MMW-NOMA). First, we build the system model in a UAN scenario. Second, we present the node pairing strategy. Third, we design a node replacement mechanism. Furthermore, we offer a power allocation algorithm. Finally, we compare the performance of MMW-NOMA with randomly paired NOMA (RP-NOMA) and orthogonal frequency division multiplexing (OFDM). Numerical results show that MMW-NOMA outperforms RP-NOMA and OFDM in throughput, mean square error, and energy efficiency. However, the complexity of MMW-NOMA is slightly higher than that of RP-NOMA but significantly lower than OFDM. These results show that MMW-NOMA achieves a reasonable trade-off between performance and complexity.

On the Performance of the Unified NOMA Framework for Satellite-Terrestrial Networks with Hardware Impairments

A unified nonorthogonal multiple access (NOMA) framework for satellite-terrestrial network is investigated over Shadowed-Rician fading channels, which can be applied to both power domain NOMA (PD-NOMA) and code domain NOMA (CD-NOMA). Under the realistic assumption of hardware impairments (HIs) and imperfect successive interference cancellation (SIC), the achievable outage probability of the paired users is analyzed first, and the closed-form expressions of exact and asymptotic outage probability are derived. Next, the ergodic sum rate is investigated in different levels of HIs and residual interference. In addition, the energy efficiency in the delay-tolerant transmission mode is studied to characterize the performance of the satellite-terrestrial network. Finally, the Monte Carlo simulation results demonstrate the accuracy of theoretical derivation and showcase the effects of a wide range of satellite channel parameters and the realistic assumption on outage probability, ergodic rate, and energy efficiency.

GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain

The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computational complexity and low acquisition accuracy under high dynamic conditions. In this paper, a GNSS signal acquisition algorithm based on two-stage compression of the code-frequency domain is proposed. The algorithm maps the incoming signal of the same interval to multiple carrier frequency bins and overlaps the mapped signal that belongs to the same code phase. Meanwhile, the code domain compression is introduced to the preprocessed signal, replacing circular correlation with compressed reconstruction to obtain Doppler frequency and code phase. Theoretical analyses and simulation results show that the proposed algorithm can improve the frequency search accuracy and reduce the computational complexity by about 50% in high dynamics.

Multiplexing Capacity of Hybrid PD-SCMA Heterogeneous Networks

Hybrid multiple access schemes are considered potential technologies towards achieving optimal spectrum sharing for future heterogeneous networks. Multiple users are multiplexed on a single resource unit in the code domain for sparse code multiple access (SCMA) or power domain for power domain non-orthogonal multiple access (PD-NOMA) and in both domains for the hybrid power domain sparse code non-orthogonal multiple access (PD-SCMA). This allows for effective spectrum usage but comes at a cost of increased detection complexity resulting in loss of performance in terms of outages. It is therefore imperative to determine the user multiplexing capacity for effective performance. This work investigates codebook capacity bounds in small cells, pairing and power capacity bounds for the number of small cell user equipment’s (SUEs) and macro cell user equipment’s (MUEs) that can be multiplexed on a codebook for the developed PD-SCMA technology. Closed-form solutions for codebook, pairing and power multiplexing capacity bounds are derived. The performance of the system results into low outage when the system’s point of operation is within the multiplexing bounds. To alleviate the resource allocation (RA) challenges of such a system at the transmitter, dual parameter ranking (DPR) and alternate search method (ASM) based RA schemes are proposed. The results show significant capacity gain with DPR-RA in comparison with conventional schemes.

Multidomain Narrowband Interference Intelligent Suppression Method Based on Cognitive Radio and MIMO in UAV Data Link.

In this paper, a kind of antinarrowband interference intelligent method based on cognitive MIMO (multiple-input multiple-output) airspace, time domain, frequency domain, and code domain is proposed. This method combines perceptual technology, spread spectrum code, MIMO space-time coding, and so on. Through coordinating space, time, frequency, and code element diversity, the narrowband interference in the UAV (unmanned aerial vehicle) monitoring and control link is effectively countered. The input and output model of MIMO monitoring and control link of UAV with natural interference are described. Then, the realization principle of the multidomain antinarrowband interference method based on CR-MIMO is presented, and the corresponding models of receiving and transmitting are also given. Finally, the anti-interference performance of the proposed method is analyzed theoretically and validated through simulation experiments, and the effectiveness of the proposed method is verified.

Low-Order Aerodynamic Model for Interference in Multirotor Systems

The paper proposes an aerodynamic model capable of capturing interference effects in arbitrary multirotor systems. The current analysis is limited for nonoverlapping rotors. The model is based on a combined inflow theory approach, where self-induced and influence-induced velocities are obtained using different inflow models. The generalized dynamic wake model was used to obtain the self-induced velocity distribution, whereas the influence-induced velocity field was computed using Beddoes’s generalized wake model. The results of the combined inflow model were compared against free-vortex wake simulations of a twin-rotor system in edgewise flight. Furthermore, the study examined the effects of tip-to-tip separation distances and freestream orientation angles on overall system performance in terms of thrust and power ratios. The comparison indicated that the results of the proposed model agreed within around 5% with the free-vortex wake analysis over all operating conditions. The computational time of the combined inflow model was two to three orders of magnitude lower than the free-vortex wake approach, depending on the code architecture and domain discretization. The analysis shows that the proposed approach is adequate to model primary aerodynamic interference effects in multirotor systems operating at advance ratios greater than or equal to 0.15.

Effects of variability in models: a family of experiments

The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.

Error Rate Analysis of Intelligent Reflecting Surfaces Aided Non-Orthogonal Multiple Access System

A good wireless device in a system needs high spectral efficiency. Non-Orthogonal Multiple Access (NOMA) is a technique used to enhance spectral efficiency, thereby allowing users to share information at the same time and same frequency. The information of the user is super-positioned either in the power or code domain. However, interference cancellation in NOMA aided system is challenging as it determines the reliability of the system in terms of Bit Error Rate (BER). BER is an essential performance parameter for any wireless network. Intelligent Reflecting Surfaces (IRS) enhances the BER of the users by controlling the electromagnetic wave propagation of a given channel. IRS is able to boost the Signal to Noise Ratio (SNR) at the receiver by introducing a phase shift in the incoming signal utilizing cost-effective reflecting materials. This paper evaluates users’ error rate performance by utilizing IRS in NOMA. The error probability expression of users is derived under Rayleigh and Rician fading channel. The accuracy of derived analytical expressions is then validated via simulations. Impact of power allocation factor, coherent and random phase shifting of IRS is evaluated for the proposed IRS-NOMA system.

Tensor Dictionary Manifold Learning for Channel Estimation and Interference Elimination of Multi-User Millimeter-Wave Massive MIMO Systems

Millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures can greatly increase system capacity and communicate with multiple users at the same time. Accurate channel estimation is crucial for multi-user communications, but its accuracy is limited as the number of antennas and users increases. In the matrix high-dimensional operation for multi-user channel estimation, not only is it computationally intensive, but also the estimation accuracy is low. It makes our work turn to channel estimation of the user group within a certain region to improve the accuracy of estimation. In this paper, we propose a tensor dictionary manifold learning method for channel estimation and interference elimination of the multi-user mmWave massive MIMO system. A multi-user digital-analog mixed received signal model is presented. The tensor dictionary manifold learning scheme is proposed to model the received signal as a third-order low-rank tensor to handle the high-dimensional user, antenna, and channel. After segmentation, clustering and manifold learning, multiple tensor dictionary manifold models containing a group of user signals are fitted. Tensor dictionary manifold learning can take advantage of the inherent multi-domain properties of signals in the frequency, time, code and spatial domains to maintain inter-user correlation within a user group while reducing the high-dimensional channels of the user group. Using the convex relaxation property of the tensor alternating direction method, we propose a strategy to eliminate interference from other groups. And with the help of the multi-signal classification method, the channel parameters of user groups are obtained to improve the accuracy of multi-user channel estimation. This method can perform channel estimation for multiple users with only a few pilots, and improve the performance of the system. Numerical results confirm the good performance of this method.

Multi-Carrier Waveforms and Multiple Access Strategies in Wireless Networks: Performance, Applications, and Challenges

Current generation mobile communications require high-quality services. Adopting multiple access (MA) and multi-carrier waveforms potentially enhances the quality of services offered to end-users. However, in the majority of literature, the integration of multi-carrier and multiple-access approaches have not been extensively examined. One possible solution is to review multiple access and multi-carrier waveforms simultaneously to create a favorable foundation for the integration of these schemes. Thus, we consider a comprehensive review of multiple-access systems and multi-carrier waveforms jointly from <inline-formula> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula>-generation (1G-5G) cellular networks. Initially, we present orthogonal MA (OMA) schemes called: frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA) that have been utilized in 1G, 2G, 3G, and 4G, respectively. In addition, 5G wireless non-orthogonal multiple access (NOMA) techniques such as power domain NOMA (PD-NOMA), code domain NOMA (CD-NOMA), and other NOMA multiplexing methods are addressed in detail. On the other hand, we glanced at 5G cellular multi-carrier waveforms such as filter bank multi-carrier (FBMC), universal filtered multi-carrier (UFMC), generalized frequency division multiplexing (GFDM), and filtered orthogonal frequency division multiplexing (f-OFDM) waveforms. The assessment and comparison between different OMA, NOMA, and multi-carrier waveforms are carried out with the parameters: modulation schemes, bit error rate (BER), signal to noise ration (SNR), sum rate, peak-to-average power ratio (PAPR), latency, out of band emission (OOBE), and complexity. The analytical formulas of OMA, NOMA, and multi-carrier waveform schemes are also derived and verified using simulation data. Each multiple access strategy&#x2019;s merits, shortcomings, applications, and factors influencing its performance are also addressed. Eventually, possible recommendations for the integration of multiple-access and modulation technologies for next-generation mobile networks are also included.

Технология NOMA с кодовым разделением в 3GPP: 5G или 6G?

The article is devoted to non-orthogonal multiple access (NOMA) schemes with code division within 3GPP standardization. Various companies have proposed a great variety of NOMA schemes for 5G and 6G systems. The scope and main advantages of NOMA technology are briefly illustrated. The most popular varieties of code domain NOMA and their features are considered, their structure, prospects and implementation problems are analyzed.

Treatment-resistant schizophrenia: Addressing white matter integrity, intracortical glutamate levels, clinical and cognitive profiles between early- and adult-onset patients

BackgroundTreatment-resistance in schizophrenia is 30–40%. Its neurobiology remains unclear; to explore it, we conducted a combined spectrometry/tractography/cognitive battery and psychopathological rating study on patients with treatment-resistant schizophrenia (TRS), dividing the sample into early-onset (N = 21) and adult-onset TRS (N = 20). Previous studies did not differentiate between early- (onset 13–18 years) and adult-onset (>18 years at formal diagnosis of schizophrenia) TRS. MethodsWe evaluated cross-sectionally 41 TRS patients (26 male and 15 female) and 20 matched healthy controls (HCs) with psychopathological and cognitive testing prior to participating in brain imaging scanning using magnetic resonance spectroscopy and diffusion tensor imaging to determine the relationship between their symptoms and their glutamate levels and white matter integrity. ResultsTRS patients scored lower than HCs on all cognitive domains; early-onset patients performed better than adult-onset patients only on the Symbol Coding domain. TRS correlated with symptom severity, especially negative symptoms. Glutamate levels and glutamate/creatine were increased in anterior cingulate cortex. Diffusion tensor imaging showed low fractional anisotropy in TRS patients in specific white matter tracts compared to HCs (bilateral anterior thalamic radiation, cortico-spinal tract, forceps minor, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, and right uncinate fasciculus). ConclusionsWe identified specific magnetic resonance spectroscopy and diffusion tensor imaging alterations in TRS patients. Adult-onset TRS differed little from early-onset TRS on most measures; this points to alterations being present since the outset of schizophrenia and may constitute a biological signature of treatment-resistance.