Implementation and Co-Simulation of Hybrid Pilot-Aided Channel Estimation With Decision Feedback Equalizer for OFDM Systems

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.

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.

A pilot-aided scheme is proposed for the joint estimation and tracking of channel impulse response (CIR), carrier frequency offset (CFO) and sampling frequency offset (SFO) in burst mode, multiple-input-multiple-output (MIMO), and orthogonal frequency division multiplexing(OFDM) systems. First, a received signal model that takes into account nonlinear CFO/SFO effects is formulated. Then, based on the derived signal model and the received multicarrier signal samples containing the pilot tones in the frequency domain, an objective function that includes MIMO CIR, CFO, and SFO is introduced to develop a vector recursive-least-square (RLS)-based joint channel estimation and synchronization algorithm. The analysis and simulation results show that, over the large ranges of the CFO and SFO values, the proposed estimation and tracking scheme offers fast convergence, high stability, and a near-optimum receiver performance that is remarkably close to the ideal performance in the case of perfect channel estimation and synchronization over quasi-static Rayleigh multipath fading channels.

Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade the channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as in a significantly lower overall computational complexity.

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.

This study deals with the joint channel and carrier frequency offset (CFO) estimation in a Multiple Input Single Output (MISO) communications system. This problem arises in OFDM (Orthogonal Frequency Division Multiplexing) based multi-relay transmission protocols such that the geo-routing one proposed by A. Bader et al in 2012. Indeed, the outstanding performance of this multi-hop relaying scheme relies heavily on the channel and CFO estimation quality at the PHY layer. In this work, two approaches are considered: The first is based on estimating the overall channel (including the CFO) as a time-varying one using an adaptive scheme under the assumption of small or moderate CFOs while the second one performs separately, the channel and CFO parameters estimation based on the considered data model. The two solutions are analyzed and compared in terms of performance, cost and convergence rate.

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

The paper proposes a novel estimation scheme of carrier frequency offset (CFO) based on Added Preamble Sequence Semi-Blind (APSSB) scheme. The proposed CFO estimator is based on APSSB OFDMA model in which Gold sequence is added to the output of IDFT modulator and exploited for CFO estimation at the receiver. CFO estimators based on APSSB scheme and preamble are compared by both SINR analysis and simulation. We propose that the APSSB scheme should be employed for fine frequency synchronization in OFDMA especially in delay- insensitive service. In this paper, we propose the APSSB OFDMA model in which APSSB sequence is added to the output of IFFT modulator. The APSSB sequence is exploited for CFO estimation by correlation detection with the local APSSB sequence at the receiver. Comparison of SINR is made between CFO estimation schemes based on APSSB and preamble. More accurate CFO estimation by APSSB scheme doesn't cause date rate decrease because data and APSSB sequence are transmitted simultaneously. Furthermore, CFO estimation of APSSB scheme is more accurate than preamble scheme when APSSB symbol number for CFO estimation is large enough to exceed the threshold. Furthermore, the estimation time of APSSB scheme is more flexible than preamble scheme. The APSSB scheme has the property of better accuracy as well as larger delay. We propose the APSSB scheme should be employed for fine frequency synchronization in delay- insensitive service.

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.

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 the uplink system, different carrier frequency offsets (CFO) of different users at the receiver resulting multiuser interference (MUI) and thus degrade the performance of Multicarrier Code Division Multiple Access (MC-CDMA) system. In the previous study, Modified Minimum Mean Square Error Frequency Domain Equalization (Modified MMSE-FDE) has been proposed by Agnihotri which results in a better performance than the conventional MMSE. However, the achieved Bit Error Rate (BER) is not smaller than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for Signal to Noise Ratio (SNR) below 20. This paper proposed a new algorithm to improve Modified MMSE-FDE performance in mitigating multiuser interference (MUI) in single cell MC-CDMA system, that is called as Carrier Frequency Offset Estimation-Minimum Mean Square Error Frequency Domain Equalization (CFO Estimation-MMSE FEQ). CFO Estimation-MMSE FEQ takes into account the rest of MUI as well as Modified MMSE-FDE to design equalization coefficient, but CFO Estimation-MMSE FEQ uses CFO estimation that is not on Modified MMSE-FDE. The value of CFO is estimated and regenerated in the receiver and then used to estimate MUI. In order to get signal processing more efficient, CFO Estimation-MMSE FEQ only takes the real trace of equalization coefficient matrix. MC-CDMA with CFO Estimation-MMSE FEQ has better performance than MC-CDMA with Modified MMSE-FDE. However, the complexity of CFO Estimation-MMSE FEQ is higher than Modified MMSE-FDE. By taking only the real trace of equalization coefficient matrix, the complexity of CFO Estimation-MMSE FEQ is getting simpler than taking the whole of equalization coefficient matrix. Furthermore, CFO Estimation-MMSE FEQ that takes only the real trace of equalization coefficient matrix is 2.9 % more efficient than CFO Estimation-MMSE FEQ that takes the whole of equalization coefficient matrix.

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 this paper, we evaluate the Cramer-Rao bounds (CRB) for the estimation of the carrier frequency offset (CFO) for general QAM modulations with no knowledge of the transmitted sequence (blind operation) in frequency hopping (FH) based short burst mode mobile radios. We investigate the blind CFO estimation problem in FH based mobile system that uses multiple short narrowband bursts in different frequency channels for the transmission of one packet. Joint blind channel and CFO estimation over multiple frequency bursts is considered. In the first part of the paper, we determine the limiting performance by finding the CRB for blind estimation, and compare it to the CRB for known data (genie aided) case. The CRB relates the reliability of CFO estimation to the number of radio frequency bursts (RFB) used, constellation and SNR level, and is an important measure for system design without the need to simulate specific algorithms. It is shown that the linear combination (in MMSE sense) of multiple frequency estimates, each obtained from a single RFB independently, achieves the joint CRB over multiple bursts. In the second part of the paper, some practical blind estimation methods are proposed. They are shown to exhibit close performance to the true CRB.

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.

The nonlinear distortions attributed by radio frequency power amplifiers (PAs) are inevitable in millimeter-wave (mmWave) systems due to the high frequency and large bandwidth. This nonlinearity induces multiplicative distortion and intercarrier interference in mmWave multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing systems, which in collusion with a frequency-selective channel and a carrier frequency offset (CFO) brings great challenges to signal detection. Furthermore, the nonlinearity destroys orthogonality of the training pilots and degrades the estimation accuracy of the channel gains and the CFO. Also, the target posterior distribution for data detection becomes analytically intractable due to the nonlinearity. In this article, we present an importance-sampling-based framework for estimating the CFO in the presence of PA impairment, which utilizes few pilots and initializes the channel estimator. We propose a particle-filter-based iterative algorithm for data detection, where the intractable posterior distribution is approximated by weighted random measures. Furthermore, a sequential-maximum-likelihood-based semiblind channel estimator in the presence of nonlinearity is proposed. Performances of the estimator and the detector are evaluated by means of normalized mean square error, mean square error, and bit error rate. To validate the efficacy of the channel and the CFO estimator, the Cramer–Rao bound (CRB) and the weighted Bayesian CRB are derived, respectively.