Carrier Frequency Offset Estimation Research Articles

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.

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.

PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.

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.

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.

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.

Frequency offset estimation for nonlinear frequency division multiplexing with continuous spectrum modulation.

Carrier frequency offset (CFO) estimation is very important for the optical fiber communications and has been studied widely in linear coherent systems, while only a few works have been reported for nonlinear Fourier transform (NFT) based systems. In continuous spectrum (CS) modulation nonlinear frequency division multiplexing (CS-NFDM) systems, frequency offset (FO) has a great influence on its performance, requiring an improved frequency offset estimation (FOE) method. We found that the oversampling rate R0 adopted in NFDM to ensure the accuracy of the NFT and inverse NFT (INFT) calculations, would cause the estimation accuracy of the traditional FFT-FOE method to decrease by R0 times. Moreover, CS-NFDM signals with higher baud rate require more subcarriers and then result in an oversampling factor greater than 16. This makes the traditional FFT-FOE method be ineffective to use the common training sequence (TS) overhead to meet the FOE error requirement of CS-NFDM system. Therefore, a modified FOE method based on FFT assisted by TS and autocorrelation has been proposed. The theoretical analysis and simulation results show that the proposed method is applicable to CS-NFDM system, no matter what modulation format is used. For 512 subcarriers, with a high rate of 70GBaud and the TS length of 8192, the proposed method can obtain a minimum FO estimation error about 0.1 MHz, which is better than the other two typical FFT-FOE and Schmidl & Cox methods. In addition, the proposed method can save at least 87.5% and 50% overhead. Thus, the proposed method has obvious improvement for CS-NFDM system with requiring high oversampling rate.

Joint CFO and Channel Estimation for RIS-Aided Multi-User Massive MIMO Systems

Accurate channel estimation is essential to achieve the performance gains promised by the use of reconfigurable intelligent surfaces (RISs) in wireless communications. In the uplink of multi-user orthogonal frequency division multiple access (OFDMA) systems, synchronization errors such as carrier frequency offsets (CFOs) can significantly degrade the channel estimation performance. This becomes more critical in RIS-aided communications, as the RIS phases are adjusted based on the channel estimates and even a small channel estimation error leads to a significant performance loss. Motivated by this, we propose a joint CFO and channel estimation method for RIS-aided multi-user massive multiple-input multiple-output (MIMO) systems. Our proposed pilot structure makes it possible to accurately estimate the CFOs without multi-user interference (MUI), using the same pilot resources for both CFO estimation and channel estimation. For optimization of the RIS phase shifts at the data transmission stage, we propose a projected gradient method (PGM) which achieves the same performance as a more computationally demanding grid search technique while having a significantly lower computational complexity. Simulation results demonstrate that the proposed method provides an improvement in the normalized mean-square error (NMSE) of channel es-timation as well as in the bit error rate (BER) performance. Moreover, analyzing the computational complexity and the pilot resource efficiency of the proposed method, we demonstrate that the proposed estimation approach requires low computational load and no extra cost in pilot overhead.

An improved pilot pattern design-based channel estimation in wireless communication using distribution ant colony optimization

Multiple-Input Multiple-Output (MIMO) communication System-Orthogonal Frequency Division Multiplexing (OFDM) approach can produce reliable transmission of high data rates over broadband wireless medium. This approach makes use of the available spectrum and proves to be a potential candidate for meeting the high demand for data rate. Therefore, channel estimation is a crucial component in MIMOOFDM systems. There exist several issues in channel estimation like Carrier Frequency Offset (CFO) estimation, power allocation and orthogonal pilot optimization. However, computing an optimal pilot pattern is the complex problem. It severely reduces channel estimation accuracy. To overcome this issue, the proposed work focuses on the best possible design for the pilot pattern to get reliable channel estimation. In this proposed work, two pilot pattern design approaches like Carrier Frequency Offset (CFO) estimation, power allocation and orthogonal pilot optimization are combined to provide orthogonal pilot patterns. Creating orthogonal pilot patterns based on Generalized Shift Invariance Property (GSIP) for a MIMOOFDM has been proposed using two pilot pattern design approaches based on Improved Pilot Design with GSIP (IPDGSIP) and Tradeoff Pilot Design with GSIP (TPDGSIP). In PDGSIP, pilot patterns with high mutual coherence are initially obtained, then updated with the best pilot patterns. Here the optimal pilot patterns are generated by using Distribution-based Ant Colony Optimization (DACO) algorithm. Once a pilot pattern is established, TPDGSIP immediately generates new pilot patterns based on GSIP to utilize the pilot distance of the obtained pilot pattern fully. Results from MATLAB simulation demonstrate that the suggested technique performs better in channel estimation in terms of Bit Error Rate (BER), Normalized Mean Square Error (NMSE) and Symbol Error Rate (SER). The proposed PDGSIP-DACO system reduces the NMSE (6%), BER (8%) and SER (20%) when compared to the PDGSIP method.

Adaptive Carrier Frequency Offset Estimation in Interference Environments for OFDMA Uplink Systems

In orthogonal frequency division multiple access (OFDMA) systems, carrier frequency offsets (CFOs) influence the different users. In this paper, we proposed a CFO estimation algorithm with strong interference resistant capability for OFDMA systems. The first part of the proposed algorithm processes the received signals so as to deal with the CFO estimation problem. And the second part of the proposed algorithm is to use the proposed adaptive process to obtain the updated CFO estimation and track the parameters' time‐variations. Simulations demonstrate the effectiveness of this method and show that its performance is to the Cramer‐Rao Bounds (CRB). © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Efficient Maximum Likelihood Algorithm for Estimating Carrier Frequency Offset of Generalized Frequency Division Multiplexing Systems

This study presents a computationally efficient maximum likelihood (ML) algorithm for estimating the carrier frequency offset (CFO) of generalized frequency division multiplexing systems. The proposed algorithm uses repetitive subsymbols and virtual carriers to estimate the fractional and integer CFOs, respectively. Through the use of repetitive subsymbols, this study first calculates the ML estimate of the fractional CFO in the time domain and then, accordingly, compensates for it from the received signal. The integer CFO can then be estimated through a virtual-carrier-mapping process in the frequency domain. In addition to improving performance in terms of estimation accuracy and computational complexity, the proposed non-data-aided algorithm is spectrally efficient relative to traditional algorithms.

Joint DoA and CFO Estimation Scheme With Received Beam Scanned Leaky Wave Antenna for Industrial Internet of Things (IIoT) Systems

Direction-of-arrival (DoA) estimation is essential in many industrial internet of things (IIoT) applications where IoT users may require a high-resolution estimation algorithm for features limited to positioning and device localization. The conventional DoA estimation is challenging as the number of antenna arrays increases leading to an increase in design complexity and cost in RF circuitry. As a result, the conventional DoA estimation algorithms may not be suited for IIoT applications. In this work, a joint DOA and carrier frequency offset (CFO) schemes is introduced for typical uplink IIoT systems by equipping the receiver with a leaky-wave antenna (LWA). By considering that the LWA can simultaneously steer its beam direction towards the active user. As a result, each user is estimated independently. The joint algorithm is developed based on the high-resolution MUSIC scheme, which utilizes the LWA properties to compute the DoA spectrum. Furthermore, we develop an iterative CFO estimation approach as the desired solution to a multi-dimensional search. Subsequently, we further formulate the Cramer–Rao lower bounds (CRLB) to serve as a performance benchmark for the accuracy of the proposed scheme. The SDR platform is provided to validate the proposed algorithm in real-time. Finally, the simulation results and the SDR experiments justify that the proposed scheme offers more robustness than conventional counterparts with linear array antenna, which achieves a root mean square error (RMSE) approach to the derived CLRB, especially at a medium signal-to-noise ratio (SNR) range which suited for IIoT applications.

Carrier FOE Scheme Based on FSTS in Spatial Diversity PM Coherent FSO Communication

The efficient and cost-effective suppression of atmospheric turbulence effects in free-space optical (FSO) communication poses a significant challenge. To address this challenge, a carrier frequency offset estimation (FOE) scheme based on frame-synchronous training sequence (FSTS) is proposed, which can be applied to spatial diversity polarization multiplexing (PM) coherent FSO communication system. The proposed scheme integrates the functions of frame synchronization (FS) and FOE by designing distinct training sequences for the X and Y polarizations. Specifically, the FOE scheme achieves high accuracy, wide range, low complexity, and multi-format versatility while maintaining FS accuracy. Simulation results for PM 4/16-quadrature amplitude modulation (QAM) demonstrate the feasibility and generality of the proposed scheme. Moreover, when combined with the spatial diversity reception under strong and weak turbulence conditions, the proposed algorithm exhibits higher receiver sensitivity compared to the conventional training sequence (TS)-based FOE algorithm. The advantages of this scheme make it highly promising for high-speed coherent FSO communications across multiple modulation formats.

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

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

Efficient Carrier Frequency Offset Estimation in Wireless Sensor Networks

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

Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks

Orthogonal Frequency-Division Multiplexing (OFDM) is a form of digital systems and a way of encoding digital data across multiple frequency components that is used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a serious disadvantage of OFDM. In this research, Fuzzy based Slime Mould optimization for CFO (FSM-CFO) has been proposed. The proposed FSM-CFO not only estimates the CFO with increased precision, but also allocates resources effectively, achieving maximum utilization of dynamic resources. Initially, Slime Mould Algorithm is utilized to extract the precise CFO. Additionally, the base station (BS) manages the Resource Units (RU), which could be used to distribute resources in such a manner that the user-requests are met. The resources are assigned using fuzzy rules, with the type of resource that is most appropriate for the nodes that have requested the resources being chosen. To assign resources to a certain job, fuzzy rules are devised. Once the residence time for the duration has passed, the highest-priority tasks receive the specific resources. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. The performance of the proposed method is evaluated in terms of Resource utilization, failure rate, life span, and energy consumption. According to the simulated results, the suggested configuration can perform better compared to the existing techniques.

Performance analysis of ICI self-cancellation schemes in OFDM systems

Abstract Orthogonal frequency division multiplexing (OFDM) is a promising technique in modern high data rate wireless communication system. However, the serious problem in OFDM system is the inter-carrier-interference (ICI) that is produced by the carrier frequency offset (CFO). CFO destroys the orthogonal properties among the sub-carriers and introduces ICI. ICI is a major impairment in OFDM system as it can degrade the bit error rate (BER) performance of the system. In this paper we have proposed an optimal ICI self-cancellation scheme that can maximize the carrier-to-interference-ratio (CIR) of the system. This scheme is based on optimizing the data allocation and combining parameters where, for estimated CFO, the optimized values of these parameters are calculated using Nelder Mead simplex algorithm. An approach to calculate the sub-optimized values of data allocation and combining parameters is also proposed, where the knowledge of CFO estimation can be avoided. In this paper, we have carried out the complete performance analysis of ICI self-cancellation schemes. We have also derived the mathematical expressions for ICI coefficients and presented a comparative analysis of standard OFDM system with different ICI cancellation schemes. The mathematical expressions for CIR and BER performance are further derived. To confirm the validity of the results Monte Carlo simulations using Matlab are also performed. We have also done the comparison of peak-to-average power ratio (PAPR) of conventional ICI self-cancellation scheme and proposed scheme.

A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication

Wireless communication at sea is an essential way to establish a smart ocean. In the communication system, however, signals are affected by the carrier frequency offset (CFO), which results from the Doppler effect and crystal frequency offset. The offset deteriorates the demodulation performance of the communication system. The conventional Gardner bit-synchronization algorithm performs timing synchronization on the baseband, but it fails to solve the problem of carrier frequency offset. In this paper, a carrier-based timing-synchronization Gardner algorithm was proposed. The algorithm performed error detection in the carrier signal to estimate the synchronization error in real time and the time-domain expansion in the passband signal. Based on the estimated expansion factor, the algorithm located the sampling points of the ideal signal and re-interpolated all the sampling points on the passband to recover the passband signal without Doppler. This algorithm can solve both the frequency shift and time-domain expansion caused by the Doppler effect without further CFO estimation and compensation, thus reducing the overall computational complexity of the system. The simulations showed that the proposed algorithm greatly improves the ability of the communication system to combat Doppler compared to the conventional Gardner algorithm. The algorithm is primarily designed for binary phase-shift keying (BPSK)-modulated signals under the additive white Gaussian noise (AWGN) channel and can be applied to various maritime communication scenarios.