High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

MOVING OBJECT TRACKING IN DYNAMIC IMAGE SEQUENCE BASED ON ESTIMATION OF MOTION VECTORS OF FEATURE POINTS

An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.

Approach to blind estimation of the PN sequence in DS-SS signals with residual carrier

This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.

Fast method for spreading sequence estimation of DSSS signal based on maximum likelihood function

To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).

Interference-Free Pilot Design and Channel Estimation Using ZCZ Sequences for MIMO-OFDM-Based C-V2X Communications

Cellular vehicle-to-everything(C-V2X) communications is regarded as a promising and feasible solution for 5G-enabled vehicular communications and networking. In this paper, we investigate the pilot design and channel estimation problem in MIMO-OFDM-based C-V2X systems with severe co-channel interference due to spectrum reusing among different V2X communication links. By using zero-correlation zone(ZCZ) sequences, we provide an interference-free pilot design scheme and a corresponding time-domain(TD) correlation-based channel estimation(TD-CCE) method. We employ the ZCZ sequences from the same family set to be designed as the TD pilot symbols and guarantee the pilot sequeneces for neighboring V2X communication links are code-division multiplexing(CDM). The co-channel pilot interference of the deisgned pilot symbols can be effectively eliminated by exploiting the provided TD-CCE method. Simulation results indicate that the accuracy of channel estimation can be effectively improved by the proposed scheme, whose performance is close to that of the non-interference situation.

A Novel Training Sequence Applied to DCS-Based Channel Estimation

Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.

A Hybrid Estimation of Distribution Algorithm for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times

A hybrid estimation of distribution algorithm (EDA) with iterated greedy (IG) search (EDA-IG) is proposed for solving the unrelated parallel machine scheduling problem with sequence-dependent setup times (UPMSP-SDST). For makespan criterion, some properties about neighborhood search operators to avoid invalid search are derived. A probability model based on neighbor relations of jobs is built in the EDA-based exploration phase to generate new solutions by sampling the promising search region. Two types of deconstruction and reconstruction as well as an IG search are designed in the IG-based exploitation phase. Computational complexity of the algorithm is analyzed, and the effect of parameters is investigated by using the Taguchi method of design-of-experiment. Numerical tests on 1640 benchmark instances are carried out. The results and comparisons demonstrate the effectiveness of the EDA-IG. Especially, the bestknown solutions of 531 instances are updated. In addition, the effectiveness of the properties is also demonstrated by numerical comparisons. © 2014 Chinese Association of Automation.

Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

An idea of estimating the direct sequence spread spectrum（DSSS） signal pseudo-noise（PN） sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map（SOFM） neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise（SNR） and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm＇s bit error rate（BER） and complexity are lower.

Time-domain training sequences design for MIMO OFDM channel estimation

This paper describes a Least Squares (LS) channel estimation scheme for MIMO OFDM systems based on time-domain training sequence. We first compute the minimum mean square error (MSE) of the LS channel estimation, and then derive the optimal criteria of the training sequence with respect to the minimum MSE. It is shown that optimal time-domain training sequence should satisfy two criteria. First, the autocorrelation of the sequence transmitted from the same antenna is an impulse function in a region longer than the channel maximum delay. Second, the cross-correlation between sequences transmitted from different antennas is zero in this region. Simulation results show that the estimator using optimal time-domain training sequences has better performance than that using optimal frequency training sequence at low signal-to-noise ratio (SNR). To reduce the training overhead, a suboptimal training sequence is also proposed. Comparing with optimal training sequence, it has low computation complexity and high transmission efficiency at the expense of little performance degradation.

An efficient training sequences strategy for channel estimation in OFDM systems with transmit diversity

This paper deals with channel estimation for orthogonal frequency-division multiplexing (OFDM) systems with transmit diversity. Space time coded OFDM systems, which can provide transmit diversity, require perfect channel estimation to improve communication quality. In actual OFDM systems, training sequences are usually used for channel estimation. The authors propose a training based channel estimation strategy suitable for space time coded OFDM systems. This novel strategy provides enhanced performance, high spectrum efficiency and relatively low computation complexity.

ON CHANNEL ESTIMATION USING OPTIMAL TRAINING SEQUENCES IN CYCLIC-PREFIX-BASED SINGLE-CARRIER SYSTEMS WITH SPACE-TIME BLOCK-CODING

In this paper, a new scheme that combines Space-Time Block-Coding (STBC) based on an Alamouti-like scheme and the Least Squares (LS) channel estimation using optimal training sequences in Cyclic-Prefix-based (CP)\Single-Carrier (SC) systems is proposed. With two transmit antennas, based on Cramer-Rao lower bound for channel estimation, it is shown that the Periodic Comple- mentary Set (PCS) is optimal over frequency-selective fading channels. Compared with the normal scheme without STBC, 3dB Mean Square Error (MSE) performance gains and fewer restrictions on the length of channel impulse response are demonstrated.

Strong Convergence Rates of Double Kernel Estimates of Conditional Desity Under Stationary Sequences

In the paper,we study the strong convergence rates of double kernel estimates of conditional density under stationary sequences.

Hepatic MR imaging using IDEAL-IQ sequence:Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

BACKGROUND Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence(IDEAL-IQ)is based on chemical shift-based water and fat separation technique to get proton density fat fraction.Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy.AIM To explore whether Gadoxetate Disodium(Gd-EOB-DTPA)interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique.METHODS IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast.After injection,IDEAL-IQ was estimated four times,and the fat fraction(FF)and R2* were measured at the following time points:Precontrast,between the portal phase(70 s)and the late phase(180 s),the delayed phase(5 min)and the hepatobiliary phase(20 min).One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points.Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent.P<0.05 was considered statistically significant.RESULTS The assessment of the FF at the four time points in the liver,spleen and spine showed no significant differences,and the measurements of hepatic FF yielded good consistency between T1 and T2[95%confidence interval:-0.6768%,0.6658%],T1 and T3(-0.3900%,0.3178%),and T1 and T4(-0.3750%,0.2825%).R2* of the liver,spleen and spine increased significantly after injection(P<0.0001).CONCLUSION Using the IDEAL-IQ sequence to measure the FF,we can obtain results that will not be affected by Gd-EOB-DTPA.The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.

Asymptotic normality of error density estimator in stationary and explosive autoregressive models

In this paper,we consider the limit distribution of the error density function estima-tor in the rst-order autoregressive models with negatively associated and positively associated random errors.Under mild regularity assumptions,some asymptotic normality results of the residual density estimator are obtained when the autoregressive models are stationary process and explosive process.In order to illustrate these results,some simulations such as con dence intervals and mean integrated square errors are provided in this paper.It shows that the residual density estimator can replace the density\estimator"which contains errors.

POWER CONTROL WITH CODER-ESTIMATOR SEQUENCES

Power control is an important issue in mobile communication systems and has been studied extensively in the literature. This paper has taken on a new perspective and modeled the problem as a state estimation problem with finite communication constraints. The problem is solved by using the 1-bit mean coder-estimator sequence. The performance of the coder-estimator sequence is compared with that of the simple feedback power control technique by evaluating their respective fade margins as in commonly done in radio system engineering.

Pseudo-noise preamble based joint frame and frequency synchronization algorithm in OFDM communication systems

Frame and frequency synchronization are essential for orthogonal frequency division multiplexing （OFDM） systems. The frame offset owing to incorrect start point position of the fast Fourier transform （FFT） window, and the carrier frequency offset （CFO） due to Doppler frequency shift or the frequency mismatch between the transmitter and receiver oscil ators, can bring severe inter-symbol interference （ISI） and inter-carrier interference （ICI） for the OFDM system. Relying on the relatively good correlation charac-teristic of the pseudo-noise （PN） sequence, a joint frame offset and normalized CFO estimation algorithm based on PN preamble in time domain is developed to realize the frame and frequency synchronization in the OFDM system. By comparison, the perfor-mances of the traditional algorithm and the improved algorithm are simulated under different conditions. The results indicate that the PN preamble based algorithm both in frame offset estimation and CFO estimation is more accurate, resource-saving and robust even under poor channel condition, such as low signal-to-noise ratio （SNR） and large normalized CFO.

Periodic performance of the chaotic spread spectrum sequence on finite precision

It is well known that the periodic performance of spread spectrum sequence heavily affects the correlative and secure characteristics of communication systems. The chaotic binary sequence is paid more and more attention since it is one kind of applicable spread spectrum sequences. However, there are unavoidable short cyclic problems for chaotic binary sequences in finite precision. The chaotic binary sequence generating methods are studied first. Then the short cyclic behavior of the chaotic sequences is analyzed in detail, which are generated by quantification approaches with finite word-length. At the same time, a chaotic similar function is defined for presenting the cyclic characteristics of the sequences. Based on these efforts, an improved method with scrambling control for generating chaotic binary sequences is proposed. To quantitatively describe the improvement of periodic performance of the sequences, an orthogonal estimator is also defined. Some simulating results are provided. From the theoretical deduction and the experimental results, it is concluded that the proposed method can effectively increase the period and raise the complexity of the chaotic sequences to some extent.

Strong Convergence of Partitioning Estimation for Nonparametric Regression Function under Dependence Samples

In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.

Optimal training sequences for MIMO systems under correlated fading

The optimal design of training sequences for channel estimation in multiple-input multiple-output （MIMO） systems under spatially correlated fading is considered. The channel is assumed to be a block-fading model with spatial correlation known at both the transmitter and the receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of the spatial correlation under the criterion of minimum mean square error （MMSE）. It is investigated that the spatial correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.

BIT-CORRELATION MATCHING FUNCTION FOR IMAGE SEQUENCE CODING

This paper proposes a new block matching criterion called the bit-correlation matching function for image sequence coding. When using the identical fast searching algorithm, the bit-correlation matching function not only results in nearly the same accuracy in displacement estimation as the mean square error function, but also makes the algorithm low in computation complexity and easy to parallel implementation, thus reducing the coding time of image sequence efficiently.