Carrier Frequency Offset Estimation for OFDM Systems using Repetitive Patterns

This paper deals with Carrier Frequency Offset (CFO) estimation for OFDM systems using repetitive patterns in the training symbol. A theoretical comparison based on Cramer Rao Bounds (CRB) for two kinds of CFO estimation methods has been presented in this paper. Through the comparison, it is shown that the performance of CFO estimation can be improved by exploiting the repetition property and the exact training symbol rather than exploiting the repetition property only. The selection of Q (number of repetition patterns) is discussed for both situations as well. Moreover, for exploiting the repetition and the exact training symbol, a new numerical procedure for the Maximum-Likelihood (ML) estimation is designed in this paper to save computational complexity. Analysis and numerical result are also given, demonstrating the conclusions in this paper

Pilot-based carrier frequency offset estimation in OFDM systems

The ability of OFDM to mitigate channel frequency selectivity is impaired by its vulnerability to carrier frequency offset (CFO). In this paper, a new pilot-based carrier frequency offset estimation scheme for frequency selective slowly-varying channels is proposed. This scheme exploits the inherent redundancy introduced by the OFDM signaling and no additional training symbol is needed. The synchronization process is to minimize a LMS-like cost function, which essentially quantifies the variance of the pilot tones across several OFDM symbols. Moreover, a reduced-complexity estimator is also developed to reduce the implementation complexity. The CFO estimation range can span the whole OFDM signal bandwidth by nonuniformly distributing the embedded pilot tones in the frequency domain. Computer simulation results show that the performance of the proposed estimator can meet the requirement of typical OFDM communication systems.published_or_final_versio

Carrier Frequency Offset Estimation for Orthogonal Frequency Division Multiplexing

This thesis presents a novel method to solve the problem of estimating the carrier frequency set in an Orthogonal Frequency Division Multiplexing (OFDM) system. The approach is based on the minimization of the probability of symbol error. Hence, this approach is called the Minimum Symbol Error Rate (MSER) approach. An existing approach based on Maximum Likelihood (ML) is chosen to benchmark the performance of the MSER-based algorithm. The MSER approach is computationally intensive. The thesis evaluates the approximations that can be made to the MSER-based objective function to make the computation tractable. A modified gradient function based on the MSER objective is developed which provides better performance characteristics than the ML-based estimator. The estimates produced by the MSER approach exhibit lower Mean Squared Error compared to the ML benchmark. The performance of MSER-based estimator is simulated with Quaternary Phase Shift Keying (QPSK) symbols, but the algorithm presented is applicable to all complex symbol constellations

Timing Metrics of Joint Timing and Carrier-Frequency Offset Estimation Algorithms for TDD-based OFDM systems

In joint timing and carrier offset estimation algorithms for Time Division Duplexing (TDD) OFDM systems, different timing metrics are proposed to determine the beginning of a burst or symbol. In this contribution we investigated the different timing metrics in order to establish their impact on the joint time and carrier-frequency offset estimation

Carrier Frequency Offset Estimation in MIMO-OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a popular method for high data rate wireless transmission. The performance of OFDM system is very sensitive to carrier frequency Offset (CFO), which introduces inter-carrier interference(ICI). Multi-input multi-output system used for increasing diversity gain and capacity of the system. Alamouti space time block code is used for MIMO transmission scheme. The basic method is used for Carrier Frequency Offset Estimation in OFDM system. In cyclic prefix (CP) based estimation, the CFO can be found from the phase angle of the product of the CP and corresponding rear part of the OFDM symbol. In CFO estimation using a training symbol, the CFO estimation range can be increased by reducing the distance between two blocks of samples for correlation. This was made possible by using training symbol that is repetitive with shorter period. An analytic expression in the form of mean square error (MSE) of frequency offset synchronization is reported, and simulation results verify theoretical analysis. Block-by-block CFO estimation is used in MIMO-OFDM system. In this algorithm we use block delay with other block and compare with the delay block. Analysis of the optimal size of block and minimization of the MSE of frequency estimator. We use S observation symbols they are grouped into two consecutive blocks with length A and S-A. The observation symbol, in each block is added sequentially and summed results are correlated block-wise

Periodogram-based carrier frequency offset estimation for orthogonal frequency division multiplexing applications

In this paper, a novel carrier frequency offset (CFO) estimation algorithm is proposed for OFDM applications. The maximum likelihood estimator (MLE) for the CFO is derived, which reveals the relationship between the CFO and the periodogram of the received signal. Theoretical analysis shows that the proposed MLE is statistically efficient. To realize this MLE in practice, a sub-optimal estimator is also introduced in which zero-padded FFT is invoked for implementation. For the objectives of reducing the implementation complexity and broadening the estimation range, a preamble structure comprising nonuniformly-spaced pilot tones is presented. Based on this preamble, the CFO estimation is split into two phases: the coarse estimation is obtained through the correlation between the received spectrum and the original pattern of the preamble; whereas the fine estimation is obtained by comparing the relative magnitude attenuation in the vicinities of those CFO-shifted pilot tones. Both analytical investigations and computer simulations indicate that the accuracy of this simplified sub-optimal estimator is proportional to the oversize ratio of zero-padded FFT, and its estimation range is equal to the bandwidth of OFDM signal. When the oversize ratio is sufficiently high, the performance of the proposed sub-optimal estimator approaches that of the proposed MLE.published_or_final_versio

Channel estimation, data detection and carrier frequency offset estimation in OFDM systems

Orthogonal Frequency Division Multiplexing (OFDM) plays an important role in the implementation of high data rate communication. In this thesis, the problems of data detection and channel and carrier frequency offset estimation in OFDM systems are studied. Multi-symbol non-coherent data detection is studied which performs data detection by processing multiple symbols without the knowledge of the channel impulse response (CIR). For coherent data detection, the CIR needs to be estimated. Our objective in this thesis is to work on blind channel estimators which can extract the CIR using just one block of received OFDM data. A blind channel estimator for (Single Input Multi Output) SIMO OFDM systems is derived. The conditions under which the estimator is identifiable is studied and solutions to resolve the phase ambiguity of the proposed estimator are given.A channel estimator for superimposed OFDM systems is proposed and its CRB is derived. The idea of simultaneous transmission of pilot and data symbols on each subcarrier, the so called superimposed technique, introduces the efficient use of bandwidth in OFDM context. Pilot symbols can be added to data symbols to enable CIR estimation without sacrificing the data rate. Despite the many advantages of OFDM, it suffers from sensitivity to carrier frequency offset (CFO). CFO destroys the orthogonality between the subcarriers. Thus, it is necessary for the receiver to estimate and compensate for the frequency offset. Several high accuracy estimators are derived. These include CFO estimators, as well as a joint iterative channel/CFO estimator/data detector for superimposed OFDM. The objective is to achieve CFO estimation with using just one OFDM block of received data and without the knowledge of CIR

Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah

Wi-Fi systems based on the IEEE 802.11 standards are the most popular wireless interfaces that use Listen Before Talk (LBT) method for channel access. The distinctive feature of a majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to perform packet detection and carrier frequency offset (CFO) estimation. Preambles usually contain repetitions of training symbols with good correlation properties, while conventional digital receivers apply correlation-based methods for both packet detection and CFO estimation. However, in recent years, data-based machine learning methods are disrupting physical layer research. Promising results have been presented, in particular, in the domain of deep learning (DL)-based channel estimation. In this paper, we present a performance and complexity analysis of packet detection and CFO estimation using both the conventional and the DL-based approaches. The goal of the study is to investigate under which conditions the performance of the DL-based methods approach or even surpass the conventional methods, but also, under which conditions their performance is inferior. Focusing on the emerging IEEE 802.11ah standard, our investigation uses both the standard-based simulated environment, and a real-world testbed based on Software Defined Radios.Comment: 13 pages, journal publicatio

Exploiting hidden pilots for carrier frequency offset estimation for generalized MC-CDMA systems

This paper proposes a novel carrier frequency offset (CFO) estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi- lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator