Optimal Training Sequences Research Articles

Effect of 3D printed teeth and virtual simulation system on the pre-clinical access cavity preparation training of senior dental undergraduates

BackgroundThe objective of the present study was to evaluate the effect of 3D printed teeth and virtual simulation system on the pre-clinical access cavity preparation training of senior dental undergraduates.MethodsThe 3D printed teeth were manufactured based on the micro-CT data of an extracted lower first molar. Ninety-eight senior dental undergraduate students were required to finish the access cavity preparation of lower first molar within 20 min on plastic and 3D printed teeth on the manikin system as well as on a virtual simulation machine respectively with randomly selected sequences. Expert dentists scored the operated teeth. The scores from the virtual simulation system were also recorded. All the scores were analyzed and compared. Following the procedure, two questionnaires were sent to students to further evaluate the feelings and optimal training sequence.ResultsNo significant differences were found between plastic and 3D printed teeth scores, while virtual simulation achieved a valid/invalid area removal ratio of 96.86% ± 5.08% and 3.97% ± 1.85%, respectively. Most students found plastic teeth training the easiest and favored the three-training combination (36.36%). 71.42% of the students thought the virtual simulation training should be put at the first place of the three trainings. Over 80% of students agreed with incorporating 3D printed teeth and virtual simulation into their routine training courses. In addition, the general advantages and disadvantages of the virtual simulation system and 3D printed teeth training received almost equal recognition by students.ConclusionsVirtual simulation system training + plastic teeth training + 3D printed teeth training might be the optimal training sequence. Virtual simulation system training could not completely replace the traditional training methods on the manikin system at the moment. With further modifications, 3D printed teeth could be expected to replace the plastic teeth for the pre-clinical access cavity preparation training.

Unsupervised Training Sequence Design: Efficient and Generalizable Agent Training

To train generalizable Reinforcement Learning (RL) agents, researchers recently proposed the Unsupervised Environment Design (UED) framework, in which a teacher agent creates a very large number of training environments and a student agent trains on the experiences in these environments to be robust against unseen testing scenarios. For example, to train a student to master the “stepping over stumps” task, the teacher will create numerous training environments with varying stump heights and shapes. In this paper, we argue that UED neglects training efficiency and its need for very large number of environments (henceforth referred to as infinite horizon training) makes it less suitable to training robots and non-expert humans. In real-world applications where either creating new training scenarios is expensive or training efficiency is of critical importance, we want to maximize both the learning efficiency and learning outcome of the student. To achieve efficient finite horizon training, we propose a novel Markov Decision Process (MDP) formulation for the teacher agent, referred to as Unsupervised Training Sequence Design (UTSD). Specifically, we encode salient information from the student policy (e.g., behaviors and learning progress) into the teacher's state space, enabling the teacher to closely track the student's learning progress and consequently discover the optimal training sequences with finite lengths. Additionally, we explore the teacher's efficient adaptation to unseen students at test time by employing the context-based meta-learning approach, which leverages the teacher's past experiences with various students. Finally, we empirically demonstrate our teacher's capability to design efficient and effective training sequences for students with varying capabilities.

An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System

ABSTRACT Recently, the massive Multiple-Input Multiple-Output (MIMO) system has been integrated with machine learning approaches to realise automatic channel state information detection. These methods need high computational complexity due to the lack of optimal training sequences and the failure to control the sparsity assembly based on massive MIMO. Moreover, it does not consider optimised training sequences based on a compressive sensing approach in the presence of contamination. Thus, this paper proposed an enhanced deep learning model with an optimised pilot training sequence for sparse channel estimation in a 5 G massive MIMO system. Initially, the system and sparse block channel impulse response model will be constructed by considering the time-domain synchronous Generalized Frequency Division Multiplexing (GFDM) system and additive white Gaussian noise (AWGN). In the proposed work, a seagull optimisation algorithm is developed to design the training pilot sequence depending on the coherence properties of the sensing matrix, which is used to recover the channel impulse response. Then, the sparse massive MIMO channel is evaluated by proposing a new deep channel estimator with an enhanced stacked auto encoder (D-ESAE). The proposed channel MSE is 10−6, the bit error rate is 0.04, and NMSE is 10−9 at 40 dB SNR through Matlab simulation.

Corrections to “Intelligent Reflecting Surface: Joint Optimal Training Sequence and Refection Pattern”

Corrections to “Intelligent Reflecting Surface: Joint Optimal Training Sequence and Refection Pattern”

Spatially correlated channel estimation for RIS‐assisted MIMO systems with correlated gaussian perturbation

AbstractThis paper studies the problem of spatially correlated channel estimation for reconfigurable intelligent surface (RIS)‐assisted multiple‐input multiple‐output (MIMO) systems with arbitrarily correlated Gaussian perturbation. In particular, composite RIS channel estimation is first explored based on minimum mean square error (MMSE) method, and optimal training sequence design of pilots with MSE minimization. Applying the Kronecker‐structured model, correlated composite RIS‐related channels and correlated Gaussian perturbation are then characterized. The optimal training sequence structure and spatial training power allocation conditions are derived, and the choice of the optimal training sequence length is also analyzed. Finally, numerical results are provided to assess the performance of the proposed estimate method under different training sequence structures, the optimal length of the training sequence, and information statistics. Extensive numerical results show that the proposed estimate method has supreme performance compared to state of‐the‐art baseline methods (e.g. maximum likelihood and two‐sided linear channel estimation methods).

Toward MR-only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool.

To generate synthetic relative proton stopping power (sRPSP) images from magnetic resonance imaging (MRI) sequence(s) and develop an online quality assurance (QA) tool for sRPSP to facilitate safe integration of magnetic resonance (MR)-only proton planning into clinical practice. Planning computed tomography (CT) and MR images of 195 pediatric brain tumor patients were utilized (training: 150, testing: 45). Seventeen consistent-cycle generative adversarial network (ccGAN) models were trained separately using paired CT-converted RPSP and MRI datasets to transform a subject's MRI into sRPSP. T1-weighted (T1W), T2-weighted (T2W), and FLAIR MRI were permutated to form 17 combinations, with or without preprocessing, for determining the optimal training sequence(s). For evaluation, sRPSP images were converted to synthetic CT (sCT) and compared to the real CT in terms of mean absolute error (MAE) in Hounsfield units (HU). For QA, sCT was deformed and compared to a reference template built from training dataset to produce a flag map, highlighting pixels that deviate by >100HU and fall outside the mean±standard deviation reference intensity. The gamma intensity analysis (10%/3mm) of the deformed sCT against the QA template on the intensity difference was investigated as a surrogate of sCT accuracy. The sRPSP images generated from a single T1W or T2W sequence outperformed that generated from multi-MRI sequences in terms of MAE (all p<0.05). Preprocessing with N4 bias and histogram matching reduced MAE of T2W MRI-based sCT (54±21HU vs. 42±13HU, p=0.002). The gamma intensity analysis of sCT against the QA template was highly correlated with the MAE of sCT against the real CT in the testing cohort (r=-0.89 for T1W sCT; r=-0.93 for T2W sCT). Accurate sRPSP images can be generated from T1W/T2W MRI for proton planning. A QA tool highlights regions of inaccuracy, flagging problematic cases unsuitable for clinical use.

Joint Channel Estimation and Synchronization Techniques for Time-Interleaved Block-Windowed Burst OFDM

From a conceptual perspective, beyond-5G technologies promise to deliver very low latency, even higher data rates, and ultrareliable connections for future generations of communication systems. Modulation schemes based on orthogonal frequency-domain multiplexing (OFDM) can accommodate these requirements for wireless systems. Several hybrid OFDM-based systems, such as the time-interleaved block-windowed burst–OFDM (TIBWB–OFDM), are capable of achieving even better spectral confinement and power efficiency. This paper addresses the implementation of the TIBWB–OFDM system in more realistic and practical wireless link scenarios by addressing the challenges of proper and reliable channel estimation and frame synchronization. We propose to incorporate a preamble formed by optimal correlation training sequences such as the Zadoff–Chu (ZC) sequences. The added ZC preamble sequence is used to jointly estimate the frame beginning through signal-correlation strategies and a threshold decision device, and acquire channel-state information (CSI) by employing estimators on the basis of the preamble sequence and transmitted data. The employed receiver estimators show that it is possible to detect the TIBWB–OFDM frame beginning and highlight the robustness of the TIBWB–OFDM technique to imperfect channel estimations by showing that it can provide comparatively close BER performance to the one where the CSI is perfectly known.

Optimal Training Sequences to Develop Lower Body Force, Velocity, Power, and Jump Height: A Systematic Review with Meta-Analysis.

Resistance training has been used to enhance a range of athletic abilities through correct manipulation of several variables such as training load, training volume, set configuration, and rest period. The aim of this systematic review and meta-analysis was to compare the acute and chronic responses of lower body cluster, contrast, complex, and traditional training across a range of athletic performance outcomes (1-repetition maximum squat strength, jump height, peak power, peak force, peak velocity, and sprint time). A database search was completed (SPORTDiscus, Medline and CINAHL) followed by a quality scoring system, which concluded with 41 studies being used in the meta-analysis. Effect sizes were calculated for acute and training intervention changes compared to baseline. For acute cluster training, effect sizes were used to represent differences between equated traditional and cluster sets. Acutely, contrast and cluster training can be implemented to enhance and maintain velocity. Complex training does not acutely show a performance-enhancing effect on jump performance. When looking to develop exercise-specific force, the exercise should be completed closer to set failure with fewer repetitions still able to be completed, which can be achieved using complex or high-volume contrast training to pre-fatigue the lighter exercise. When the objective is to improve velocity for the target exercise, it can be combined with a heavier contrast pair to create a postactivation performance enhancing effect. Alternatively, cluster set designs can be used to maintain high velocities and reduce drop-off. Finally, traditional training is most effective for increasing squat 1-repetition maximum.

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

The increasing demand for higher data rates motivates the exploration of advanced techniques for future wireless networks. To this end, massive multiple-input multiple-output (mMIMO) is envisioned as the most essential technique to meet this demand. However, the expansion of the number of antennas in mMIMO systems with short coherence time makes the downlink channel estimation (DCE) overhead potentially overwhelming. As such, the number of training sequence (TS) needs to be significantly reduced. However, reducing the number of TS reduces the mean-squared error (MSE) accuracy significantly and to date it is not clear to what extend can this TS reduction affects the achievable sum rate performance. Therefore, this paper develops a low complexity and tractable TS solution for DCE and establishes an analytical framework for the optimum TS. Furthermore, the tradeoff between the achievable sum rate maximization criteria and the MSE minimization criteria is investigated. This investigation is essential to characterize the optimum TS length and the actual performance of mMIMO systems when the channel exhibits a limited coherence time. To this end, the statistical structure of mMIMO channels is exploited. In addition, this paper utilizes a random matrix theory (RMT) method to characterize the downlink achievable sum rate and MSE in a closed-form. This paper shows that maximizing the downlink sum rate criterion is more important than minimizing the MSE of the SINR only, which is typically considered in the conventional MIMO systems and/or in the time division duplex (TDD) mMIMO systems. The results demonstrate that a feasible downlink achievable sum rate can be achieved in an frequency division duplex (FDD) mMIMO system. This finding is necessary to extend the benefit of mMIMO systems to high frequency bands such as millimeter-wave (mmWave) and Terahertz (THZ) communications.

Training Optimization for Hybrid MIMO Communication Systems

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

Intelligent Reflecting Surface: Joint Optimal Training Sequence and Refection Pattern

We investigate channel estimation for an intelligent reflecting surface (IRS) aided wireless communication system with a multi-antenna access point (AP), an multi-element IRS, and a set of multiple users, each with a single antenna. Our goal is to minimize mean square error (MSE) of estimating the channels of direct and cascaded links from the AP to each user via the IRS elements by jointly optimizing the training sequence of the AP and reflection pattern of the IRS. To solve this challenging problem, we first find the joint optimal structure and reflection pattern for the training sequence, and then, we convert the problem into a convex form to design the optimal training sequence efficiently. To reduce the computational complexity, we also propose a suboptimal training sequence in closed form. Numerical results show that the proposed scheme outperforms baseline schemes.

Non-Iterative Downlink Training Sequence Design Based on Sum Rate Maximization in FDD Massive MIMO Systems

This paper considers the problem of downlink (DL) training sequence design with limited coherence time for frequency division duplex (FDD) massive MIMO systems in a general scenario of single-stage precoding and distinct spatial correlations between users. To this end, a computationally feasible solution for designing the DL training sequences is proposed using the principle of linear superposition of sequences constructed from the users' channel covariance matrices. Based on the non-iterative superposition training structure and the P-degrees of freedom (P-DoF) channel model, a novel closed-form solution for the optimum training sequence length that maximizes the DL achievable sum rate is provided for the eigenbeamforming (BF) precoder. Additionally, a simplified analysis that characterizes the sum rate performance of the BF and regularized zero forcing (RZF) precoders in closed-form is developed based on the method of random matrix theory and the P-DoF channel model. The results show that the superposition training sequences achieve almost the same rate performances as state-of-the-art training sequence designs. The analysis of the complexity results demonstrates that more than four orders-of-magnitude reduction in the computational complexity is achieved using the superposition training design, which signifies the feasibility of this approach for practical implementations compared with state-of-the-art iterative algorithms for DL training designs. Importantly, the results indicate that the analytical solution for the optimum training sequence length with the P-DoF channel model can be effectively used with high accuracy to predict the sum rate performance in the more realistic one ring (OR) channel model, and thus, near optimal solutions can be readily obtained without resorting to computationally intensive optimization techniques.

Robust Superimposed Training Optimization for UAV Assisted Communication Systems

In this paper, we propose a superimposed training based two-phase robust channel estimation scheme for the unmanned aerial vehicle (UAV) assisted cellular communication system, in which various unitarily-invariant channel statistics errors are considered. Specifically, in the first phase, mobile station (MS) estimates the UAV-MS channel via the UAV training sequence, of which the robust design can be solved based on convex-concave theory. While in the second phase, the superimposed training scheme is considered at the ground base station (GBS) to improve spectrum efficiency. Then the robust GBS training sequence, the information signal power and the UAV amplifying factor are jointly optimized for the partially cascaded GBS-UAV-MS channel estimation subject to GBS and UAV transmit power constraints as well as the required information signal strength at the MS. To tackle this NP-hard problem, the optimal structures of involved variables are firstly derived, based on which the robust superimposed training design is simplified and proved to be quasi-convex in the UAV amplifying factor. Particularly, for Spectral norm and Nuclear norm bounded errors, the optimal training sequence can be obtained via convex-concave theory and Golden section searchWhile for Frobenius norm bounded error, a tractable upper-bounding scheme is proposed for the robust superimposed training design. Furthermore, we extend our work into the more general probabilistic path loss scenario of UAV-ground channels, and analyze the impacts of the probabilistic path loss and Rician $K$ -factor on channel estimation performance. Numerical results illustrate the excellent performance of the proposed superimposed training based two-phase channel estimation scheme.

Burst-Mode Synchronization for SOQPSK

We consider a comprehensive synchronization strategy for burst-mode transmission of shaped-offset quadrature phase-shift keying (SOQPSK) signals over the additive white Gaussian noise channel. Due to the similarities between SOQPSK and continuous phase modulation (CPM), we make use of recent results for synchronization of burst-mode CPMs. We first derive a training sequence that is optimal in the sense that it jointly minimizes the Cramer–Rao bounds (CRBs) for frequency offset, phase offset, and timing offset estimation. Additionally, we develop a maximum likelihood data-aided algorithm for joint estimation of the synchronization parameters for SOQPSK signals. We show that the proposed algorithm for the optimal training sequence can be adapted to work with the suboptimal training sequence that is being considered for use in burst-mode integrated network enhanced telemetry. This demonstrates an immediate practical application for our approach. We present numerical results on the mean-squared error performance of the proposed algorithm for both training sequences and for different versions of SOQPSK. The numerical results show that our joint estimation algorithm yields performance that is very close to the CRBs for all three synchronization variables. Finally, we compare the overall performance of our proposed training sequence and synchronization algorithm for different sequence lengths by simulating a burst-mode SOQPSK receiver. This allows us to employ the right training sequence length based on the desired complexity and bit error rate performance.

Suboptimal training sequence design for synchronisation of burst‐mode CPM

In this study, they authors introduce a suboptimal training sequence for burst-mode continuous phase modulation. The optimum training sequence has been prove to be optimal for the estimates of carrier frequency offset, carrier phase error and timing error. However, the optimum sequence brings high side-lobe of the log-likelihood function (LLF), that is not benefit for detecting the start of the sequence (SOS). Thus, the suboptimal sequence is designed to improve the SOS performance through decreasing the side-log effect of the LLF. Meanwhile, the proposed sequence can minimise the Cramer-Rao bound for the joint estimation of carrier frequency offset, carrier phase error and symbol timing error. The data-aided maximum likelihood algorithms for timing error and phase error estimation are derived for evaluating the performance of the proposed sequence. The simulation results demonstrate that the new sequence provides better performance on start detection of the sequence than the optimal one. The performance of timing and carrier phase recovery is also close to the ideal synchronisation performance.

Optimal Pilot Design for MIMO Broadcasting Systems Based on the Positive Definite Matrix Manifold

In the MIMO broadcasting system, channel state information (CSI) is often used for data detection at the receiver or preprocessing techniques such as the power control and user scheduling at the transmitter and hence, the study of its acquisition is important. Usually, the CSI can be obtained by the training-based channel estimation. Assuming that the users have the channels’ statistical information and adopt the minimum mean-square-error (MMSE) receivers, this paper studies how to design an optimal training sequence to minimize the total weighted mean square error (MSE) of channel estimation. Following this objective, we formulate this problem as a positive semidefinite problem (SDP) with two constraints, and further transform it into a dual problem. In the solving process of the dual problem, we model it as an optimization problem on the positive definite matrix manifold and solve it by use of the iterative geodesic equation on the matrix manifold. The convergence and correctness of the proposed method are demonstrated by computer simulation; the effects of key system parameters on the weighted MSE are also investigated. Under various system configurations, the superiority of the proposed method is shown by comparing with a few other existing schemes.

Optimal Training for Residual Self-Interference for Full-Duplex One-Way Relays

Channel estimation and optimal training sequence design for full-duplex one-way relays are investigated. We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. In the presence of RSI, the overall source-to-destination channel becomes an inter-symbol-interference (ISI) channel. With the help of estimates of the RSI channel, the destination is able to cancel the ISI through equalization. We derive and analyze the Cramer-Rao bound (CRB) in closed-form by using the asymptotic properties of Toeplitz matrices. The optimal training sequence is obtained by minimizing the CRB. Extensions for the fundamental one-way relay model to the frequency-selective fading channels and the multiple relays case are also considered. For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed. Simulations using LTE parameters corroborate our theoretical results.

Delay Performance of Wireless Communications With Imperfect CSI and Finite-Length Coding

With the rise of critical machine-to-machine applications, next generation wireless communication systems must meet challenging requirements with respect to latency and reliability. A key question in this context relates to channel state estimation, which allows the transmitter to adapt the code rate to the channel state. In this paper, we characterize the tradeoff between the training sequence length and data codeword length: shorter channel estimation leaves more time for the payload transmission but reduces the estimation accuracy and causes more decoding errors. Using lower coding rates can mitigate this effect, but may result in a higher backlog of data at the transmitter. In order to optimize the training sequence length and the rate adaptation scheme with respect to the delay performance, we employ queuing analysis on top of accurate models of the physical layer. We obtain an analytically tractable solution to the problem by deriving a closed-form approximation for the decoding error probability due to imperfect channel knowledge and finite-blocklength channel coding. The optimized training sequence length and rate adaptation strategy can reduce the delay violation probability by an order of magnitude, compared with suboptimal strategies that do not consider the delay constraints.

Optimal Training Design for MIMO Systems With General Power Constraints

Training design for general multiple-input multiple-output (MIMO) systems is investigated in this paper. Unlike prior designs that are applicable only for centralized MIMO systems with total power constraints, general power constraints are considered here. They cover total power constraints, individual power constraints, and mixed individual and per-user sum-power constraints as special cases. By writing the MIMO received signals in matrix and vector forms, respectively, and using Kronecker structured channel and noise statistics, three channel estimation schemes, i.e., right estimation, left estimation, and right-left estimation, are discussed. Their corresponding training designs are considered individually with the general power constraints. Under each channel estimation scheme, optimal training sequences to maximize the mutual information between the true channel and its estimated counterpart, and to minimize the mean square error (MSE) of the channel estimate are, respectively, proposed in semiclosed forms. The relationship between the two design criteria, i.e., the mutual information maximization and the MSE minimization, is clearly revealed. The optimal training designs under the three estimation schemes are also compared in depth. It is demonstrated that right estimation exploits less statistical information about the channel and noise, and provides worse performance than the left estimation but with lower computational complexity. On the other hand, right-left estimation performs in between the other two and provides a good compromise between complexity and performance. Finally, the optimality and effectiveness of the proposed training designs are verified by extensive simulations.

Joint Estimation of Timing and Carrier Phase Offsets for MSK Signals in Alpha-Stable Noise

Impulsive noise modeled as symmetric α-stable (SαS) distribution is commonly seen in many practical communication scenarios. In this letter, we focus on the joint timing and carrier phase synchronization of minimum shift keying signals in SαS noise. We first derive the Cramér-Rao lower bound (CRLB) of joint timing and carrier phase offsets estimation. Then, an optimal synchronization training sequence is designed to minimize the CRLB. As the corresponding maximum likelihood estimator is hard to implement in SαS noise, we further propose a pragmatic synchronization parameters estimation algorithm based on explicit myriad cost function and a global optimization method. Extensive simulation results show that our proposed algorithm works well and is robust to the estimation errors of received signal-to-noise ratio and SαS noise parameter.