Frequency Offsets Research Articles

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Aspect ratio optimization of piezoelectric extensional mode resonators for quality factor and phase noise performance enhancement

Abstract This study focuses on optimizing the resonator geometry via the aspect ratio design of a width-extensional mode resonator to improve its quality factor (Q), which is one of the critical performance parameters for resonators in either sensing (Allan deviation) or frequency reference (phase noise) applications. The proposed approach uses finite element analysis to reduce the strain energy at anchor supports by altering the resonator geometric structure, thereby reducing energy loss through anchors. Moreover, process limitations on feature sizes are used as constraints to find aspect ratios that can not only increase the Q but also reduce spurious modes near the targeted frequency. The devices were fabricated using AlN thin film piezoelectric on a substrate (TPoS) process. The simulated energy dissipation trends for specific length-to-width (L/W) ratios closely match the measured changes in the resonator Q values in vacuum. In vacuum, the highest Q-factor achieved by the device is close to 8816, with a motional resistance of a few tens of ohms. Additionally, a board-level oscillator realized using a commercial low-noise amplifier exhibits phase noise performance of −141.21 dBc Hz−1 and −164.25 dBc Hz−1 at 1 kHz and 1 MHz frequency offsets, respectively. The calculated figures of merit for these offsets are 204 and 168, respectively.

Dataset Augmentation and Fractional Frequency Offset Compensation Based Radio Frequency Fingerprint Identification in Drone Communications

Dataset Augmentation and Fractional Frequency Offset Compensation Based Radio Frequency Fingerprint Identification in Drone Communications

Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements

AbstractAirborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction‐finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one‐dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far‐field scenarios.

Sampling frequency offset compensation scheme based on machine learning for MB-OFDM UWBoF system

Digital to analogue converter (DAC) at the transmitter side and analogue to digital converter (ADC) at the receiver side with not exactly same clock period leads to sampling frequency offset (SFO) that affects the performance of multi-band ultra-wideband orthogonal frequency division multiplexing over fiber (MB-OFDM UWBoF) system, four machine-learning algorithms are proposed for compensating the SFO for the system and are compared in this paper. Simulation results show that the best compensation among the three conventional machine learning algorithms after 100 km standard single mode fiber (SSMF) transmission is the self-organising mapping (SOM) algorithm, which is able to compensate the SFO in the range of ±150 ppm, with a high complexity. The next best is the conventional kmeans algorithm which can compensate ±40 ppm and is slightly better compared to the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The DBSCAN algorithm only compensates around ±25 ppm of SFO, when the constellation points are slightly chaotic, the wrong cluster results in failure of demodulation, which makes it less applicable for problems of SFO. Moreover, the improved kmeans algorithm proposed in this paper can compensate the SFO of ±225 ppm, better than three conventional machine learning algorithms. And it can still make the BER lower than 3.8e-3 at received optical power (ROP) of −15 dBm when the SFO is 225 ppm.

Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.

To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra. Fisher information gain analysis identified optimal frequency offsets (termed "Fisher offsets") for the multi-pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U-NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient-like cases. Traditional multi-pool models failed to quantify CEST maps from undersampled images (structural similarity index [SSIM] <0.2, peak SNR <20, Pearson r <0.1). Conversely, U-NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z-spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e-4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology. The U-NET architecture effectively quantifies CEST maps from undersampled Z-spectra at various undersampling levels.

Thinned and Sparse Beamforming for Semicircular FDAs in the Transmit–Receive Domain

The thinned and sparse beamforming for semicircular FDAs were investigated, where the excitation amplitudes were also considered in thinned semicircular FDAs, and only the elements’ positions were incorporated into the sparse semicircular FDA. Firstly, the transmit–receive model was introduced to handle the inherent time-varying issue of FDA, followed by the thinned and sparse implementations successively. Note that three types of non-linearly varying frequency offsets (FO), i.e., log-FO, sin-FO, and tanh-FO, were adopted during the investigations. Under the same assumption that 50% of the elements should be saved, the sidelobe levels (SLLs) of the thinned semicircular -FDAs were reduced by 5.8 dB, 4.4 dB, and 4.4 dB, and the widths of the mainlobes were all widened by 3° in their angle dimension. Compared with the thinned semicircular FDAs, the phenomenon of mainlobe widening was alleviated in the sparse semicircular FDAs where the SLLs were reduced by 2.2 dB, 3.7 dB and 3.5 dB, and the mainlobes’ widths in the angle dimension were widened by 1°, 0° and 1°, respectively. It should be highlighted that the sparse semicircular FDA with sin-FO did not broaden the mainlobe in the angle dimension. Therefore, it can be concluded that a sparse semicircular FDA is superior over a thinned semicircular FDA, since it can reduce the same cost with a higher array resolution.

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

Simultaneous frequency and phase corrections of single-shot MRS data using cross-correlation.

The objective of this study was to propose a novel preprocessing approach to simultaneously correct for the frequency and phase drifts in MRS data using cross-correlation technique. The performance of the proposed method was first investigated at different SNR levels using simulation. Random frequency and phase offsets were added to a previously acquired STEAM human data at 7 T, simulating two different noise levels with and without baseline artifacts. Alongside the proposed spectral cross-correlation (SC) method, three other simultaneous alignment approaches were evaluated. Validation was performed on human brain data at 3 T and mouse brain data at 16.4 T. The results showed that the SC technique effectively corrects for both small and large frequency and phase drifts, even at low SNR levels. Furthermore, the mean square measurement error of the SC algorithm was comparable to the other three methods used, with much faster processing time. The efficacy of the proposed technique was successfully demonstrated in both human brain MRS data and in a noisy MRS dataset acquired from a small volume-of-interest in the mouse brain. The study demonstrated the availability of a fast and robust technique that accurately corrects for both small and large frequency and phase shifts in MRS.

Coarse frequency offset estimation and compensation based on short-time spectrum analysis in FSO communication system

Due to the high-speed movement of low-orbit satellites, the Doppler frequency offset in 1550-nm wavelength satellite-to-ground coherent laser communication is as high as ±4.5 GHz, which greatly increases the difficulty of carrier frequency acquisition. Low complexity, short capture time, and stable coarse frequency offset estimation and compensation algorithms are technical bottlenecks that must be addressed. In this work, a coarse frequency offset estimation and compensation algorithm based on short-time spectrum analysis is proposed. In the proof-of-principle system, the feasibility of the coarse frequency offset capture is verified based on a 2.5-GBaud data rate PM-QPSK modulation FPGA-based coherent receiver. The results show that the frequency offsets capture range is extended to ±4.5 GHz, covering the Doppler frequency offset range. Moreover, the standard deviation of the residual frequency offset after coarse frequency offset compensation is less than 140 MHz, which is smaller than the accurate frequency offset compensation range of 312.5 MHz.

Dual-chirp-based photonic THz-ISAC system with adaptive frequency synchronization.

Recent advancements have brought significant attention to photonic terahertz (THz)-integrated sensing and communication (ISAC) systems. In this work, we present an adaptive frequency offset (FO) compensation method for dual-chirp-based ISAC waveforms, using the fractional Fourier transform (FrFT) method. The proposed scheme can enable frequency synchronization without a need for training preambles and exhibit robustness against system noise. We validate this approach through an experimental demonstration in a 300 GHz photonic THz-ISAC system with 20 Gbps quadrature-phase shift keying (QPSK) data transmission and 1.5 cm range resolution. The experiment successfully compensates for frequency offsets ranging from -5 to 5 GHz, achieving an estimation error of less than 0.08% and a chirp-pilot power overhead of 0.5%.

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI.

One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition.

Characterization and Modelling of Carrier Frequency Offset in OFDM Systems under Additive White Gaussian Noise

Characterization and Modelling of Carrier Frequency Offset in OFDM Systems under Additive White Gaussian Noise

Exploiting Carrier Frequency Offset and Phase Noise for Physical Layer Authentication in UAV-Aided Communication Systems

Exploiting Carrier Frequency Offset and Phase Noise for Physical Layer Authentication in UAV-Aided Communication Systems

Transmit beampattern design for FDA-MIMO radar using complex manifold optimization

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can produce time-stationary range-angle dependent beampattern. The problem with these beampatterns is the repeating lobes in the angle-range domain. Different methods are proposed to obtain better beampattern in terms of grating lobe and sidelobe suppression by using frequency offsets. However, these methods mostly choose the frequency offsets using an analytic expression and do not offer a control on the resulting range beampattern. In this paper, a new method for transmit beampattern design in range is presented. This method transfers the design problem into a tangential space and solves the problem using complex manifolds through an optimization procedure. The proposed approach considers amplitude tapering in the transmitter antenna elements and the practical instrumented range of the radar. Range beampattern constraints can be defined conveniently leading to an effective design process. Several simulations are done to show that the proposed method can significantly improve the beamwidth and sidelobe level in range patterns.

On the performance of OFDM-IM systems in the presence of CFO effects

This study presents a comprehensive analysis of the performance degradation effects of carrier frequency offset (CFO) on orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems operating over frequency-selective multipath fading channels. CFO is an impairing factor that degrades the signal-to-noise ratio (SNR) through signal attenuation and inter-carrier interference (ICI). We derive a closed-form expression to quantify the SNR degradation under CFO for OFDM-IM systems. Additionally, we formulate a very tight upper bound for the bit error rate (BER), accounting for index modulation errors, CFO distortion, and multipath fading.The presented analytical formulations capture the unique characteristics of OFDM-IM systems and facilitate precise performance evaluation. The findings yield valuable insights into mitigating CFO-induced BER degradation through appropriate system parameter selection and CFO compensation techniques. Moreover, this investigation makes significant contributions towards designing reliable OFDM-IM communication links resilient to the combined effects of index modulation, frequency offsets, and dispersive channel conditions.

Spectral analysis based blind frequency offset estimation for digital subcarrier multiplexing systems with adjustable roll-off factors and various modulation formats.

We proposed a blind frequency offset estimation (FOE) method for digital subcarrier multiplexing (DSM) signals. By utilising spectral information from the DSM signal and analysing the correlation between the frequency offset (FO) and the summed power of the signal spectrum, the proposed FOE method can accurately and effectively handle various modulation schemes assigned to each subcarrier. The proposed FOE method exhibits a flexibility to the roll-off factor of the root raised cosine (RRC) spectral shaping and can achieve a high level of accuracy in FOE under different roll-off factors and subcarrier numbers, with an FOE error of less than 30 MHz under a fast Fourier transform (FFT) size of 2048. Additionally, the proposed FOE method has advantages in terms of the computational complexity compared to the existing method. The performance of the proposed FOE method is experimentally verified for 48Gbaud 16QAM DSM signals in a coherent detection system.

IQ-channel multiplexed DP-4ASK signal using power loading for downlink coherent PON system with access-span length difference

This study explores the integration of dual-polarized (DP) four-level amplitude shift keying (4ASK) signals within an intensity and quadrature division multiplexing framework, mainly focusing on coherent passive optical network (PON) systems. The primary objective is to address the challenges associated with distance disparities in downstream transmissions, which are prevalent in coherent PON architectures. We introduce a novel approach that significantly reduces the power consumption of digital-to-analog converters by implementing power loading strategies tailored explicitly for IQ channel division multiplexing (IQDM)-DP-4ASK signals. Our simulations confirm that ASK standard frequency offset compensation is adaptable to these signals, providing a robust method for managing frequency offsets in a dynamic network environment. Moreover, we propose and empirically validate an energy-efficient adaptive downlink solution capable of supporting 128 Gb/s per optical network unit (ONU), utilizing IQ-imbalanced DP-4ASK signals. This solution is particularly advantageous for systems where the transmission conditions include a 10 km variance in access span lengths between ONUs. Results from our experimental setup demonstrate that reducing the digital-to-analog converter output current for the Q channel by 137 mA is feasible without compromising signal integrity or system performance. Additionally, the research verifies the effectiveness of the automatic bias controller on the transmitter side, ensuring stable operation across varying conditions. Equally, the receiver-side digital signal processing effectively handles polarization separation, illustrating the system's capability to maintain high data integrity and reliability under diverse operational scenarios. These findings highlight the technical feasibility and operational benefits of using IQDM-DP-4ASK signals in coherent PON systems and underscore the potential for significant power savings and enhanced system efficiency in optical networks. The study paves the way for future research into scalable, energy-efficient solutions suitable for high-capacity optical access networks striving for increased sustainability and reduced operational costs.

Enhanced frame synchronization and carrier recovery in coherent FSO communication: a pseudo-random and cyclic QPSK approach.

To alleviate the impact of atmospheric turbulence on receiver carrier recovery in free-space optical communication, this study introduces a novel frame synchronization and carrier recovery method. This method employs a blend of pseudorandom sequences and specific cyclic quadrature phase shift keying (QPSK) training sequences, facilitating frame synchronization and frequency offset (FO) estimation. The research designs a time-series metric curve to counteract the effect of side peaks, enhancing the accuracy of frequency offset estimation via QPSK spectrum features. Furthermore, the method incorporates a two-stage frequency offset estimation process, utilizing the inherent sign characteristics of the x and y polarization states. Applied to the quadrature amplitude modulation system under atmospheric turbulence conditions, the proposed algorithm demonstrates high precision under both strong and weak turbulence conditions. Furthermore, we confirmed the universality of the algorithm across multiple modulation formats.

An Analysis of Radio Frequency Transfer Learning Behavior

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.