Review on Phase Synchronization Methods for Spaceborne Multistatic Synthetic Aperture Radar.

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated.

Performance Evaluation of Carrier-Frequency Offset as a Radiometric Fingerprint in Time-Varying Channels.

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter-receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.

An improved asymmetric susceptibility tensor imaging model with frequency offset correction.

PURPOSE: To improve susceptibility tensor imaging (STI) reconstruction using the asymmetric STI model with the correction of non-bulk-magnetic-susceptibility (NBMS) effects. METHOD: A frequency offset term was introduced into the asymmetric STI model to account for the bias between measured MRI frequency signals and conventional susceptibility tensor models because of NBMS contributions. Experiments were conducted to compare the proposed model with conventional STI, conventional STI with the proposed frequency offset correction, and asymmetric STI on simulation, ex vivo mouse brain, and in vivo human brain data. RESULTS: In the simulation where NBMS contributions are head rotation-invariant, the proposed method achieves the lowest errors in mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) and is more robust to noise in the estimation of principal eigenvector (PEV). When considering the head orientation dependency of NBMS contributions, the proposed method shows advantages in estimating MSA and PEV. On the mouse and human brain data, the proposed method produces more reliable MSA maps and more consistent white matter fiber directions when referring to those from DTI than the compared STI methods. CONCLUSION: The proposed method can reduce the effects of NBMS-related frequency shifts on the susceptibility tensors in the brain white matter. This study inspires STI reconstruction from the perspective of better modeling the sources of frequency shifts.

Three Level Recognition Based on the Average of the Phase Differences in Physical Wireless Parameter Conversion Sensor Networks and Its Effect to Localization with RSSI.

In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.

Nonlinear Frequency Offset Beam Design for FDA-MIMO Radar.

The beam pattern of frequency diversity array (FDA) radar has a range-angle two-dimensional degree of freedom, which makes it possible to distinguish different targets from the same angle and brings a new approach to anti-jamming of radars. However, the beam pattern of conventional linearly frequency-biased FDA radar is range-angle-coupled and time-varying. The method of adding nonlinear frequency bias among the array elements of the FDA array has been shown to eliminate this coupling property while still allowing for better beam performance of the emitted beam. In this paper, we obtain a decoupled and time-invariant beam direction map using the FDA-multi-input-multi-output (FDA-MIMO) radar scheme and then obtain a sharp pencil-shaped main sphere beam pattern with range-angle dependence using a linear frequency offset scheme weighted by a Chebyshev window. Finally, the anti-interference performance of the proposed method is verified in an anti-interference experiment.

Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication.

Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only -0.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.

Two-Dimensional Barrage Jamming against SAR Using a Frequency Diverse Array Jammer.

Due to the modulation of tiny frequency offset on the array elements, a frequency diverse array (FDA) jammer can generate multiple range-dimension point false targets, and many deception jamming methods against SAR using an FDA jammer have been studied. However, the potential of the FDA jammer to generate barrage jamming has rarely been reported. In this paper, a barrage jamming method against SAR using an FDA jammer is proposed. To achieve two-dimensional (2-D) barrage effect, the stepped frequency offset of FDA is introduced to generate range-dimensional barrage patches, and the micro-motion modulation is employed to increase the extent of barrage patches along the azimuth direction. Mathematical derivations and simulation results demonstrate the validity of the proposed method in generating flexible and controllable barrage jamming.

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.

Utilization of Carrier-Frequency Offset Measurements in UWB TDoA Positioning with Receiving Tag.

High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate their position based on differences in the time of arrival of those messages. However, the drift of the tag clock causes systematic errors at a sufficiently high magnitude to effectively deny the positioning, if left uncorrected. Previously, the extended Kalman filter (EKF) has been used to track and compensate for the clock drift. In this article, the utilization of a carrier frequency offset (CFO) measurement for suppressing the clock-drift related error in anchor-to-tag positioning is presented and compared to the filtered solution. The CFO is readily available in the coherent UWB transceivers, such as Decawave DW1000. It is inherently related to the clock drift, since both carrier and timestamping frequencies are derived from the identical reference oscillator. The experimental evaluation shows that the CFO-aided solution performs worse than the EKF-based solution in terms of accuracy. Nonetheless, with CFO-aiding it is possible to obtain a solution based on measurements from a single epoch, which is favorable especially for power-constrained applications.

A Comprehensive Experimental Emulation for OTFS Waveform RF-Impairments.

The orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in a high-mobility environment with high time-frequency dispersive channels. Since radio frequency (RF) impairments have a direct impact on waveform behavior, this paper investigates the experimental implementation of RF-impairments that affect OTFS waveform performance and compares them to the OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, and then an experimental framework is established for investigating the impact of RF-impairments, including non-linearity, carrier frequency offset (CFO), I/Q imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR) at carrier frequencies of 2.4 GHz and 5 GHz based on the Keysight EXG X-Series devices. The comparison of the performances of OFDM and OTFS in the presence of RF-impairments reveals that OTFS significantly outperforms OFDM.

Signal Detection for GSTFIM Systems with Carrier Frequency Offset.

Generalized space-time-frequency index modulation (GSTFIM) inherits the drawbacks of the conventional orthogonal frequency-division multiplex (OFDM), such as being sensitive to carrier frequency offset (CFO). For a robust design against this problem, in this contribution, a novel construction of a message passing (MP)-aided detector is developed for GSTFIM systems to combat the influence of CFO, while offering a flexible tradeoff between transmission performance and computational complexity. Through complexity analysis and simulation results, we demonstrate that, in the context of CFO, with a careful design, the developed MP detector is capable of approaching traditional GSTFIM with maximum likelihood (ML) detection, and of offering better performance at lower complexity compared to its minimum mean-square-error (MMSE)-aided counterpart.

ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks.

As the applications of the internet of things are becoming widely diversified, wireless sensor networks require real-time data reception, accommodation of access from several sensors, and low power consumption. In physical wireless parameter conversion sensor networks (PhyC-SN), all the sensors use frequency shift keying as the modulation scheme and then access the channel to the fusion center, simultaneously. As a result, the fusion center can recognize the statistical tendency of all the sensing results at a time from the frequency spectrum of the received signal. However, the information source, i.e., the sensor, cannot be specified from the received signal because no ID-indicating sensor is inserted to the signal. The data-tracking technique for tracing the time continuity of the sensing results is available for decomposing the sequence of the sensing results per sensor but the error tracking, which is a wrong recognition between the sensing results and the sensor, occurs owing to the similarity of the sensing results. This paper proposes the sensing result separation technique using a fractional carrier frequency offset (CFO) for PhyC-SN. In the proposed scheme, the particular fractional CFO is assigned to each user and it is useful for the ID specifying sensor. The fractional CFO causes inter-carrier interference (ICI). The ICI cancellation of the narrowband wireless communications is proposed. The two types of data-tracking techniques are proposed and are selectively used by the fusion center. Since the proposed data-tracking technique is multi-dimensional, high accuracy of data tracking is achieved even under the similar tendency of the sensing results. Based on computer simulation, we elucidate the advantage of the proposed sensing results separation.

An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar.

For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance-for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe-can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations.

Frequency Offset Tolerant Synchronization Signal Design in NB-IoT.

Timing detection is the first step and very important in wireless communication systems. Timing detection performance is usually affected by the frequency offset. Therefore, it is a challenge to design the synchronization signal in massive narrowband Internet of Things (NB-IoT) scenarios where the frequency offset is usually large due to the low cost requirement. In this paper, we firstly proposed a new general synchronization signal structure with a couple of sequences which are conjugated to remove the potential timing error that arises from large frequency offset. Then, we analyze the suitable sequence for our proposed synchronization signal structure and discuss a Zadoff⁻Chu (ZC) sequence with root 1 as an example. Finally, the simulation results demonstrate that our proposed synchronization signal can work well when the frequency offset is large. It means that our proposed synchronization signal design is very suitable for the massive NB-IoT.

MAGPI: A framework for maximum likelihood MR phase imaging using multiple receive coils.

PURPOSE: Combining MR phase images from multiple receive coils is a challenging problem, complicated by ambiguities introduced by phase wrapping, noise, and the unknown phase-offset between the coils. Various techniques have been proposed to mitigate the effect of these ambiguities but most of the existing methods require additional reference scans and/or use ad hoc post-processing techniques that do not guarantee any optimality. THEORY AND METHODS: Here, the phase estimation problem is formulated rigorously using a maximum-likelihood (ML) approach. The proposed framework jointly designs the acquisition-processing chain: the optimized pulse sequence is a single multiecho gradient echo scan and the corresponding postprocessing algorithm is a voxel-per-voxel ML estimator of the underlying tissue phase. RESULTS: Our proposed framework (Maximum AmbiGuity distance for Phase Imaging, MAGPI) achieves substantial improvements in the phase estimate, resulting in phase signal-to-noise ratio (SNR) gains by up to an order of magnitude compared to existing methods. CONCLUSION: The advantages of MAGPI are: (1) ML-optimal combination of phase data from multiple receive coils, without a reference scan; (2) voxel-per-voxel ML-optimal estimation of the underlying tissue phase, without the need for phase unwrapping or image smoothing; and (3) robust dynamic estimation of channel-dependent phase-offsets.

Improved estimation of myelin water fraction using complex model fitting.

In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

Improved efficiency on editing MRS of lactate and γ-aminobutyric acid by inclusion of frequency offset corrected inversion pulses at high fields.

γ-Aminobutyric acid (GABA) and lactate are metabolites which are present in the brain. These metabolites can be indicators of psychiatric disorders or tumor hypoxia, respectively. The measurement of these weakly coupled spin systems can be performed using MRS editing techniques; however, at high field strength, this can be challenging. This is due to the low available B1 (+) field at high fields, which results in narrow-bandwidth refocusing pulses and, consequently, in large chemical shift displacement artifacts. In addition, as a result of the increased chemical shift displacement artifacts and chemical shift dispersion, the efficiency of the MRS method is reduced, even when using adiabatic refocusing pulses. To overcome this limitation, frequency offset corrected inversion (FOCI) pulses have been suggested as a mean to substantially increase the bandwidth of adiabatic pulses. In this study, a Mescher-Garwood semi-localization by adiabatic selection and refocusing (MEGA-sLASER) editing sequence with refocusing FOCI pulses is presented for the measurement of GABA and lactate in the human brain. Metabolite detection efficiencies were improved by 20% and 75% for GABA and lactate, respectively, when compared with editing techniques that employ adiabatic radiofrequency refocusing pulses. The highly efficient MEGA-sLASER sequence with refocusing FOCI pulses is an ideal and robust MRS editing technique for the measurement of weakly coupled metabolites at high field strengths.

Algorithm for frequency capture and rectification in a low-orbit satellite IoT communication network.

In satellite communication systems, due to relative motion between satellites and that between satellites and the ground, the resulting Doppler frequency offset adversely affects communication synchronization. In this research, Doppler frequency offset compensation and phase offset compensation method eliminate the influence of the Doppler effect on synchronization. The proposed algorithm divides frequency estimate into two steps, coarse and precision. Finally, the corresponding frequency offset and phase offset compensation are performed. The simulation results show that the demodulated output results after frequency offset and phase offset compensation agree well with the original modulation data, indicating that the algorithm is valid and accurate.

Joint frequency offset, time offset, and channel estimation for OFDM/OQAM systems.

Among the multicarrier modulation techniques considered as an alternative to orthogonal frequency division multiplexing (OFDM) for future wireless networks, a derivative of OFDM based on offset quadrature amplitude modulation (OFDM/OQAM) has received considerable attention. In this paper, we propose an improved joint estimation method for carrier frequency offset, sampling time offset, and channel impulse response, needed for the practical application of OFDM/OQAM. The proposed joint ML estimator instruments a pilot-based maximum-likelihood (ML) estimation of the unknown parameters, as derived under the assumptions of Gaussian noise and independent input symbols. The ML estimator formulation relies on the splitting of each received pilot symbol into contributions from surrounding pilot symbols, non-pilot symbols and additive noise. Within the ML framework, the Cramer-Rao bound on the covariance matrix of unbiased estimators of the joint parameter vector under consideration is derived as a performance benchmark. The proposed method is compared with a highly cited previous work. The improvements in the results point to the superiority of the proposed method, which also performs close to the Cramer-Rao bound.