Carrier Frequency Offset Research Articles

Impact of hardware impairments and BS antenna tilt on 3D drone localization and tracking

Today, GPS-free drone localization is increasingly gaining attention in various applications, but it faces significant accuracy challenges in three-dimensional (3D) space due to various impairments. This study investigates the effects of carrier frequency offset (CFO), phase noise (PN), and down-tilted base station (BS) antennas on drone positioning and tracking. Additionally, we explore the impact of inter-site distance (ISD) and BS density on drone position estimation accuracy. In our methodology, we consider a flying drone equipped with a single transmission antenna and BSs configured with 4 × 4 antennas under specific impairments. We first analyze the effects of these impairments on the signal’s covariance matrix. Then, using the MUSIC algorithm, we estimate the azimuth and elevation angles, which serve as the basis for drone localization using the Least Squares (LS) method across all BSs. Finally, the estimated positions feed into an Extended Kalman Filter (EKF) for tracking. Our results present a sequential analysis of the impact of all impairments on the off-diagonal covariance matrix, on the Angle of Arrival (AOA) estimation and 3D drone localization. We use simulations to demonstrate how hardware impairments affect 3D drone localization accuracy under varying ISD and BS densities.

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. Results: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.

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.

Virtual Array-Based Signal Detection and Carrier Frequency Offset Estimation in a Multistatic Collaborative Passive Detection System

To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as 1 × 10−4, with a false detection probability of 1 × 10−3, can be obtained under a signal-to-interference ratio (SIR) of −10 dB and Eb/N0 of 1 dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than 3%, with the condition of Eb/N0 being as low as −5 dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios.

Non-pilot based carrier frequency offset estimation and reflection coefficient optimization for RIS-assisted OFDM systems

Carrier frequency synchronization between the transmitter and receiver is challenging due to oscillator inaccuracies and Doppler shifts, which lead to inter-carrier interference. To address this synchronization error, i.e., carrier frequency offset (CFO), we introduce a novel method for estimating the CFO in reconfigurable intelligent surfaces (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems. Additionally, we present a method to optimize reflection coefficients, as adjusting the reflection coefficient of RIS can effectively manipulate signal propagation and amplitude, thereby enhancing system performance. In the proposed method, a randomized set of reflection coefficients is applied for the first OFDM symbol, and CFO is estimated using the oversampling method within that symbol over the Rayleigh fading environment. A deterministic approach is adopted to reduce grid search complexity. Simulation results demonstrate the superior performance of the proposed CFO estimation method, particularly in high signal-to-noise ratio scenarios, compared to the existing Zadoff Chu sequence-based estimator in terms of mean square error (MSE). Subsequently, the CFO is compensated in the second OFDM symbol using the estimated CFO, and the reflection coefficient is optimized by identifying the channel tap associated with the maximum channel impulse gain technique. The optimized reflection coefficient enhances system performance in terms of bit error rate (BER) for RIS-assisted OFDM systems.

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

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.

A channel resilient RFF extraction scheme for cyclic prefix contained systems

Radio frequency fingerprint (RFF) based identification technique has been proved efficient for ensuring the validity of devices connected to network. However, it is still a tough task to extract robust RFF in the scenarios with multi-path channel and moving terminals. To solve this problem, this paper proposes a channel-resilient RFF extraction scheme which can effectively reduce the influences from complex channel condition and retain robust device fingerprint. In the proposed system, blind synchronization and symbol-scale carrier frequency offset (CFO) estimation are designed for signal preprocessing for preparations of the following RFF extraction. A cyclic-prefix based de-channel algorithm (CPDCA) which can effectively weaken channel interference is proposed to meet the channel robustness of our system. Additionally, symbol-scale feature stacking algorithm (SFSA) is applied for RFF denoising, which can further enhance the performance of proposed system. Experiments using practical dataset collected from Long Term Evolution (LTE)-V2X communication system has been carried out under different signal-to-noise ratio (SNR). The results demonstrate that the proposed scheme has the ability to extract channel-robust RFF and to achieve reliable classification performance under complex channel conditions.

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

OFDM Modulation Classification Using Cross-SKNet With Blind IQ Imbalance and Carrier Frequency Offset Compensation

OFDM Modulation Classification Using Cross-SKNet With Blind IQ Imbalance and Carrier Frequency Offset Compensation

An approach to detect identity spoofing in AIS messages

Automatic identification system (AIS) is a maritime communication system that uses a transceiver to automatically exchange navigational data. These data help navigation and allow to monitor the maritime traffic. However, this system can be hacked and malicious users can easily transmit false data to mislead the coastguards or vessels navigating around. While previous research has proposed methods to detect these falsifications, none of them suggest strategies that detect AIS identity spoofing combining the tracking of the ship position and AIS transceiver’s carrier frequency offset (CFO). The CFO, caused by the carrier frequencies mismatch between emitter and receiver and Doppler effect, is used as a radiometric signature to identify materially every transceiver independently of its transmitted identity. It can drift over time and this is why it is tracked thanks to a Kalman filter (KF). In addition, position is also considered to reduce the miss probability of spoofing detection. The KF is noise adaptive to be robust against various CFO drifts and noise levels of the environment. The strategy is tested on real AIS data and the results demonstrate its efficacy: false alarm and miss probabilities were respectively 1% and 1.7%. These results show the ability of the test to correctly detect identity spoofing and the interest of CFO as a radiometric signature. This signature, used for the first time in an AIS application, could be used with other signatures in a future work to improve identity spoofing detection. This is why we made open source in GitHub our algorithm and the real AIS data used.

Frequency Offset Estimation for UWB-OFDM System using Weight and Exhaustiveness based Monarch Butterfly Optimization Approach

The performance of an OFDM system has limitation because the carrier frequency offset (CFO) and sampling frequency offset (SFO) is not estimated correctly. Current methods propose the way of obtaining frequency estimation by using the evolutionary algorithm, but the evolution algorithm suffers from the premature convergence problem. In order to provide efficient frequency offset estimation, this paper proposed weight and exhaustiveness based monarch butterfly optimization approach. First, on the transmitter block, for modulating an input symbol, the Hybrid-Quadrature Phase-Shift Keying modulation scheme is used. Brunn’s IFFT is done at each transmitter for symbol mapping and then in each transmitter block cyclic prefix is inserted. After that, WE-MBO algorithm estimates the frequency offset. On receiver side, the inverse operation of the transmitter is performed. After comparison of proposed system with the existing system, the proposed system gave better performance.

Non pilot data-aided carrier and sampling frequency offsets estimation in fast time-varying channel

This paper considers the non pilot data-aided estimation of the carrier frequency offset (CFO) and sample frequency offset (SFO) of orthogonal frequency division multiplexing (OFDM) signals in fast time-varying channel. The main obstacle is the time-variant channel response, which deteriorates the estimation validity. A practical approach to mitigate this impact is to reduce the time consumption of one-shot estimation. In this way, we propose a method to reduce the time consumption to within one OFDM symbol duration. The maximum likelihood (ML) estimator is derived based on the observations of frequency domain constellations output of two FFTs on one symbol; its closed-form approximation is then derived to reduce the calculation burden. Remarkably, our method does not require any training symbol or pilot tone embedded in the signal spectrum, therefore achieves the highest spectral efficiency. Theoretical analysis and simulation results are employed to assess the performance of proposed method in comparison with existing alternatives.

Millimeter-wave over fiber integrated sensing and communication system using self-coherent OFDM

Orthogonal frequency-division multiplexing (OFDM) waveform is highly preferred as a dual-function candidate for integrated sensing and communication (ISAC) systems. However, the sensitivity to both carrier frequency offset (CFO) and phase noise greatly impedes its applications in millimeter-wave ISAC systems. Here, we propose and experimentally demonstrate a photonic millimeter-wave ISAC system employing the virtual-carrier-aided self-coherent OFDM technique, wherein a digitally-generated local oscillator is transmitted along with the OFDM signal. Then, a compact CFO-immune and phase noise-immune envelope detection method is implemented for down-converting millimeter-wave communication and radar echo signals. In experiments, a V-band ISAC system is successfully implemented with a simplified remote radio unit, using the remote photonic millimeter-wave heterodyning up-conversion for downlink and the envelope detection-assisted down-conversion for uplink (or radar echoes). In the converged transmission link with a 5-km fiber link and 2-m space link, the Kramers–Kronig (KK) receiver supports a communication data rate up to 16-Gbit/s by mitigating signal-signal beat interference (SSBI). More significantly, the SSBI leads to negligible effects on the sensing performance when classic matched filtering is adopted for target identification. Consequently, a 4.8-cm range resolution and a 4-mm range accuracy are obtained for the radar sensing function.

Two-way ranging with carrier frequency and phase offset compensation based on software defined radio

In the context of Two-Way Ranging (TWR)measurements, distinct transmitter and receiver devices lead to discrepancies in clock timing, resulting in a Carrier Frequency Offset (CFO) and Carrier Phase Offset (CPO) that diminish the accuracy of distance measurements. Furthermore, moving objects also exacerbate these issues, increasing measurement errors. The effect of the CFO leads to variations and bit value inversion in the code in one frame when measuring two-way ranging distances with N random number sequence. In order to resolve this issue, the Fast Fourier Transform (FFT) is used in this work to determine the CFO and peak cross-correlation for the searching CPO. In outdoor experiments using Software Defined Radio (SDR) conducted over a maximum distance of 80 m, it was found that the precision of measurements was accurate within 1.25 m, and the overall accuracy was within 0.82 m when the Signal-to-Noise Ratio (SNR) was greater than 5 dB.

A Model-Driven Deep Learning-Based Receiver for OFDM System With Carrier Frequency Offset

A Model-Driven Deep Learning-Based Receiver for OFDM System With Carrier Frequency Offset

Beampattern analysis of cooperative beamforming with carrier frequency offsets in three-dimensional wireless networks

Cooperative beamforming (CB), where spatially distributed nodes synchronize their transmit phases to maximize the power of their combined signal at a desired receiver, can be used as an effective solution for hardware-limited nodes to achieve communication range extension. Thus, CB demonstrates effectiveness in providing higher reliability, lower latency, and extended transmission range for nodes in non-terrestrial networks (NTNs), which inherently face power and hardware limitations. In this paper, we consider the CB technique using UAVs in three-dimensional (3D) networks and analyze the average beampattern of the virtual antenna array constructed by the multiple UAVs. Further, because the mobile nature of the UAVs may cause carrier frequency offsets (CFOs), we analyze the impact of the CFO using the non-parametric kernel method. The simulation and analytical results show that the peak average beampattern degrades by about 3 dB with the CFO standard deviation of 1 kHz, which emphasizes the significance of frequency synchronization.

A CFO-Assisted Algorithm for Wireless Time-Difference-of-Arrival Localization Networks: Analytical Study and Experimental Results

Localization of wireless transmitters is traditionally done using Radio Frequency (RF) sensors that measure the propagation delays between the transmitter and a set of anchor receivers. One of the major challenges of wireless localization systems is the need for anchor nodes to be time-synchronized to achieve accurate localization of a target node. Using a reference transmitter is an efficient way to synchronize the anchor nodes Over-The-Air (OTA), but such algorithms require multiple periodic messages to achieve tight synchronization. In this paper, we propose a new synchronization method that only requires a single message from a reference transmitter. The main idea is to use the Carrier Frequency Offset (CFO) from the reference node, alongside the Time of Arrival (ToA) of the reference node messages, to achieve tight synchronization. The ToA allows the anchor nodes to compensate for their absolute time offset, and the CFO allows the anchor nodes to compensate for their local oscillator drift. Additionally, using the CFO of the messages sent by the reference nodes and the target nodes also allow us to estimate the speed of the targets. The error of the proposed algorithm is derived analytically and is validated through controlled laboratory experiments. Finally, the algorithm is validated by realistic outdoor vehicular measurements with a software-defined radio testbed.