Efficient Joint Estimation of Carrier Frequency and Sampling Frequency Offsets for MIMO-OFDM ATSC Systems

The estimation of carrier and sampling frequency offsets is considered, for IEEE 802.11a-based OFDM wireless LAN (WLAN) systems, based on the guard interval and two long symbols (GI2L) in the preamble of an OFDM WLAN packet. The model of the measurement of the GI2L is developed, in the presence of both carrier and sampling frequency offsets. Based on this model, a method is proposed, based on the GI2L. Improved performance, measured by estimation error and bit error rate, is achieved by the proposed method, as compared with some relevant existing methods.

A successive interference cancellation (SIC)-based weighted least-squares (WLS) estimation for the carrier frequency offset (CFO) and the sampling frequency offset (SFO) is presented for wireless local area networks (WLANs) based on orthogonal frequency division multiplexing (OFDM). The proposed SIC-based WLS performs the estimation by exploiting the phase rotation in the frequency domain caused by the CFO and the SFO. SIC-based WLS estimates the CFO and the SFO successively instead of by traditional simultaneous estimation. The estimation of CFO based on the Taylor series is performed first, and then the WLS estimation of SFO based on successive cancellation of the CFO is carried out. The simulation results show that the SIC-based WLS can estimate the CFO and the SFO effectively. Compared to the WLS scheme, a performance improvement of more than 0.6 dB is achieved by SIC-based WLS, and nearly 10 percent of the complexity is reduced.

Orthogonal frequency division multiplexing (OFDM) has been adopted in various wireless communication standards to exploit its high spectral efficiency and robustness against multipath fading channels. Along with the potential benefits of OFDM transmissions, a stringent requirement on orthogonality among subcarriers makes OFDM highly susceptible to synchronization errors, i.e., time and frequency offsets. Addressing the carrier frequency offset (CFO) estimation problem, the paper proposes a pilot-aided CFO estimation scheme for both coarse and fine carrier frequency synchronization in single-input single-output (SISO) OFDM receivers. By exploiting the maximum likelihood (ML) principle, the proposed ML-based CFO estimation algorithm functions properly over a wide range of CFO values and outperforms existing correlation-based schemes.

In this paper, we propose a new carrier frequency offset (CFO) estimation algorithm and a new CFO correction structure in the multi-stage synchronization strategy in time and frequency domain for OFDM. The new CFO estimation algorithm uses all pilots including continual pilots (CP) and scattered pilots (SP) to estimate the CFO in frequency domain, and it can acquire more accurate estimation results than the conventional algorithm only using CP. The new CFO correction structure is a double-loop structure, and in the double-loop synchronization structure, fractional frequency offset (FFO) is corrected using a long loop as conventional method, however, integral frequency offset (IFO) does not go back to the time domain, and it is recovered using a short loop in frequency domain, which can shorten the convergence time of frequency synchronization. The analysis and simulations show the improved performance of the proposed CFO estimation algorithm and the double-loop correction structure.

In this new information age, high data rate and strong reliability features our wireless communication systems and is becoming the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization. In the first part, this dissertation formulates the channel estimation problem for MIMO-OFDM systems and proposes a pilot-tone based estimation algorithm. A complex equivalent base-band MIMO-OFDM signal model is presented by matrix representation. By choosing equally-spaced and equally-powered pilot tones from sub-carriers in one OFDM symbol, a down-sampled version of the original signal model is obtained. Furthermore, this signal model is transformed into a linear form solvable for the LS (least-square) estimation algorithm. Based on the resultant model, a simple pilot-tone design is proposed in the form of a unitary matrix, whose rows stand for different pilot-tone sets in the frequency domain and whose columns represent distinct transmit antennas in the spatial domain. From the analysis and synthesis of the pilot-tone design in this dissertation, our estimation algorithm can reduce the computational complexity inherited in MIMO systems by the fact that the pilot-tone matrix is essentially a unitary matrix, and is proven an optimal channel estimator in the sense of achieving the minimum MSE (mean squared error) of channel estimation for a fixed power of pilot tones. In the second part, this dissertation addresses the wireless location problem in WiMax (worldwide interoperability for microwave access) networks, which is mainly based on the MIMO-OFDM technology. From the measurement data of TDOA (time difference of arrival), AOA (angle of arrival) or a combination of those two, a quasi-linear form is formulated for an LS-type solution. It is assumed that the observation data is corrupted by a zero-mean AWGN (additive white Gaussian noise) with a very small variance. Under this assumption, the noise term in the quasi-liner form is proved to hold a normal distribution approximately. Hence the ML (maximum-likelihood) estimation and the LS-type solution are equivalent. But the ML estimation technique is not feasible here due to its computational complexity and the possible nonexistence of the optimal solution. Our proposed method is capable

The paper deals with the problem of the carrier frequency-offset (CFO) estimation for wireless communications. Since the well-known maximum-likelihood (ML) CFO estimator is rather complex, many complexity-reduced CFO estimators have so far been proposed. However, most of them are based on periodic training sequences and have limited estimation range. We propose a general framework called composite frequency- offset estimator (CFE), which can extend the estimation range of any range-limited correlation-based CFO estimator up to the full transmission spectrum. We first formulate the CFO estimation as a problem of composite hypothesis testing, and then solve the problem by the composite hypothesis testing approaches. We show that the composite frequency-offset estimate is the ML estimate as long as the original range-limited estimator is unbiased and attains the Cramer-Rao Lower Bound. The CFE also allows a flexible tradeoff between the estimation range and computational complexity. Finally, numerical results show the efficiency of the proposed method.

In orthogonal frequency division multiplexing (OFDM) systems, most of the conventional sampling frequency offset (SFO) estimation methods work under the assumption of time-invariant or slow time-variant channels. In mobile environment, the time-variant channel significantly degrades the accuracy of SFO estimation. To solve the problem, we first analyze the properties of time-variant channels. If terminal moves within some tens of the wavelength of radio frequency (RF) signal, channel path delay almost remains unchanged. For most practical OFDM systems, our analysis indicates that channel path delay can be regarded as unchanged during the interval of some tens of OFDM symbols in time-variant channels. Based on the analysis, we propose a novel SFO estimation method for pilot-aided OFDM systems. Different from the conventional methods, the proposed method estimates SFO by detecting the variation of the symbol timing error caused by SFO. The detection is finished by implementing correlation between the channel impulse responses (CIRs) estimated by different OFDM symbols. Performance of the proposed method is simulated and compared with that of two conventional post-FFT methods. Numerical results show that, the proposed SFO estimation method performs better than the conventional methods not only in time-variant channels, but also at low SNRs and large residual carrier frequency offsets (CFOs).

Ambient backscatter communication (ABC) is a promising technology for future wireless systems due to its ultra-low-power operation and simplicity. To further enhance its performance, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) has been adopted in this work. However, carrier frequency offset (CFO) can disrupt the orthogonality of the MIMO-OFDM signal, which significantly degrades the performance of the reader’s receiver. To address this issue, we consider an asynchronous ABC system and propose a deep learning (DL) algorithm to estimate and compensate the CFO at the reader, ensuring reliable communication in MIMO-OFDM-based ABC systems. The proposed architecture integrates a hybrid attention mechanism, which captures inter-dependencies between the two types of extracted features, and a squeeze-and-excitation network that adaptively refines channel-wise feature responses. The proposed architecture can also work for conventional OFDM and MIMO-OFDM systems. Furthermore, to validate the model’s robustness and generalization capability, additional test datasets are generated for various modulation schemes and different 3GPP TR38.901 tapped delay line channel models. The simulation results indicate its outperformance over existing approaches and has lower computational complexity than the existing methods.

In this paper, we propose a low-complexity carrier frequency offset (CFO) estimation algorithm for OFDM based wireless LAN, IEEE 802.11a. The complexity of the arctangent operation to calculate the argument of auto-correlation for CFO estimation is reduced by a novel range pointer method. The proposed algorithm estimates fine CFO value first and then based on the fine CFO value, simple criteria is used for the boundary decision of integer CFO estimation. The simulation results show that the performance of the proposed algorithm is slightly better than the conventional method while the computational complexity is reduced by 50%. Furthermore, the proposed method can be easily implemented for the low complex next generation MIMO-OFDM based WLAN systems.

In recent years, the constant-envelope orthogonal frequency-division multiplexing (CE-OFDM) has been considered as a candidate waveform in broadband satellite systems as it has a 0-dB peak-to-average power ratio (PAPR). However, the carrier frequency offset (CFO) estimation scheme for CE-OFDM broadband satellite systems directly affects system performance. In this paper, we analyze the network architecture and the propagation environment of CE-OFDM broadband satellite systems, and we propose a data-aided CFO estimation strategy based on the frequency domain pilot symbols. The Cramer–Rao bound (CRB) of our CFO estimator is given by mathematical analysis, and the effect of the number of pilot symbols on the estimation performance is analyzed. The pilot symbol-based CFO estimator is composed of a phase demodulator and a discrete Fourier transform (DFT) module, and it can obtain a large estimation range under a small pilot overhead. The simulation results show that the CE-OFDM broadband satellite systems can achieve a good bit error rate (BER) performance by using the proposed strategy to estimate and compensate the CFO.

Block-wise frequency offset estimation scheme in MIMO-OFDM systems

The carrier frequency offset (CFO) estimation is an important task for orthogonal frequency-division multiple-access (OFDMA) systems. Making use of the intrinsic periodic structure of the received signal samples, an effective Unitary-ESPRIT algorithm based on unitary transformation technique is proposed for the CFO estimation problem. Compared with other recently proposed CFO estimation approaches, the proposed algorithm offers a number of advantages. Firstly, the presented approach can reduce the computation complexity considerably, since it is efficiently formulated in terms of real-valued computation except for the data preprocessing. Secondly, it can provide better CFO estimation performance by incorporating the data stacking technology and the unitary transformation, etc. Simulation results demonstrate the efficacy of the proposed algorithm.

Although fruitful studies have been conducted on carrier frequency offset (CFO) estimations in linear coherent optical fiber communication systems, there are few studies on CFO estimations and recoveries in the systems based on the nonlinear Fourier transform (NFT). Although the CFO is originated from the linear frequency domain, it definitely has effects on nonlinear spectra, including the shift of the nonlinear frequency and the phase rotations of the scattering data, which are similar to its effects on linear spectra. This work indicates that it is feasible to estimate frequency offset (FO) by capturing symbol variations in the nonlinear frequency domain (NFD) rather than in the linear frequency domain; the latter was usually exploited in the literature. Based on a thorough investigation of the FO induced behavior that appears in a nonlinear frequency division multiplexing (NFDM) system, we proposed a nonlinear frequency domain estimation method aided by training symbols (TS) using an angle search algorithm after NFT operations at the receiver. The discussions in this paper prove that the proposed method is generally applicable to the NFDM systems regardless of whether using single or multiple eigenvalues. A performance comparison between the NFD method and the conventional method in the linear frequency domain is performed with different modulation formats for both single and multiple eigenvalue NFDM transmission systems. The analysis results show that the proposed method holds the better stability and estimation accuracy in contrast with the linear domain estimation method. The TS overhead can also be deduced dramatically, which implies better transmission efficiency. Therefore, the NFD method is more powerful for eigenvalue NFDM transmission systems, especially for the scenarios where high order modulation formats and multiple eigenvalues are utilized.

In this paper we propose a non-data aided estimator for joint time delay, carrier frequency offset (CFO) and channel estimation for uplink asynchronous CDMA system. The cost function for CFO estimation is a determinant of a matrix, which is a function of the CFO. The CFO estimator does the estimation by performing a linear search and the search point which minimizes the determinant is the estimated CFO. The matrix corresponding to the estimated CFO is used for channel estimation, where the channel impulse response (CIR) is obtained as the eigenvector corresponding to the minimum eigenvalue of that matrix. Time synchronization is inherently done along with the channel estimation, assuming the maximum channel order for all user channels is known. We also modify the existing estimator based on generalized eigenvalue problem (GEVP) and show the performance improvement through computer simulations. The performance of the proposed method is compared with the modified generalized eigenvalue problem method (MGEVPM). Computer simulations show that the proposed estimator performs better than MGEVPM and has wider CFO acquisition range than MGEVPM. Using Taylor's series approximation, we reformulate the proposed estimator in order to reduce the computational complexity. Through computer simulations we show that the performance of the reformulated estimator improves with increase in the order of Taylor's series and performs better than MGEVPM.

The problem of pilot assisted carrier frequency offset (CFO) estimation in LTE system is investigated in this paper. Since both the pilot design and the number of pilot symbols are already fixed in LTE technical standards, it is proposed to employ multiple successive pilot symbols to improve the CFO estimation performance under the LTE standard constraints. We firstly present the generalized CRLB (GCRLB) analysis based on multiple successive pilot symbols. It is revealed that, better CFO estimate can be achieved by jointly using multiple successive pilot symbols than by using only one pilot symbol. The enhanced maximum likelihood (ML) carrier frequency offset estimate problem is formulated and simulation results are presented to validate that the proposed CFO estimate scheme offers an effective mechanism to improve the CFO estimate in practical LTE system.