Carrier Frequency Offset Research Articles

An Integrated Orthogonal Frequency-Division Multiplexing Chirp Waveform Processing Method for Joint Radar and Communication Based on Low-Density Parity-Check Coding and Channel Estimation

With the advancement of information technology construction, the integration of radar and communication represents a crucial technological evolution. Driven by the research boom of integrated sensing and communications (ISACs), some scholars have proposed utilizing orthogonal frequency-division multiplexing (OFDM) to separately modulate radar and communication signals. However, the OFDM symbols in this paper incorporate a cyclic prefix (CP) and a virtual carrier (VC) instead of zero padding (ZP). This approach mitigates out-of-band power caused by ZP, in addition to reducing adjacent channel interference (ACI). In addition, we introduce low-density parity-check (LDPC) and use an improved normalized min-sum algorithm (NMSA) in decoding. The enhanced decoding efficiency and minimized system errors render the proposed waveform more suitable for complex environments. In terms of signal processing methods, this paper continues to use radar signals as a priori information to participate in channel estimation. Further, we consider the symbol timing offset (STO) and carrier frequency offset (CFO) issues. In order to obtain more reliable data, we use the minimum mean-square error (MMSE) estimation based on the discrete Fourier transform (DFT) to evaluate the channel. Simulation experiments verify that the system we propose not only realizes the transmission and detection functions but also improves the performance index of the integrated signal, such as the bit error rate (BER) of 7 × 10−5, the peak side lobe ratio (PSLR) of −13.81 dB, and the integrated side lobe ratio (ISLR) of −8.98 dB at a signal-to-noise ratio (SNR) of 10 dB.

RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.

Blind CFO estimation based on weighted subspace fitting criterion with fuzzy adaptive gravitational search algorithm

AbstractThis paper deals with the blind carrier frequency offset (CFO) estimation based on weighted subspace fitting (WSF) criterion with fuzzy adaptive gravitational search algorithm (GSA) for the interleaved orthogonal frequency-division multiplexing access (OFDMA) uplink system. For the CFO estimation problem, it is well known that the WSF has superior statistical characteristics and better estimation performance. However, the type of CFO estimation must pass through the high-dimensional space problem. Optimizing complex nonlinear multimodal functions requires a large computational load, which is difficult and not easy to maximize or minimize nonlinear cost functions in large parameter spaces. This paper firstly presents swarm intelligence (SI) optimization algorithms such as GSA, particle swarm optimization (PSO), and hybrid PSO and GSA (PSOGSA) to improve estimation accuracy and reduce the computational load of search. At the same time, this paper also integrates a fuzzy inference system to WSF-GSA to dynamically adjust the gravitational constant, which can not only reduce the searching computational load, but also improve the performance of GSA in the global optimization and solution accuracy. Finally, several simulation results are provided for illustrating the effectiveness of the proposed estimator.

Attention Guided Multi-Task Network for Joint CFO and Channel Estimation in OFDM Systems

The existence of high carrier frequency offset (CFO) and fading channels degrades the performance of communication systems in high mobility environments significantly. To address these challenges, in this paper, we propose an attention guided multi-task network for joint CFO and channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Specifically, considering the correlation between CFO estimation and channel estimation, we construct a multi-task neural network framework which consists of a shared branch (SB), a CFO estimation branch (CFOEB) and a channel estimation branch (CEB). Among them, the SB extracts common features of two tasks, and the CFOEB and CEB make more detailed CFO and channel estimation, respectively. To further suppress the influence of CFO estimation error on channel estimation, we introduce an attention guided module (AGM) which completes the re-calibration of the channel dimension on the basis of different CFO characteristics. We train the whole network by an end-to-end paradigm and construct a suitable multi-task loss function to balance the losses between different tasks. Simulation results demonstrate that the proposed attention guided multi-task learning based joint CFO and channel estimation scheme outperforms the conventional methods and has greater robustness under various conditions.

A Novel Machine Learning Approach for Blind Carrier Offset Estimation in OFDM Systems

As wireless networks continue to evolve with the emergence of sixth-generation networks and beyond, OFDM technology is expected to be an essential system component for providing reliable connectivity in an increasingly complex environment. Its high spectrum efficiency, efficiency against impulse noise, and resilience against frequency-selective multipath fading channels make it an invaluable component. Although OFDM technology has various capabilities and uses and is widely used, it has some issues and challenges, and one of these challenges is Carrier Frequency Offset (CFO). The CFO destroys orthogonality between subcarriers and causes subcarrier misalignment at the receiver, resulting in Inter- Carrier Interference (ICI) in OFDM systems. This interference impairs system performance by introducing errors and degrading signal-to-noise ratios (SNR). In this paper, a novel approach to estimate the CFO in OFDM systems via Feedforward Neural Network (FNN) has been proposed. The architecture is precisely designed for regression tasks, with a focus on accurately estimating the continuous parameter φ based on the input features. The multiple hidden layers facilitate the extraction of intricate patterns, while careful training options ensure efficient optimization and monitoring of the training process. The performance of the proposed FNN algorithm is assessed and compared with other subspace-based methods, ESPRIT, PM(MUSIC), and QR. Remarkably, the results demonstrate that the FNN algorithm consistently outperforms all subspace methods, especially in low SNR scenarios.

ИСПОЛЬЗОВАНИЕ КУМУЛЯНТНОГО АНАЛИЗА ДЛЯ РАСПОЗНАВАНИЯ QAM-МОДУЛЯЦИИ В УСЛОВИЯХ АПРИОРНОЙ НЕОПРЕДЕЛЕННОСТИ

The exponential growth in the volume of transmitted data necessitates the development of efficient methods for signal processing and analysis, particularly under conditions of a priori uncertainty. This paper presents a novel method for estimating carrier frequency and initial phase offsets in quadrature amplitude modulation (QAM) signals based on cumulant analysis. The method leverages the properties of fourth-order cumulants, which are invariant to phase shifts. An algorithm is developed that enables accurate determination of carrier frequency and initial phase values for QAM signals in the absence of a priori information about signal parameters. Experimental results demonstrate the high accuracy and efficiency of the proposed method in radio monitoring and radio frequency spectrum analysis applications.

Real-Time Wide-Range Carrier Frequency Offset Compensation by Multi-Thread PADE for Fully Softwarized Access Networks

Real-Time Wide-Range Carrier Frequency Offset Compensation by Multi-Thread PADE for Fully Softwarized Access Networks

Estimation of Carrier Frequency Offset Plagued by IQ Mismatch Using Least-Squares Interpolation of the DFT Coefficients

Estimation of Carrier Frequency Offset Plagued by IQ Mismatch Using Least-Squares Interpolation of the DFT Coefficients

Filter design and power allocation in multi-user universal filtered multi-carrier (MU-UFMC) systems

Universal Filtered Multi-Carrier (UFMC) system has been introduced as a candidate for the new generation. UFMC is a multi-carrier system, which can overcome challenges in wireless communications. In fact, the advantages of multi-carrier systems over single-carrier systems include higher bandwidth gain and higher robustness against frequency-selective fading. Generally, most multi-carrier systems suffer from notoriously sensitive to symbol-time offset (STO) and carrier-frequency offset (CFO). However, since UFMC systems utilize subband filters, sensitivity to CFO and STO can be reduced by designing efficient subband filters. In this paper, filter design problem is addressed in order to mitigate interference caused by effect of CFO in a multi-user UFMC (MU-UFMC) system under fair optimization framework. Since filter design problem is formulated as a non-convex optimization problem, we present two different approach in order to solve it. In the first approach, the formulated problem is relaxed so that one is converted to a standard second-order-cone programming (SOCP). In the second approach, we present a proposed algorithm with low complexity based on some lemma and relaxation. Then power allocation problem is solved to maximize the minimum achievable rate among all the users in a MU-UFMC system. The simulation results show the proposed schemes for filter design and power allocation outperform the benchmark schemes.

TEA-RFFI: Temperature adjusted radio frequency fingerprint-based smartphone identification

Recently, radio frequency fingerprints (RFFs) have been widely applied for smartphone identification, since RFFs are distinguishable and hard to imitate. Compared with other types of RFFs, carrier frequency offset (CFO) in Wi-Fi signals is more robust and practical. However, when many smartphones need to be identified, the probability of CFO collision is high due to the weak distinguishability of CFO, and thus the primitive CFO-based identification solution will perform poorly. Fortunately, we find that the CFO varies with crystal oscillator temperature. Inspired by the phenomenon, we can actively adjust the crystal oscillator temperature to increase the difference between Wi-Fi device fingerprints for CFO collision mitigation. Firstly, we propose a non-intrusive temperature sensing and adjustment solution, which can obtain crystal oscillator temperature accurately and actively adjust its temperature to specified values without any additional hardware. Then, we investigate the temperature selection problem which aims to maximize overall differences among all smartphones, and propose the corresponding algorithm that combines greedy strategy and simulated annealing to assign a proper temperature value to each smartphone for identification. Finally, we implement the TEA-RFFI system and conduct several sets of experiments under the cases of different positions, time periods and scenarios. Experimental results demonstrate that TEA-RFFI can effectively identify 20 smartphones with over 90% precision, recall and F1-score. Even when the smartphones are moving, our proposed system still can identify them with over 89% precision, recall and F1-score.

Understanding Concurrent Transmissions: The Impact of Carrier Frequency Offset and RF Interference on Physical Layer Performance

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This article fills this gap and provides an extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15.4 and BLE 5. We first highlight through simulation how the impact of errors induced by relative carrier frequency offsets on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware and with varying environmental conditions through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based data collection and dissemination protocols in the presence of RF interference on a large-scale testbed, deriving insights on how the employed physical layer affects their dependability.

Design and evaluation of space‐time coded multiple input multiple output‐orthogonal frequency division multiplexing physical layer

SummaryTwo fundamental aspects of wireless communication, which make it challenging and interesting, are fading and interference. Multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques when utilized together effectively combat fading and interference thereby achieving the requirement of high data rate and high performance. In this paper, a 2 × 1 MIMO‐OFDM baseband model of the physical layer according to IEEE 802.16.1 standard employing 256 subcarriers capable of transmitting 192 Mbps in a 21‐MHz bandwidth frequency selective fading channel is designed. All essential blocks of the communication system like channel coding, modulation, time synchronization, frequency synchronization, channel estimation, residual carrier frequency offset (CFO) estimation, and channel decoding are covered at the algorithm level. A two‐step time synchronization method is used in this work to avoid any ambiguity in frame detection. A long preamble, consisting of three short preambles, is designed to aid in time synchronization, frequency synchronization, and channel estimation. To evaluate the performance of the proposed design, simulations were carried out in MATLAB‐SIMULINK.

Timing‐deviation and frequency‐offset estimations for multicarrier transmission in high mobility environments using deep neural network

SummaryThe multicarrier technology used in fourth‐generation (4G) and fifth‐generation (5G) communications is quite susceptible to multipath fading and Doppler frequency shift, which is more prominent in high mobility environments. Therefore, the accuracy of timing and frequency synchronization impacts the overall system performance significantly. Existing synchronization methods rely on the correlation of the preamble sequences and are, hence, vulnerable to severe multipath effect and Doppler effect. In this article, we propose a novel scheme leveraging deep neural network (DNN) to achieve high‐precision synchronization in high mobility environments. Concretely, a convolutional neural network (CNN) architecture is proposed to extract the hidden features of received signals for timing deviation estimation. In the following, a fully connected (FC) model is demonstrated to classify the optimal carrier frequency offset (CFO) estimate from several CFO candidates. The proposed synchronization scheme is assessed under extended vehicular A (EVA) channel with various Doppler frequency shifts. Simulation results corroborate that the proposed DNN‐based scheme achieves a significant performance gain over the conventional correlation methods, and the proposed timing deviation estimation scheme exhibits an excellent complexity reduction; wherefore, it is extremely promising for multicarrier transmission systems in high mobility environments.

Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.

Non-cooperative underwater acoustic MFSK time-frequency representation optimization method based on sparse reconstruction

The performance of the traditional non-cooperative underwater acoustic (UWA) multiple frequency shift key (MFSK) recognition and parameter estimation method based on time-frequency representation (TFR) is affected by TFR energy divergence. Herein, a sparse TFR estimation model for UWA MFSK is presented. The model exploits the properties of MFSK TFR element sparsity and row sparsity, which can effectively reduce the impact of the channel and does not require any a priori information. To achieve better TFR performance, we propose an inversion fitting method that can effectively improve the frequency resolution, while keeping the time resolution unchanged. Simulation and experimental results show that the Peak Signal to Noise Ratio (PSNR) of the proposed method reaches 21dB with the increase of Signal to Noise Ratio (SNR), indicating that the proposed method has high carrier frequency reconstruction and noise suppression performance. The proposed method can continuously reduce the value of Average Carrier Frequency Offset (ACFO) under inversion fitting method, indicating that inversion fitting method can further reduce the energy divergence and improve frequency resolution.

Bayesian Learning-Assisted Joint Frequency Tracking and Channel Estimation for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is very sensitive to the carrier frequency offset (CFO). The CFO estimation precision heavily makes impacts on the OFDM performance. In this paper, a new Bayesian learning-assisted joint CFO tracking and channel impulse response estimation is proposed. The proposed algorithm is modified from a Bayesian learning-assisted estimation (BLAE) algorithm in the literature. The BLAE is expectation-maximization (EM)-based and displays the estimator mean square error (MSE) lower than the Cramer-Rao bound (CRB) when the CFO value is near zero. However, its MSE value may increase quickly as the CFO value goes away from zero. Hence, the CFO estimator of the BLAE is replaced to solve the problem.

Radio Frequency Fingerprint Identification With Hybrid Time-Varying Distortions

Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDs-robust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDs-robust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals. Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3%with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.

Robust Doppler compensation for low frequency underwater communications

Impairment caused by Doppler frequency shift and harsh channel condition distinguish underwater acoustic communications from radio communications. Slow speed of acoustic waves produces relatively high Doppler shift and result in expansion or compression in time samples, which is known as Doppler scaling. In particular, the Doppler scaling causes significant carrier frequency offset (CFO) in orthogonal frequency division modulation (OFDM). To compensate the CFO, it is essential to resample signal samples according to the Doppler scale. In theory, the Doppler scale can be estimated by measuring the distance between peaks of the matched filter output of consecutive Doppler-invariant preambles. In practice, multi-path reflections and non-orthogonality between the HFM and OFDM payload produce large numbers of local false peaks. In this paper, we propose a robust Doppler estimation technique by employing constant false alarm rate (CFAR) filter to remove false peaks. We validate the performance of the proposed scheme through two sea-trials carried out in shallow water near Jervis Bay, Australia. In the sea-trials, a slow-moving towed hydrophone array was deployed as the receiver and the source was a stationary, low source level, vertical transmit array. Sea-trial results demonstrated up to 112bps information rate using bit-interleaved coded OFDM over 400 Hz bandwidth.

Design of satellite mobile synchronization system based on OFDM code

In the 5G era, satellite communication has been widely applied in more scenarios. Orthogonal Frequency Division Multiplexing (OFDM) has been adopted to the 3GPP standard and is the primary sync encoding method for satellite communication. However, the OFDM-based system is extremely susceptible to carrier frequency offset (CFO), and Doppler shift make a major impact on the CFO. Therefore, the traditional methods of synchronization based on OFDM cannot meet the requirement of satellite communication with large frequency offset and delay. There are many studies focus on improving the methods of CFO estimation and compensation to adapt the technologies of synchronization based on OFDM to the satellite communication system. In this paper, ZC based and matrix-vector multiplication method, multi-symbol merging method and preamble and PSS symbols based and filter frequency locked loop-based method of synchronization of OFDM-based satellite communication will be introduced. The accuracy and complexity of these methods both meet the requirement of 5G satellite communication system. Future researchers may focus on reducing complexity and expanding application scenarios.

STUDY ON METHODS FOR ESTIMATING AND COMPENSATING FREQUENCY OFFSET IN 6G COMMUNICATION SYSTEMS WITH DFT-S-OFDM TECHNOLOGY UNDER CONDITIONS OF RICE AND RAYLEIGH PROPAGATION CHANNELS

The article investigates effectiveness of methods for estimating and compensating for carrier frequency offset (CFO) for signal processing technology based on orthogonal frequency distribution using discrete Fourier transform (DFT-s-OFDM) in terms of use of Rice and Rayleigh channels close to real propagation channels. Also, the possibility to increase the efficiency of estimating and compensating for frequency mismatch by introducing the Kronecker product operation into original algorithms of signal processing methods is considered. The article investigates the effectiveness of methods for estimating and compensating for carrier frequency offset (CFO) for signal processing technology based on orthogonal frequency distribution using discrete Fourier transform (DFT-s-OFDM) in conditions of using Rice and Rayleigh channels close to real propagation channels. Also, the possibility of increasing the efficiency of estimating and compensating for frequency mismatch by introducing the Kronecker product operation into the original algorithms of signal processing methods is considered. The effectiveness of using shorter guard intervals in noisy channels and high levels of frequency mismatch is confirmed.