Multi-invariance propagator method-based carrier synchronization for MIMO-OFDM electrical special network

OFDM (orthogonal frequency division multiplexing) is a promising technology for future broadband wireless communications due to its capability in combating multipath fading. Carrier frequency offset (CFO), which can induce the loss of orthogonality among OFDM sub-carriers and significant performance degradation, needs to be estimated and compensated for. In this paper, we propose a general CFO estimator based on the maximum likelihood (ML) estimation criterion. It will be shown that with the same estimator architecture, CFO can be obtained using either training OFDM symbols, pilot tones, null sub-carriers, or a combination of them. Furthermore, by taking advantage of the channel side information, the performance of CFO estimation can be significantly improved. To demonstrate the capability of the proposed CFO estimator, we will consider an OFDM system using the signal structure of the IEEE WLAN standard 802.11a. Compared with previous work using null sub-carriers alone, it will be shown that by taking advantage of the pilot tones, null sub-carriers, and the channel statistics, the performance of CFO estimation can be improved by about 2 dB

In this paper, we consider design optimization of low-density parity-check (LDPC) coded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high data rate wireless transmission. First, starting from the maximum-likelihood (ML) principle, we devise a novel expectation-maximization (EM)-based scheme for joint estimation of carrier frequency-offset (CFO) and channel coefficients in MIMO-OFDM systems. Based on simulation results, the performance of joint estimator is quite satisfying. Then, this CFO estimator is incorporated into the initialization step of the turbo receiver. Experimental results show the effectiveness of our receiver design in combating CFO over unknown frequency selective fading channels.

ABSTRACTWhile inter-symbol interference is not of concern, inter-carrier interference that is caused by carrier frequency offset (CFO) is more concerned in orthogonal frequency division multiplexing (OFDM) systems. The estimation of channel impulse response (CIR) is another important parameter of interest in the system. Recently, a number of approaches for the joint estimation of CFO and CIR in OFDM systems have been proposed in the literature, and most of them are traditional maximum-likelihood-based approaches. In this paper, we propose a number of mixture cost-reference particle filtering (CRPF) approaches for joint estimation of CIR and CFO, and a couple of them can be applied without the knowledge of both state and measurement noise statistics. Conventional least mean squares algorithm or recursive least squares algorithm is jointly employed with recently developed CRPF for nonlinear and multiple parameters estimation in OFDM systems. The computer simulation results show that mixture CRPFs are more robust against particle impoverishment (PI) problem than standard mixture particle filtering (PF) and mixture regularized PF (RPF) approaches although RPF was particularly developed to tackle the PI problem. In this paper, we investigate a number of mixture PF approaches including ones that do not need the information of noise statistics for the joint estimation of CFO and channel in OFDM systems.

The paper proposes a new iterative method to estimate the carrier frequency offset (CFO) with scattered pilot symbols inserted for channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Assuming that the channel remains constant during a few consecutive OFDM blocks, the new method uses the correlations of the pilot symbols in these consecutive OFDM blocks. And an iterative concept is also employed to reduce the inter-carrier interference (ICI) that deteriorates the CFO estimation performance. After a few iterations, the proposed method can achieve the accuracy with no ICI. The mean square error (MSE) of the new method is analyzed and simulation results show the new method outperforms existing methods.

Multiple Input Multiple Output (MIMO) system with orthogonal frequency division multiplexing (OFDM) is a promising physical layer candidate for the current and future wireless standards. MIMO increases system throughput while OFDM maximizes spectrum efficiency. To reduce the impacts of frequency selective fading and increase the capacity, MIMO requires precise channel state information (CSI). The presence of carrier frequency offset (CFO) introduces intercarrier interference (ICI) among the subcarriers which degrades the system performance. MIMO channel estimation is done under the assumption of perfect frequency synchronisation between the transmitter and receiver, or zero CFO. However, such an assumption is incorrect for time-varying and frequency-selective wireless channels, and the presence of CFO has an impact on channel estimation accuracy. In the existing methods, CFO and channel estimations are carried out independently using separate training sequences which reduces the spectral efficiency. As a result, the pilot aided interpolation approach is proposed in this paper for the joint estimate and compensation of CFO and channel effects using the same set of pilot tones. Theresults show that the proposed method offers precise CFO and channel estimation which improves the bit error rate performance and carrier to interference power ratio (CIR) of MIMO-OFDM system.

The estimation of carrier frequency offset (CFO) is an important issue in the study of the orthogonal frequency division multiplexing (OFDM) systems. In the past, many CFO estimation methods have been proposed. In this paper, we propose a new blind CFO estimation for OFDM systems based on the so-called remodulated received vectors [8]. The CFO estimate is given by a closed form formula. The proposed method has very low complexity and its performance is robust to different modulation symbols and the presence of virtual carriers.

A bstract—To overcome the inter-carrier interference (ICI) of orthogonal frequency division multiplexing (OFDM) systems due to the carrier frequency offset (CFO), an ICA-based carrier synchronization method is proposed. Independent component analysis (ICA) is also known as blind source separation (BSS), which depends on the independence of source signals to separate or extract latent signals of interest. In this paper, we build an analogous ICA model for OFDM systems with the CFO. By virtue of this model, the ICA technique can be utilized to compensate corresponding loss of performance due to imperfect CFO estimation in the conventional method. Theoretical analysis shows that the ICA-based carrier synchronization method not only has maximum likelihood (ML) source recovery ability, but also can improve signal to noise ratio (SNR) of OFDM systems. Simulation results demonstrate the validity of the proposed method.

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.

Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

Low cost video sensors used for streaming video signals to help firefighters, require high bit rate due to uncompressed images. To increase spectral efficiency given a limited bandwidth, very high order constellations in high signal to noise ratio regimes can be used. However, noise is not the only factor effecting the high order constellations. These constellations are also sensitive to hardware impairments and system non‐linearities. Therefore, in this paper, the effect of carrier frequency offset (CFO) on the performance of an orthogonal frequency division multiplexing (OFDM) system with high order quadrature amplitude modulation (QAM) is studied. A closed form expression is derived for the maximum normalized residual CFO that an OFDM system with M ‐QAM constellation can resist to have an error free symbol detection. Finally, the suitability of common previous CFO estimation techniques such as the cyclic prefix based technique and the Moose technique in these systems are investigate. The results show that the maximum residual CFO that an OFDM system with M ‐QAM constellation can resist is proportional to the inverse of . The results also show that very large order QAM constellations such as 4096‐QAM are very sensitive to even small residual CFO values and their performance degrades, significantly. However, the bit error rate analysis indicate that the Moose CFO estimation technique can be used in these systems to compensate the CFO effect, accurately.

Blind carrier frequency offset estimation in OFDM systems with I/Q imbalance

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.

In this paper, we study the joint estimation of inphase and quadrature-phase (I/Q) imbalance, carrier frequency offset (CFO), and channel response for multiple-input multipleoutput (MIMO) orthogonal frequency division multiplexing (OFDM) systems using training sequences. A new concept called channel residual energy (CRE) is introduced. We show that by minimizing the CRE, we can jointly estimate the I/Q imbalance and CFO without knowing the channel response. The proposed method needs only one OFDM block for training and the training symbols can be arbitrary. Moreover when the training block consists of two repeated sequences, a low complexity two-step approach is proposed to solve the joint estimation problem. Simulation results show that the mean-squared error (MSE) of the proposed method is close to the Cramer-Rao bound (CRB).

This paper proposes two blind carrier frequency offset (CFO) estimation schemes for differentially modulated orthogonal frequency division multiplexing (OFDM) systems. The proposed schemes estimate the fractional part of the CFO with only two consecutive OFDM blocks, and they exploit two implicit properties associated with differentially modulated OFDM (DOFDM) systems, i.e., the channel keeps constant over two consecutive OFDM blocks, and the DOFDM systems employ an M-ary phase-shift keying constellation. One of the schemes is based on the finite alphabet (FA) constraint and the other one is based on the constant modulus (CM) constraint. They provide a trade-off between the performance and computational complexity. The constrained Cramer-Rao lower bound is also derived. Several numerical examples are presented to validate the efficacy of the proposed schemes.

Carrier frequency offset (CFO) is an important issue in orthogonal frequency division multiplexing (OFDM) systems. It destroys the orthogonality between subcarriers and causes degradation in system performance. This paper presents an improved algorithm for the existing ESPRIT-based CFO estimation method. The proposed method estimates the CFO by taking the determinant of a matrix within the ESPRIT algorithm. It is a simple modification to the original ESPRIT-based method and its computational overhead is low. The performance analysis shows that the proposed method can achieve a lower mean square error.