Offset Estimation Research Articles

Blind Carrier Frequency Offset Estimation in Weighted Fractional Fourier Transform Communication Systems

In this study, a blind carrier frequency offset (CFO) estimation method is proposed using the time-frequency symmetry of the transmitted signals of a weighted Fourier transform (WFrFT) communication system. Blind CFO estimation is achieved by focusing on the property that results in matching the signal waveforms before and after the Fourier transform when the WFrFT parameter is set to a certain value. Numerical simulations confirm that the proposed method is more resistant to Rayleigh fading than the conventional estimation methods.

Efficient Carrier Frequency Offset Estimation in Wireless Sensor Networks

The carrier frequency offset (CFO) estimation is a crucial problem in orthogonal frequency division multiplexing (OFDM) systems, especially for the enabling technologies in the Internet of Things, which demand stringent limitations of low cost, low power consumption, and wide ranges, like wireless sensor networks. In this letter, we propose an efficient CFO estimation method for OFDM systems with in- and quadrature-phase (IQ) imbalance, which combines the emerging multitask learning (MTL) with the channel residual energy (CRE) to reduce the average inferring time greatly. The MTL estimator can extract the correlated features between the CFO and the IQ imbalance so as to mitigate the intractable coupling effect. On the other hand, the CRE technique can guide the neural network and loss function design to improve the estimation accuracy and the training efficiency. Numerical results demonstrate that our proposal achieves high estimation accuracy while the inferring time is significantly reduced compared with other methods.

Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this paper, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multi-hop nodes and employs multi-hop average-consensus instead of single-hop average-consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multi-hop node is calculated and used to estimate the average value. Consequently, an average-consensus among local multi-hop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence.

A new scheme for adding a pilot at direct current component location in photonics-assisted OFDM MMW system

This paper proposes a new scheme, that is, a constant is added to the real part or imaginary part of the signal at the transmitting end, and the constant becomes a pilot at the location of DC component, thereby replacing the scheme of traditional manual bias IQ modulator to generate a pilot. By adopting this scheme, the purpose of frequency offset estimation can be achieved, and the performance can be improved by quantitatively optimizing the magnitude of the constant at the same time. Using this technology and combined probability shaping technology, a 3.4Gbaud 256QAM OFDM signal can be transmitted 700 m wireless distance, achieving the normalized general mutual information (NGMI) threshold of 0.83 with 25% soft-decision forward-error correction (SD-FEC) overhead.

Adversarial learning of permanent seismic deformation from GNSS coordinate timeseries

Deformation produced by an earthquake has a wide variety of forms. Therefore, there are a variety of models for quantifying the amount of deformation observed on GNSS coordinate timeseries, each of them based on different assumptions about the underlying mechanism that generates the data. Hence, it is of interest to look for methods relying on minimal assumptions about the observed position series.In this work we propose a semiparametric method, based on adversarial learning, to perform inference of the permanent seismic deformation. The only assumption made is that the probability distributions of GNSS fixed-length coordinate series for periods with and without observable seismic deformation differs mainly in the permanent deformation, represented by an additive scaled heaviside function.A dataset based on the series of GNSS coordinates published by the Nevada Geodetic Laboratory was built, and an adversarial model was trained over this dataset. In order to train the algorithm, an initial labeling of the samples was conducted using time, position an magnitude information of seismic events from the USGS database. It was shown that learning was possible with the available real data, and multiple sanity checks were run, showing consistency of the offset estimations compared with a trajectory model based estimator and with published offsets for well studied events on the South American active margin.To assess the capabilities of the method in a more controlled environment, further experiments were conducted on synthetic data. Those experiments confirmed that the presence of postseismic transient signals does not impede learning.As a derivative, our proposal allows to refine imperfect initial estimations for the presence/absence of seismic deformation.

Rapid Estimation of Undifferenced Multi-GNSS Real-Time Satellite Clock Offset Using Partial Observations

Real-time satellite clock offset is a crucial element for real-time precise point positioning (RT-PPP). However, the elapsed time for undifferenced (UD) multi-global navigation satellite system (GNSS) real-time satellite clock offset estimation at each epoch is increased with the growth of stations, which may fall short of real-time application requirements. Therefore, a rapid estimation method for UD multi-GNSS real-time satellite clock offset is proposed to improve the computation efficiency, in which both the dimension of the normal equation (NEQ) and the number of redundant observations are calculated before adjustment; if these two values are larger than the predefined thresholds, the elevation mask is gradually increased until they are less than the predefined thresholds. Then, the clock offset estimation is conducted; this method is called clock offset estimation using partial observations. Totals of 50, 60, 70 and 80 stations are applied to perform experiments. Compared to clock offset estimation using all observations, the elapsed times of clock offset estimation using partial observations can be reduced from 6.80 to 3.10 s, 7.93 to 2.97 s, 12.04 to 3.14 s for 60, 70 and 80 stations, respectively. By using the proposed method, the elapsed time of the clock offset estimation at each epoch is less than 5 s. The estimated clock offset accuracy for GPS, BDS-3, Galileo and GLONASS satellites are better than 0.04, 0.05, 0.03 and 0.16 ns when using the partial observations to estimate clock offset with 50, 60, 70 and 80 stations, respectively. For the multi-GNSS kinematic PPP using the estimated clock offset from 50, 60, 70 and 80 stations with partial observations, the positioning accuracy at 95% confidence level in the east, north and up direction are better than 2.70, 2.20 and 5.60 cm, respectively.

Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems.

Since orthogonal frequency division multiplexing (OFDM) systems are very susceptible to symbol timing offset (STO) and carrier frequency offset (CFO), which cause inter-symbol interference (ISI) and inter-carrier interference (ICI), accurate STO and CFO estimations are very important. In this study, first, a new preamble structure based on the Zadoff-Chu (ZC) sequences was designed. On this basis, we proposed a new timing synchronization algorithm, called the continuous correlation peak detection (CCPD) algorithm, and its improved algorithm: the accumulated correlation peak detection (ACPD) algorithm. Next, the correlation peaks that were obtained during the timing synchronization were used for the frequency offset estimation. For this, the quadratic interpolation algorithm was adopted as the frequency offset estimation algorithm, which was better than the fast Fourier transform (FFT) algorithm. The simulation results showed that when the correct timing probability reached 100%, under the parameters of m = 8 and N = 512, the performance of the CCPD algorithm was 4 dB higher than that of Du's algorithm, and that of the ACPD algorithm was 7 dB. Under the same parameters, the quadratic interpolation algorithm also had a great performance improvement in both small and large frequency offsets, when compared with the FFT algorithm.

Novel Control Theoretic Consensus-Based Time Synchronization Algorithm for WSN in Industrial Applications: Convergence Analysis and Performance Characterization

Fourth Industrial Revolution (IR4.0) depends heavily on the Internet of Things (IoT) and wireless sensor networks (WSNs). Through WSN, data in an industrial environment can be captured. Time synchronization is an important issue in WSN to ensure the correct sequence of the data collected. Besides, gauging on a common perceived time reference, local clocks on each node within any interconnected WSN should be able to initiate message exchanges. Referencing to an unsynchronized value of virtual global reference clock will render the nodes’ communication and applications in WSN useless. In this work, a newly improved control theoretic consensus-based time synchronization algorithm for WSN known as time synchronization using sliding-mode control and proportional, integral, and derivative (TSMPID) is proposed. The novel algorithm uses an augmented sliding-mode control (SMC) in the skew and relative skew estimation schemes, which is a useful switching action, to provide a salient feature in guaranteeing finite-time convergence in synchronized virtual time clock estimation. A proportional, integral, and derivative (PID) term has been introduced in the offset estimation to improve the estimation performance. The main purpose is to achieve global clock synchronization with lower computational effort and reduced synchronization error. TSMPID has the characteristics of being totally distributed, asynchronous, scalable across different network topological structures and robust to ad hoc nodes deployment. Ad hoc communication link node deployment scenarios are simulated comprising five, seven, and nine WSN nodes. TSMPID requires low energy and exhibits a much lesser consensus error spike in comparison to other recent prior-art time synchronization schemes.

Carrier recovery for satellite-to-ground coherent laser communication systems using double feedback loop and Viterbi–Viterbi feedforward cascade structure

High dynamic and wide range frequency offset in satellite-to-ground coherent optical communication system caused by Doppler frequency shift, transceiver laser frequency offset and laser frequency stabilization technology based on perturbation is one of the important differences compared with terrestrial fiber-optic coherent transmission. The design of a reliable frequency offset acquisition and fine carrier tracking algorithm is critical to ground optical stations. Two-stage feedback loop and Viterbi–Viterbi (V–V) feedforward cascaded carrier recovery algorithm is presented in this work. Pre-decision-based angle differential estimator (PADE) is adopted as the residual frequency offset (RFO) estimator in the first stage to estimate the high dynamic and wide range of frequency offset. Then V–V based RFO estimator in the second stage is employed to accurately compensate the residual frequency. V–V algorithm is used to compensate phase offset, which share the same logic resource with V–V based RFO estimator. A real-time 10-Gbps data rate PM-QPSK experiment based on FPGA is performed to evaluate the algorithm performance. The proposed cascaded algorithm exhibits excellent performance in terms of dynamic tracking speed and normalized mean-square-error (MSE) of the FOE.

CNN-Attention-DNN Design for CFO Estimation of Non-Pilot-Assisted OFDM System

In this letter, we propose a novel attention mechanism inspired convolutional neural network (CNN)-deep neural network (DNN) called CAD model architecture for carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) systems. The hyperparameters used in the proposed CAD model are optimized using the Bayesian approach. The proposed CFO estimation scheme is robust to small symbol timing offset errors and does not require apriori knowledge of modulation format, channel, and transmitted OFDM signal parameters. Simulations performed over the realistic frequency-selective channel models with and without power amplifier non-linearity indicate that the proposed CAD architecture outperforms the conventional statistical-based and recurrent neural network (RNN)-based models. Lastly, the proposed CAD model is validated on the radio frequency testbed.

Carrier phase recovery of LDPC-coded systems based on the likelihood difference algorithm

The problem of blind phase offset recovery of low density parity-check (LDPC)-coded systems is considered in this paper. We propose a new algorithm of phase offset estimation that involves the computation and maximization of a likelihood difference (LD)-based cost function calculated from the parity-check matrix of the code. We show in this paper that the new cost function has a simplified form compared to another algorithm proposed in the literature and presents similar estimation performance. Mean squared error (MSE) curves show very good performance of the proposed phase offset estimation algorithm, even at low signal-to-noise ratios.

Hardware-efficient and accurately frequency offset compensation based on feedback structure and polar coordinates processing

Compared with off-line digital signal processing (DSP) verification, achieving the expected performance and hardware efficiency in application specific integrated circuit (ASIC) is more critical for DSP to reduce power consumption and cost. The performance of traditional frequency offset compensation algorithms based on feedforward structure is strongly dependent on the accuracy of frequency offset estimation. Therefore, frequency offset calculation and loop filtering implemented in ASIC or field programmable gate array (FPGA) usually choose high word-width fixed-point or floating-point operations, which increase the consumption of logical resources and reduce the clock frequency that hardware logic can achieve. This work proposes a frequency offset compensation scheme based on polar coordinate processing and feedback structure, and a pre-decision-based angle differential estimator is used to estimate the residual frequency offset. In the proposed feedback structure, the frequency offset estimator is realized by a simple accumulator, and the input of the accumulator is the residual frequency offset or its scaling, which will reduce the requirement for the accuracy of the residual frequency offset estimator. The offline verification results show that the performance of the proposed algorithm is close to that of the traditional algorithm, but the proposed algorithm has lower logic resource consumption and higher clock frequency in hardware implementation based on FPGA. The performance of the proposed method is evaluated in real-time through 10-Gbps data rate polarization-multiplexed quadrature phase shift keying modulation after 20-km standard single-mode fiber transmission. Compared with offline processing in MATLAB®, no hardware implementation penalty is observed.

New Preamble Sequence for WiMAX System with Improved Synchronization Accuracy

Worldwide Interoperability for Microwave Access (WiMAX) trusts Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) combination for the deployment of physical layer functions and for connecting the medium access control to the wireless media. Even though Orthogonal Frequency Division Multiplexing (OFDM) facilitates reliable digital broadband transmission in the fading wireless channels, the presence of synchronization errors in the form of Carrier Frequency Offset (CFO) and Time Offset (TO) adversely affect the performance of OFDM based physical layers. The objective of this work is to improve the accuracy of the frequency and the time offset estimation in the WiMAX physical layer. A method to enhance the synchronization accuracy by fine-tuning the merit factor of the preamble sequence is suggested in this paper. Also, a new preamble with improved synchronization accuracy is proposed for the WiMAX system. The performance of the proposed preamble is evaluated in a Rayleigh fading channel and the results of simulations show that the Mean Square Error (MSE) in offset estimation is significantly reduced and it outperforms the standard WiMAX preamble.

Estimation of Array Locations, Orientations, Timing Offsets and Target Locations in Bistatic Radars

Estimation of Array Locations, Orientations, Timing Offsets and Target Locations in Bistatic Radars

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as -10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different ways have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.

Novel Data-Aided Carrier Frequency Offset Compensation Methods Using Asymmetric-Shape Constellations for Burst-Mode Coherent Reception

This paper introduces a novel data-aided carrier frequency offset (CFO) estimation and compensation method for m-array phase shift keying (m-PSK) signals in burst-mode digital coherent reception. Its key advances are as follows; the use of newly designed training symbols (TS) that exhibit asymmetric distributed constellations after differential decoding and a vector summation based feed-forward CFO estimation algorithm. These offer short response time and strong robustness to training symbols (TS) timing detection error with wide CFO estimation range of ±B/2, where B is signal baudrate. We evaluate the feasibility of the proposal by simulations of and experiments on 12.5-Gbaud SP-QPSK signals that target optical budget enhancement of 25-Gbps/λ-class PONs to realize long-reach and high splitting ratio optical access. The 100-km burst-mode transmission experiments confirm the proposal offers high sensitivity detection of better than −43 dBm with much wider CFO range of above ±10 GHz.

Distributed Sampling Rate Offset Estimation Over Acoustic Sensor Networks Based on Asynchronous Network Newton Optimization

Sampling rate synchronization is an inevitable issue in distributed acoustic sensor networks. In this paper, an analytical sampling rate offset (SRO) estimation approach is first proposed, and then, it is extended to a distributed method that suitable for acoustic sensor networks with arbitrary communication graphs. Specifically, a linear-phase drift model in the short-time Fourier transform domain is used to approximate the SRO between each pair of microphone nodes. Next, after unwrapping the temporally averaged phase information, SROs are recovered analytically via a new weighted-sum criterion. Based on this, a distributed cost function is established at each node to obtain the SROs of all nodes simultaneously in a distributed manner. Finally, a state-of-the-art distributed algorithm named asynchronous network Newton optimization is adopted to carry out the distributed SRO estimation. The proposed method can effectively estimate the SROs among acoustic sensor nodes in noisy and reverberant environments. Compared with the existing approaches, it does not require an external central processor, and only local communications among nodes are needed. Experimental results confirm the validity of the proposed method.

Realizing rapid re-convergence in multi-GNSS real-time satellite clock offset estimation with dual-thread integer ambiguity resolution

Realizing rapid re-convergence in multi-GNSS real-time satellite clock offset estimation with dual-thread integer ambiguity resolution

Identification quality assessment of the complex object management in multicollinearity conditions

The article presents the method of assessing the quality of the complex object management (COM) identification process in multicollinearity conditions. The main problem in the process of identifying complex object management is assessing its quality. When operating COM, multicollinearity often arises, which significantly complicates the task. Nowadays, a lot of evaluation methods have been developed and studied. Despite this, when solving practical tasks related to processing data from experiments about the management object, the accuracy of the MNS becomes insufficient. As a criterion for the optimality of the regression model, it is proposed to use the amount of forecast error in a given area. A method for finding the optimal regularization parameter for offset estimation of regression equation parameters is proposed. A scientific substantiation of the principles of assessing the quality of the COM identification process in multicollinearity conditions are resolved by building its predictive model using the method of offset assessment of regression equation parameters. In this case, a method is proposed for selecting the regularization parameter r based on the minimum root-mean-square error of the forecast obtained from this model. The simulation experiments using the technique, shown that the value of the optimal regularization parameter ropt obtained is close to the experimental one. This confirms the correctness of the proposed approach. Thus, the quality of the predictive model under conditions of multicollinearity has been improved, taking into account the uncertainty of the model structure and the method of biased estimation of model parameters.

Local phase center hodograph of radiating element’s navigation antenna Glonass

The main methods for the determination of the object position using code phase and signal carrier phase measurements are given. An accuracy of pseudorange measurements of both carrier phase and group delay code signal depends on local phase center variations for the signals coming from different directions. The relation of group delay and local phase center of the antenna is determined wherein the key factor is frequency dependence of phase or local phase center variation in solid angle of the radiation pattern. The definition of local phase center whose coordinates depends on the direction of radio signal is given. The single helical antenna analysis of local phase center position is carried out considering that the radio technical performances of the navigation antenna’s spacecraft «Glonass» depends on the same ones of its radiating elements. The features and the technique for hodographs computation of the helical radiator’s navigation phased antenna array using electrodynamic simulation tools are considered. The offset estimation of local phase center is obtained. The features of that offset in relation to the radiator construction depending on the operating frequency are detected.