EM-based ML channel estimation in OFDM systems with phase distortion using RB-EKF

In this article, design of preamble for channel estimation and pilot symbols for pilot-assisted channel estimation in orthogonal frequency division multiplexing system with null subcarriers is studied. Both the preambles and pilot symbols are designed to minimize the l2 or the l∞ norm of the channel estimate mean-squared errors (MSE) in frequency-selective environments. We use convex optimization technique to find optimal power distribution to the preamble by casting the MSE minimization problem into a semidefinite programming problem. Then, using the designed optimal preamble as an initial value, we iteratively select the placement and optimally distribute power to the selected pilot symbols. Design examples consistent with IEEE 802.11a as well as IEEE 802.16e are provided to illustrate the superior performance of our proposed method over the equi-spaced equi-powered pilot symbols and the partially equi-spaced pilot symbols.

Channel estimation can be classified into pilot‐assisted channel estimation, blind channel estimation, and decision DDCE. This chapter focuses on pilot‐assisted channel estimation in OFDM systems. There are two main design issues for channel estimation in OFDM systems. The first one is pilot design and the second one is channel estimator design with both low complexity and accuracy. An interpolation technique should be performed to obtain a channel estimation value at each data subcarrier. Channel estimation for MIMO‐OFDM systems is more complicated than single antenna systems but the basic principle is similar to the single antenna systems. The purpose of equalizers is to find the inverse function of the channel response and eliminate an Inter‐Symbol Interference (ISI) caused by the dispersive nature of the channel. The channel estimator and the equalizer are designed according to the system requirements such as pilot assignment, antenna array, system complexity, latency, and power.

To solve the problem of low accuracy of channel estimation in OFDM systems in the scenario of high-speed multipath channels, it is proposed in the paper that a noise reduction scheme of channel estimation is based on DFT (Discrete Fourier Transform) algorithm. By analyzing the algorithm principles, the mathematical model of the system is built, and the performance of the scheme is analyzed. Simulation results reveal that in high-speed scenario, the scheme has effectively achieved low bit error rate and low mean square error, thus having good application value.

Complex Amplitudes Tracking Loop for multipath channel estimation in OFDM systems under slow to moderate fading

In this paper, we propose a robust channel estimation method in OFDM systems over fast fading channels. In consideration of the effect of null subcarriers, the proposed channel estimation is first performed in the frequency domain. By making efficient use of channel correlation and the property of ICI to reduce the number of needed parameters for channel estimation, robust channel estimation for ICI is accomplished in parallel, pilot cluster by pilot cluster. It is then transformed into the time domain to obtain a gain in noise reduction and to reduce interpolation error as well. Simulation results show that the proposed channel estimation has good performance compared to previous works.

This paper presents SLRTNet, a deep learning model designed for channel estimation in OFDM systems. SLRTNet employs a super-resolution network in combination with a transformer architecture, which features an improved encoder-decoder structure. To extract essential input information, a transformer encoder with a multi-head attention mechanism and a Feedforward Net that utilizes LRR Blocks are used. A multi-level residual network is implemented as a transformer decoder to improve the model's generalization ability. The simulation results show that SLRTNet outperforms other deep learning-based neural network schemes. Compared with the conventional LS algorithm, SLRTNet exhibits a performance gain of 0.7 to 2 dB in the low signal-to-noise ratio (SNR) regime and 5.5 to 8 dB in the high SNR regime. This indicates that SLRTNet has good performance in channel estimation for OFDM systems and improves the accuracy of channel estimation effectively.

To combat with the ICI caused by the Doppler and carrier frequency errors, an autoregressive (AR) process is used to model time evolution of the fading channel and a novel frequency MMSE equalizer combined with Kalman channel estimator is proposed. Combined techniques outperform traditional linear and DFT interpolation methods in both slow and fast fading conditions. The complexity of the equalizer is reduced significantly by using the fact that only a few adjacent subchannels are major interferers to a desired subchannel. Mean squared error (MSE) and bit error rate (BER) performance of the proposed algorithm are verified by computer simulation

Pilot assisted channel tracking (PACT) has been very popular for channel estimation in OFDM systems. However, as the mobility in the system increases and one has to maintain a high accuracy in channel estimation, the pilot overhead typically has to be increased. Emerging cellular OFDM standards are expected to use about 6 - 12% pilot overhead per stream, and any possible reduction in pilot overhead would be useful. Decision directed channel tracking (DDCT) can help reduce pilot overhead, but is known to suffer from error propagation at high fade rates. In emerging broadband wireless systems promising peak bit rates of 50 Mbps or more, saving on pilot overhead by using DDCT schemes would be highly attractive provided: (a) Such a DDCT approach does not suffer from error propagation and has an error rate performance comparable to (or better than) PACT schemes even at high fade rates; (b) The computational complexity of such a DDCT approach is not significantly more than that of the MMSE based PACT scheme. In this work, we propose a low complexity DDCT method which exploits the structure of the regression matrix in conjunction with robust statistics to mitigate the effect of error propagation. It has a much lower computational complexity and a better error rate performance than the decision directed EM-Kalman and other existing robust statistics based DDCT schemes. The proposed method also outperforms a 12.5% pilot overhead based PACT scheme with only a modest increase in computational complexity.

We investigate a practical pilot design for indoor channels with an analysis of channel estimation for block and comb-type pilot arrangements. For the pilot design over indoor channels, the performance is determined by the tradeoff between the interpolation error by frequency selectivity of the channel and the noise reduction by the interpolator, compared to the Doppler effect. Results show that the performance of channel estimation for the comb-type pilot symbol, with respect to the derived criterion, outperforms that of the block-type pilot symbol for indoor channels, whereas it is a slow-fading environment.

Implementation of Compressed Sensing Technology with ROMP Reconstruction for Sparse Channel Estimation in OFDM Systems

OFDM technique uses multiple sub-carriers for the data transmission. Therefore, bit error rate increases because of inter-carrier interference caused by nonlinear high power amplifier and carrier frequency offset. Wireless OFDM transmission over multi path fading channels is characterized by small transmission gain in multiple sub-carrier frequency interval. Therefore bit error rate increases because of burst errors. Inter-leaver and convolution error control coding are effective for the reduction of this burst error. Pilot symbol is used for the channel estimation in OFDM systems. However, imperfect channel estimates in this systems degrade the performance. The performance of this convolution coding OFDM systems using inter-leaver, gauged by the bit error rate, is analyzed considering the nonlinear high power amplifier, carrier frequency offset and channel estimation error.

A Survey on Pilot Symbol Assisted Channel Estimation in OFDM Systems*

This paper considers the problem of channel estimation for OFDM systems, where the number of channel taps and their power delay profile (PDP) are unknown. Using a Bayesian approach, we construct a model in which we estimate jointly the coefficients of the channel taps, the channel order and decay rate of the PDP. In order to sample from the resulting posterior distribution we develop a novel Trans-dimensional Markov chain Monte Carlo (TDMCMC) algorithm. This is done using a Stochastic Approximation (SA) approach to develop an adaptively learning algorithm to improve mixing rates of the basic Birth-Death (B-D) Markov chain for the between model subspaces. Using simulations we assess its performance in terms of channel order estimation and bit error rate (BER). It is shown that the proposed algorithm can achieve results very close to the case where both the channel length and the PDP are known.

This paper presents a novel preamble-aided method for joint estimation of timing, carrier frequency offset, and channel for orthogonal frequency division multiplexing systems that operate in high-mobility situations. Basis expansion modeling (BEM) that captures the time variations of the channel is used to reduce the number of unknown channel parameters. The BEM coefficients along with timing and frequency offsets are estimated by using a maximum likelihood approach. An efficient algorithm is then proposed for reducing the computational complexity of the joint estimation. The complexity of the new method is assessed in terms of the number of multiplications. The mean square estimation error of the proposed method is evaluated in comparison with previous methods and the hybrid Cramer–Rao lower bound, indicating a remarkable performance improvement by the new method.

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.