Frequency-selective Channel Estimation Research Articles

Optimal Estimation of Frequency-Selective Channels in OFDM-based Superimposed Training Schemes

Optimal Estimation of Frequency-Selective Channels in OFDM-based Superimposed Training Schemes

Joint CFO and highly frequency selective channel estimation in FBMC/OQAM systems

Filter Bank-based Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) has been considered as an alternative waveform to the conventional Cyclic Prefix (CP)-based Orthogonal Frequency Division Multiplexing (OFDM). Despite the adoption of variants of the latter in 5G NR, FBMC/OQAM is still at the top of the promising multicarrier modulation waveforms for future generation communication systems. Although it enjoys a number of unique advantages over CP-OFDM, most importantly maximum spectral efficiency and good time-frequency localization, it is also sensitive to Carrier Frequency Offset (CFO), which can cause inter-carrier interference that degrades system performance if left uncompensated. Besides, the presence of CFO prevents accurate channel estimation. In this paper, our goal is to propose joint estimators for the channel impulse response (CIR) and CFO via an approximate Maximum Likelihood (ML) approach. No simplifying assumption is made about the channel delay spread so our results also apply to highly frequency selective channels. One of the novelties is that the estimation procedure is aided by so choosing the pilots as to result in zero intrinsic interference in the absence of impairments. Moreover, to reduce the complexity of CFO estimation, a lighter implementation, based on the truncation of the Taylor series expansion of the exponential term containing the CFO, is proposed. Simulation results demonstrate the superiority of the proposed estimators over existing methods, in terms of both mean squared estimation errors and bit error rate.

Preamble based SNR Estimation in Frequency Selective Channels for Wireless OFDM System

Preamble based SNR Estimation in Frequency Selective Channels for Wireless OFDM System

Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling

Downlink channel estimation in FDD massive MIMO systems is a challenge in 5G wireless communication systems. Using orthogonal pilots for downlink channel estimation leads to the pilot overhead problem. To cope with this problem, spatio-temporal common sparsity feature of delay domain beside the compressive sensing algorithm has used for channel estimation. In a practical affair, the spatial common sparsity of the adjacent antennas groups is not entirely separate. In this paper, we model the FDD massive MIMO downlink frequency selective channel estimation problem by a spatial partial-common sparsity, in which it is assumed that the spatial sparsity pattern of antennas in each group has a common part and an uncommon part. For the proposed model, we design a proper pilot sequence, and finally, we propose an estimation method associated with this model to solve the problem. Our proposed method has better NMSE and BER performance than reference methods in the same pilot overhead ratio, which is shown in the simulation results.

Training‐based frequency synchronisation and highly frequency selective channel estimation for OFDM/OQAM systems

This work addresses frequency synchronisation and channel estimation for highly frequency selective orthogonal frequency division multiplexed systems with offset quadrature amplitude modulation. A two-step approach is proposed, wherein a coarse carrier frequency offset (CFO) estimator, which does not require channel knowledge, is designed in the first step by exploiting the correlation between the received and training signals. A time-domain model is derived for the joint estimation of fine CFO (FCFO) and highly frequency selective channel in the second step. In contrast to the frequency domain model in the literature, the derived time-domain model does not require the channel to be frequency flat. Based on the time-domain model, the weighted least squares and minimum mean square error estimators are investigated to jointly estimate the FCFO and frequency selective channel. The Cramer-Rao lower bounds (CRLBs) are derived for the proposed estimators, and are used to evaluate the performance of the joint FCFO and channel estimation in the presence of inter-symbol-interference due to the overlapping of adjacent time-domain orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) symbols. Simulation results demonstrate achievability of the CRLB bounds and performance gain of the proposed estimators over the state-of-the-art existing estimators.

Low Complexity Estimation of Frequency Selective Channels for the LTE-A Uplink

3GPP LTE-A uplink channel estimation is based on pilot symbols. For the support of MIMO transmissions, a special reference symbol structure was standardized to perform MIMO channel estimation without excessive overhead. We propose a low complexity channel estimation method in the frequency domain based on correlation that exploits the orthogonal structure of the reference symbols to mitigate inter-layer interference. Further, we introduce a smoothing method to deal with the interference that leads to improved performance at low signal to noise ratio.

Preamble‐aided time delay estimation in frequency selective channels for wireless OFDM systems

In this Letter, an improved method for estimating the time delay in preamble-aided orthogonal frequency division multiplexing systems is presented. It uses a conventional preamble structure and combines cross-correlation techniques to achieve estimations of time delay and the number of multipaths without any additional overhead. Computer simulations results show that the proposed method is of near-ideal property in frequency-selected channels.

Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems

The frequency-selective channel-estimation problem in multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is investigated from the perspective of compressed sensing (CS). By minimizing the mutual coherence of the measurement matrix in CS theory, two pilot allocation methods for the CS-based channel estimation in MIMO-OFDM systems are proposed. Simulation results show that using the pilot patterns designed by the two proposed methods gives a much better performance than using other pilot patterns in terms of the mean square error of the channel estimate as well as the bit error rate of the system. Moreover, the optimal pilot patterns obtained by the proposed second method based on genetic algorithm and shift mechanism could offer a larger performance gain than those by the first method based on minimizing the largest element in the mutual coherence set possessed by pilot patterns for all multiple antenna ports.

Fast Synchronization and Channel Estimation for OFDM Systems

A joint synchronization and channel estimation scheme is proposed for orthogonal frequency division multiplexing (OFDM) system. In the proposed scheme, the integer frequency offset, fine timing offset and channel estimation are estimated based on the characteristics of the chirp signals. The main contributions of the paper are: the more robust ability to find the actual first path instead of the strongest path on the assumption of repeated chirp waveforms in the timing synchronization approach, and the lower computational complexity with high performance in the channel estimation approach. Moreover, we analyze the performance of the proposed method and check it via Monte Carlo simulations. The simulation results show that the proposed method can achieve good performance for both synchronization and channel estimation in frequency selective channels.

Optimal Pilot Pattern Design for Compressed Sensing-Based Sparse Channel Estimation in OFDM Systems

The frequency selective channel estimation problem in orthogonal frequency division multiplexing (OFDM) systems is investigated from the perspective of compressed sensing (CS). By minimizing the mutual coherence or the modified mutual coherence of the measurement matrix in CS theory, two criteria for optimizing the pilot pattern for CS-based channel estimation are proposed. Simulation results show that using the pilot pattern designed by either of the two criteria gives a much better performance than using other pilot patterns in terms of the mean-squared error of the channel estimate as well as the bit error rate of the system. Moreover, the optimal pilot pattern designed by minimizing the modified mutual coherence offers a larger performance gain than that obtained by minimizing the mutual coherence.

On the performance evaluation of an LTE SFBC system with Wiener channel estimation in frequency-selective channels

The uncoded performance of an Alamouti space-frequency block code operating in a frequency-selective channel is studied in the presence of channel estimation errors. The Alamouti Maximum Likelihood (ML) symbol detector with linear combining is considered at the receiver. Imperfect channel estimates are provided by a realistic as well as robust Wiener interpolator. A closed-form expression of the uncoded error probabilities of a space-frequency block code employing an Alamouti with linear combining is derived. Our analysis is conducted in the framework of a Long-Term Evolution air interface. The impact of channel estimation errors on the uncoded performance of the system is evaluated as a function of key system parameters, such as the anticipated power delay profile at the channel estimator, the actual, i.e. true, power delay profile, the pilot spacing and the Wiener filter length. The role of partial knowledge of the channel statistics is investigated as well. Finally, we derive a lower bound from our closed-form expression and calculate the bit error outage of the linear Alamouti combiner in presence of log-normal fading signal-to-noise ratio (SNR).

Joint frequency selective channel estimation and turbo decoding in space time systems

A non-coherent multiple input multiple output (MIMO) coded communication system over a frequency selective (FS) block fading channel is considered. A theoretical limit for the channel estimation error is established via a closed form derivation of the modified Cramer-Rao bound (MCRB) (more precisely, a tight lower bound to it) for the underlying channel model and equi-power signal constellations. Furthermore, it is shown that, for a practical coded MIMO system that employs turbo coding and an appropriate iterative decoding, the resulting mean squared error (MSE) in channel estimation approaches very closely to the derived theoretical limit.

PAR-Constrained Training Signal Designs for MIMO OFDM Channel Estimation in the Presence of Frequency Offsets

Training signals for OFDM channel estimation should possess low PAR to avoid nonlinear distortions at the transmit amplifier and at the same time they should be robust against frequency offsets. In this letter, we show that the above two requirements of the training signals are conflicting. We propose two new training signal designs for MIMO OFDM frequency-selective channel estimation. Our proposed training signals achieve more robust channel estimation performance against frequency offsets while satisfying the PAR constraints compared to training signals designed to achieve a fixed low PAR but without any consideration for robustness to frequency offsets.

Optimal training signals for MIMO OFDM channel estimation

This paper presents general classes of optimal training signals for the estimation of frequency-selective channels in MIMO OFDM systems. Basic properties of the discrete Fourier transform are used to derive the optimal training signals which minimize the channel estimation mean square error. Both single and multiple OFDM training symbols are considered. Several optimal pilot tone allocations across the transmit antennas are presented and classified as frequency-division multiplexing, time-division multiplexing, code-division multiplexing in the frequency-domain, code-division multiplexing in the time-domain, and combinations thereof. All existing optimal training signals in the literature are special cases of the presented optimal training signals and our designs can be applied to pilot-only schemes as well as pilot-data-multiplexed schemes.

Aperiodic complementary sets of sequences-based MIMO frequency selective channel estimation

Accurate estimation of MIMO frequency selective fading channels is important for reliable communication. In this letter, a new channel estimator which relies on aperiodic complementary sets of sequences is proposed. Theoretical analysis and Monte-Carlo simulation have shown that it achieves the minimum possible Cramer-Rao lower bound. A low-complexity hardware implementation of the estimator is also provided.

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

Time-Varying and Frequency-Selective Channel Estimation with Unequally Spaced Pilot Symbols

In this paper, an accurate and computationally efficient algorithm is proposed for estimating a time-varying and frequency-selective fading channel with unequally spaced pilot symbols. By employing the time-varying coefficient polynomial interpolation method, it is proved that the time-varying channel impulse response can be estimated by the product of a constant interpolation matrix and the fading information at pilot symbol positions. Furthermore, a least square off-line training algorithm is presented to optimally calculate the constant matrix, taking into consideration the statistics of channel fading and noise. Simulation results indicate that the bit error rate performance of our new estimation algorithm is close to that of the perfect channel estimation.

Training sequence design for frequency offset and frequency-selective channel estimation

We consider the problem of data-aided frequency-offset and channel estimation in the case of frequency-selective channels. More precisely, we address the problem of training sequence selection with the goal of providing accurate frequency offset and channel estimates. Toward this end, we consider the Crame/spl acute/r-Rao bound (CRB), for which we derive a closed-form expression. Since the CRB is a complicated function of the training sequence and the channel parameters, a much simpler asymptotic CRB is derived. Two criteria for training sequence design based on the asymptotic CRB are proposed, and a minmax approach is presented to optimize them. Our main contribution is to show that a white sequence is minmax optimal for both criteria considered, and that the quest for a generally optimal sequence is hardly motivated.

Training sequence selection for frequency offset estimation in frequency selective channels

We consider the problem of optimal training sequence selection for frequency offset estimation in frequency-selective channels. Since it is desired that the optimal training sequence does not depend on a particular estimation method, we examine the Cramér–Rao bound (CRB) for the problem at hand. For a fairly large class of training sequences, an expression for the asymptotic CRB is derived which depends in a simple way on the channel impulse response and the training sequence correlation. Based on the asymptotic CRB, two methods are presented to select an optimal training sequence. First, we consider a minmax problem which consists in minimizing the worst-case asymptotic CRB and whose solution is shown to be a white training sequence. Next, an expression for the training sequence that minimizes the asymptotic CRB is derived. Numerical simulations illustrate the estimation performance obtained with these training sequences.

Least squares frequency estimation in frequency-selective channels and its application to transmissions with antenna diversity

A new data-aided frequency estimator for frequency-selective fading channels is introduced. The proposed estimator is developed based on a least squares (LS) error criterion and can estimate frequency offsets without the need for channel information. Statistical analysis indicates that the resulting estimate is unbiased and tends to approach the Cramer-Rao lower bound (CRLB). Simulation shows that the proposed LS method is preferable to existing techniques in mobile communications. The application of the LS estimator to systems with transmitter antenna diversity is also considered. In particular, it is demonstrated that the LS method can be successfully applied to third-generation wireless communication systems.