Training Sequence Research Articles

Downlink Training Sequence Design Based on Waterfilling Solution for Low-Latency FDD Massive MIMO Communications Systems

Future generations of wireless communications systems are expected to evolve toward allowing massive ubiquitous connectivity and achieving ultra-reliable and low-latency communications (URLLC) with extremely high data rates. Massive multiple-input multiple-output (m-MIMO) is a crucial transmission technique to fulfill the demands of high data rates in the upcoming wireless systems. However, obtaining a downlink (DL) training sequence (TS) that is feasible for fast channel estimation, i.e., meeting the low-latency communications required by future generations of wireless systems, in m-MIMO with frequency-division-duplex (FDD) when users have different channel correlations is very challenging. Therefore, a low-complexity solution for designing the DL training sequences to maximize the achievable sum rate of FDD systems with limited channel coherence time (CCT) is proposed using a waterfilling power allocation method. This achievable sum rate maximization is achieved using sequences produced from a summation of the user’s covariance matrices and then applying a waterfilling power allocation method to the obtained low-complexity training sequence. The results show that the proposed TS outperforms the existing methods in the medium and high SNR regimes while reducing computational complexity. The obtained results signify the proposed TS’s feasibility for practical consideration compared with the existing DL training sequence designs.

Carrier FOE Scheme Based on FSTS in Spatial Diversity PM Coherent FSO Communication

The efficient and cost-effective suppression of atmospheric turbulence effects in free-space optical (FSO) communication poses a significant challenge. To address this challenge, a carrier frequency offset estimation (FOE) scheme based on frame-synchronous training sequence (FSTS) is proposed, which can be applied to spatial diversity polarization multiplexing (PM) coherent FSO communication system. The proposed scheme integrates the functions of frame synchronization (FS) and FOE by designing distinct training sequences for the X and Y polarizations. Specifically, the FOE scheme achieves high accuracy, wide range, low complexity, and multi-format versatility while maintaining FS accuracy. Simulation results for PM 4/16-quadrature amplitude modulation (QAM) demonstrate the feasibility and generality of the proposed scheme. Moreover, when combined with the spatial diversity reception under strong and weak turbulence conditions, the proposed algorithm exhibits higher receiver sensitivity compared to the conventional training sequence (TS)-based FOE algorithm. The advantages of this scheme make it highly promising for high-speed coherent FSO communications across multiple modulation formats.

Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations

Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices’ activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. Moreover, a distributed federated learning system is used to solve the problem of insufficient number of local training samples for a multi-party joint training model without exchanging the original data of the samples. The federated learning center receives the network parameters uploaded by all local models for aggregation, and feeds the aggregated parameters back to each local model for a global update. The proposed blind identification method requires less information and no training sequences and pilots. Thus, it achieves energy-efficiency and spectrum-efficiency. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radios

Neuro-control adjustment of conveyor belt tension

Goal.Identification of ways to improve the quality indicators of conveyor operation during their design and construction using modern automation elements, automatic control systems, and the application of neural controller systems for automatic support of optimal technological processes during material transportation. Research methodology. The proposed methodology involves supervisory training of a neural controller system for automatic tensioning of the conveyor belt using the MATLAB software package. Research results. A sequence for building and training an imitation neural control system for regulating (supporting) the optimal tension of the conveyor belt has been developed and investigated using the MATLAB software package. It was established that for systems containing elements with significant time constants, it is advisable to experimentally determine the ratio of the number of training stages to the number of segments during supervisory training of the neural controller. Scientific novelty. The article proposes the construction of an imitation model of a tension control system for a conveyor belt using artificial intelligence and the implementation of a neural controller. This approach allows for preliminary tuning of the developed control system and its utilization in the design phase of similar objects. Practical significance. The sequence for constructing an imitation model of an optimal tension control neural system for a conveyor belt can be applied in the design and investigation of similar mechanisms. The system is adaptable and enhances reliability while maintaining desired performance parameters. The model can be recommended for preliminary determination of neural controller parameters, regulator system tuning, obtaining predicted transient processes, and improving work productivity.

Reforming clinical psychological science training: The importance of collaborative decision-making with trainees.

To effectively address the staggering burden of mental illness, clinical psychological science will need to face some uncomfortable truths about current training practices. In a commentary authored by 23 current or recent trainees, Palitsky and colleagues highlight a number of urgent challenges facing today's clinical interns. They provide a thoughtful framework for reform, with specific recommendations and guiding questions for a broad spectrum of stakeholders. Key suggestions are applicable to the entire sequence of clinical training. While there is cause for cautious optimism, overcoming these systemic barriers will require a coordinated, all-hands approach and a more collaborative approach to policy-making.

Gait Training with Functional Electrical Stimulation Improves Mobility in People Post-Stroke

(1) Background: Stroke is one of the leading causes of disability. To identify the best treatment strategies for people with stroke (PwS), the aim of the current study was to compare the effects of training on a treadmill with functional electrical stimulation (TT-FES) with training on a treadmill (TT), and to analyze the effects of sequence of training on mobility and the parameters of walking ability. (2) Methods: Prospective, longitudinal, randomized and crossover study, in which 28 PwS were distributed into groups, namely the A-B Group (TT-FES followed by TT) and B-A Group (TT followed by TT-FES), using the foot drop stimulator, and were measured with functional tests. (3) Results: We found improved mobility, balance, non-paretic limb coordination, and endurance only in the group that started with TT-FES. However, sensorimotor function improved regardless of the order of training, and paretic limb coordination only improved in the B-A Group, but after TT-FES. These data indicate that the order of the protocols changed the results. (4) Conclusions: Although biomechanical evaluation methods were not used, which can be considered a limitation, our results showed that TT-FES was superior to isolated training on a treadmill with regard to balance, endurance capacity, and coordination of the non-paretic limb.

Assessing Expertise Using Eye Tracking in a Virtual Reality Flight Simulation

ABSTRACT Objective The aim of this work was to examine the fidelity and validity of an aviation simulation using eye tracking. Background Commercial head-mounted virtual reality (VR) systems offer a convenient and cost-effective alternative to existing aviation simulation (e.g., for refresher exercises). We performed pre-implementation testing of a novel aviation simulation, designed for head-mounted VR, to determine its fidelity and validity as a training device. Method Eighteen airline pilots, with varying levels of flight experience, completed a sequence of training ‘flows.’ Self-reported measures of presence and workload and users’ perceptions of fidelity were taken. Pilots’ eye movements and performance were recorded to determine whether more experienced pilots showed distinct performance and eye gaze profiles in the simulation, as they would in the real-world. Results Real-world expertise correlated with eye gaze patterns characterized by fewer, but longer, fixations and a scan path that was more structured and less random. Multidimensional scaling analyses also indicated differential clustering of strategies in more versus less experienced pilots. Subjective ratings of performance, however, showed little relationship with real-world expertise or eye movements. Conclusion We adopted an evidence-based approach to assessing the fidelity and validity of a VR flight training tool. Pilot reports indicated the simulation was realistic and potentially useful for training, while direct measurement of eye movements was useful for establishing construct validity and psychological fidelity of the simulation.

Parallel and Collaborative Passenger Flow Control of Urban Rail Transit Under Comprehensive Emergency Situation

The artificial cooperative control methods are used to fully utilize the train capacity and reduce the passenger delays by coordinating the number of entering and boarding passengers at each station, but it ignores the potential risk associated with the aggregation attributes of passengers in the station under emergency and the unfairness caused by the imbalance of travel opportunities for different origin–destination (OD) passengers. A new parallel and cooperative control model under comprehensive emergency situation is constructed from the perspective of guaranteeing travel safety, improving travel fairness, and increasing travel efficiency to generate passenger flow control strategies. The model aims to maximize passenger turnovers, minimize passenger delays and reduce the difference in the ratio of stranded passengers at stations. The complex multi-objective integer programming problem involving nonlinear equation constraints is difficult to solve numerically. A dynamic programming model taking train sequence as stage variable is then constructed as well as an NSGA-II algorithm with multiple improved strategies (MIS-NSGA-II) is proposed to solve the model. Numerical experiments on a line of Beijing Rail Transit show that the MIS-NSGA-II outperforms the traditional NSGA-II, and that increasing the overload coefficient of the train and the tolerance coefficient of each area in the station can further alleviate passenger congestion and promote the operation efficiency. Compared with other control strategies mentioned in this study, the proposed control model from a comprehensively perspective, which can ensure the travel safety and promote the travel efficiency while forcefully decongesting the bottleneck areas and improving the fairness of passenger travel.

A Single Session of Mindfulness Meditation Expedites Immediate Motor Memory Consolidation to Improve Wakeful Offline Learning

Posttraining meditation has been shown to promote wakeful memory stabilization of explicit motor sequence information in learners who are experienced meditators. We investigated the effect of single-session mindfulness meditation on wakeful and sleep-dependent forms of implicit motor memory consolidation in meditation naïve adults. Immediately after training with a target implicit motor sequence, participants (N = 20, eight females, 23.9 ± 3.3 years) completed either a 10-min mindfulness meditation (N = 10) or a control listening task before exposure to task interference induced by training with a novel implicit sequence. Target sequence performance was tested following 5-hr wakeful and 15-hr postsleep periods. Bayesian inference was applied to group comparisons of mean reaction time (RT) changes across training, interference, wakeful, and postsleep timepoints. Relative to control conditions, posttraining meditation reduced RT slowing between target sequence training and interference sequence introduction (BF10 [Bayes factors] = 6.61) and supported RT performance gains over the wakeful period (BF10 = 8.34). No group differences in postsleep RT performance were evident (BF10 = 0.38). These findings illustrate that posttraining mindfulness meditation expedites wakeful, but not sleep-dependent, offline learning with implicit motor sequences. Previous meditation experience is not required to obtain wakeful consolidation gains from posttraining mindfulness meditation.

Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

Joint Channel Estimation and Decoding for Underwater Acoustic Communication With a Short Pilot Sequence

With the advance of undersea communication technologies, the operation of large numbers of small submerged devices and their connection, such as the Internet of Underwater Things (IoUT), is becoming possible. Deployed over long periods of time, energy restrictions limit the application of underwater acoustic (UWA) communication, and thus, the peer-to-peer (P2P) communication packets should be small with minimum overhead dedicated to training symbols. On the other hand, the UWA communication in physical layer requires good equalization performance to manage the inevitably long intersymbol interference. In this article, we propose a semiblind joint channel estimation and decoding (JCED) approach for P2P UWA communication to allow efficient channel equalization despite the short training sequence. We formulate the JCED problem and solve it by passing messages between the raw acoustic observations, the nodes that characterize the connections of the channel's taps, and the information-bearing symbols within a turbo equalization framework. To initialize the prior of the channel's components, we employ the expectation maximization algorithm, assuming a Gaussian mixture model for the channel's taps. Extensive numerical simulation and results from a field experiment show that our approach outperforms state-of-the-art benchmarks in terms of the communication system's capability of performing well with a small number of pilot symbols.

Reevaluating trials to criterion as a measure in second language research

AbstractResearch on input processing and processing instruction has often employed a scoring method known as trials to criterion to observe the effects of instruction that emerge during training. Despite its common use in this research (see Fernández, 2021) this metric has never been evaluated critically. The present study first discusses several challenges associated with trials to criterion, including issues with its conceptual and methodological implementation. The study then introduces three alternative approaches for analyzing accuracy data collected during training sequences: trials to accuracy threshold, growth curve analysis, and bootstrapped differences of timeseries. For each approach, advantages and disadvantages are discussed and example analyses are presented using data from previous research. This discussion shows how these alternative approaches can supplement current trials-to-criterion-based analyses, expand the methodological choices available to researchers, and permit new and interesting research questions.

A novel complex-valued classifier with a weighted loss function employed for W-band PS-16QAM transmission over 4.6 km

Probabilistic shaping(PS) technique can reduce Signal-Noise Ratio (SNR) requirement and save the transmission power, which is meaningful for millimeter wave (mm-wave) long-distance wireless transmission. Traditional Digital Signal Processing(DSP) algorithms are affected by the uneven probability distribution of constellation points when working on PS signals. We propose a novel complex-valued classifier with a weighted loss function to replace traditional algorithms in PS-16QAM W-band long-distance transmission. The proposed algorithm can provide a receiving sensitivity improvement of about 0.5 dBm. It can reduce the training sequence length by 4%–7%, the computational complexity by 10.53%–16.89%, and the number of training iterations by 23.08%, respectively.

Frequency offset modeling in presence of ASE noise and corresponding low-complexity solution for discrete spectrum modulated nonlinear frequency division multiplexing system

For discrete spectrum modulated nonlinear frequency division multiplexing (DS-NFDM) transmission, the combined effect of laser frequency offset (FO) and amplifier spontaneous emission (ASE) noise would cause a perturbative shift of eigenvalues and corresponding spectral distortion. Moreover, the classical 4th-power feedforward frequency offset estimation (FOE) scheme is not suitable for DS-NFDM system with low baud rate. In this paper, we firstly deduce an FO impairment model with ASE noise in nonlinear Fourier domain (NFD). Meanwhile, we have proposed a training sequence-assisted FOE (TS-FOE) scheme, which effectively reduces the impact of ASE noise. The 2 GBaud DS-NFDM 16QAM simulation results illustrate that this scheme achieves an FOE error within 1 MHz using a training sequence (TS) with length of 64. Besides, its FOE range achieves [−Rs/2, +Rs/2], where Rs denotes the baud rate of signal. Furthermore, the effectiveness of TS-FOE scheme has been verified by 2 GBaud NFDM QPSK experimental system. The results demonstrate that when optical signal-to-noise ratio (OSNR) is set 13 dB, the TS-FOE scheme presents stable FO compensation performance for different FOs within 1 GHz, and the corresponding bit error rates (BERs) are all below the 7% hard decision forward error correction (HD-FEC) threshold. Meanwhile, its complexity is on the order of OL and related with the length L of TS, which is as one thousandth as that of angle search FOE scheme.

Joint Compensation of TX/RX IQ Imbalance and Channel Parameters for OTSM Systems

Orthogonal time sequency multiplexing (OTSM), robust to doubly-selective fading channel, performs similar to orthogonal time frequency space (OTFS) system with less modulation/demodulation complexity. Direct conversion OTSM system suffers from hardware impairments such as in-phase and quadrature phase (IQ) imbalance at the transmitter (TX) and receiver (RX). At high frequencies, hardware imperfections are inevitable and should be compensated for better performance. In this Letter, we propose joint estimation of TX and RX IQ imbalance and estimate channel parameters in the delay time domain using pseudo noise based training sequence approach. The mean square error of the estimated channel parameters reduce with increase in training sequence length and power. With matched filtered Gauss-Seidel iterative method of detection of low complexity, it is shown that the compensated system performs close to ideal system under fractional delay and fractional Doppler.

Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks

Non-terrestrial network (NTN) services using low-Earth-orbit (LEO) satellites are expanding. Interference management of NTN services with other terrestrial wireless services is emerging as a critical issue owing to the inherent international and vast coverage nature of NTN. This study develops a multi-agent deep reinforcement learning (DRL) framework to establish a multi-beam uplink channel allocation strategy that minimizes interference with incumbent stations under the given quality of service (QoS) constraints. We propose a novel framework with the sequential training of agents in a specific order to overcome the inherent non-stationarity of multi-agent DRL. To improve learning efficiency, we design the training sequence in accordance with reward function and initial state. As a result, taking actions in the order of the largest interference to the incumbent station provides superior performance to taking actions in an arbitrary order. Moreover, the proposed channel allocation performs close to the optimal exhaustive search and outperforms conventional greedy graph coloring method.

Symmetrical Z-Complementary code sets for optimal training in generalized spatial modulation

This paper considers the optimal training design for broadband generalized spatial modulation systems over frequency-selective channels using a novel class of code sets introduced, called “symmetrical Z-complementary code sets”, whose aperiodic auto- and cross- correlation sums exhibit zero-correlation zones at both the front-end and tail-end of the entire correlation window. Two constructions of (optimal) symmetrical Z-complementary code sets based on generalized Boolean functions are presented. Numerical evaluations indicate that the proposed training sequences for generalized spatial modulation can achieve optimal channel estimation performance and outperform other classes of sequences.

Click train patterns of free-ranging harbour porpoises acquired using T-PODs may be useful as indicators of their behaviour

Harbour porpoise signals consist of directional, high frequency stereotypic clicks which can be logged using T-PODs. Variation in interclick intervals (ICIs) can be used to distinguish different acoustic behaviours. So far, studies on ICI variation are mostly descriptive and the behavioural context in which certain click train patterns are emitted is poorly understood. In this study, the behaviour of free-ranging porpoises was quantified by using typical ICI patterns known from the literature. These were recorded using two T-PODs deployed at a wind farm site (Nysted, Denmark) between 14 June and 12 July 2005 and during the entanglement of a porpoise calf in a gillnet (Clayoquot Sound Canada). It was possible to distinguish between feeding, approach behaviour and communication and known ICI patterns associated with these behaviours were used to categorise acoustic data. During feeding typical click trains start with long ICIs (30-70ms) and end with ICIs down to about 2ms. In a transition phase ICIs rapidly decrease. Click trains attributed to feeding were found in the wind farm data at a rate of 6.3d–1 (n=174) with a patchy distribution. We found 20 to 74s long click train sequences with ICIs gradually decreasing from a median of 72ms (range 34 to 143ms) down to 5ms at a rate of 1.6day–1 (n=45). This was interpreted as approach behaviour, in which the animal was acoustically ‘locked on’ to a reflective structure. Communication signals are built up of click trains with very short ICIs (<7.7ms). During the entanglement of a porpoise calf, three different call types were determined at a rate of 8.9min–1 (n=89). One call with variable duration (100 to 890ms) and relatively stable ICIs as low as 3.6ms resembled ‘distress calls’ described by Amundin (1991b). Another call type with durations from 780 to 830ms and ICIs ranging from 3.0 to 10 ms and thus different with respect to ICI curve progression was found only three times. These had a U-shaped ICI curve, similar to an ‘alarm’ or ‘fright’ call described by Busnel and Dziedzic (1966). A third and previously unreported call is characterised by a long call duration (up to 1,270ms) and sometimes oscillating ICIs with an initial decrease from about 9ms to around 7ms and an increase towards the end. The data presented suggest that the T-POD is a promising tool for behavioural studies. It is possible to recognise certain acoustic behavioural categories described in the literature, but it is important to look at the temporal context with other vocalisations in T-POD data, such as ICIs of preceding click trains.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.