Enhanced Channel Estimation Based On Basis Expansion Using Slepian Sequences for Time Varying OFDM Systems

The Channel estimation in OFDM has become very important to recover the accurate information from the received data as the next generation of wireless technology has very high data rate along with the very high speed mobile terminals as users. In addition the fast fading channels, ICI, multipath fading channels may completely destroy the data. Also it is required to use less complex method for estimation. We are proposing the method which compares the number of techniques and gives the results in BER Vs SNR graphs. The LS estimation technique is less complex as compared to MMSE estimation but gives fails in accuracy. Using Prolate function we can reduce the complexity in calculation of parameters. If compared with state of art approach where the complexity is O(N)3, the complexity using Prolate function is O(N)2.The function depends upon maximum delay and maximum Doppler frequency spread thus parameter calculation is reduced. The technique dose not calculate particular channel characteristics. Slepian sequences utilizes the bandwidth as the sharp pulses replace the regular rectangular pulses which causes spectral leakage and thus ICI. The simulation of BER Vs SNR using CP and UW with and without Prolate is proposed that increases spectral efficiency with reduced calculations replacing rectangular pulses by Slepian pulses which increase energy concentration by Sharpe pulses thus reduction in inter carrier interference caused by multipath fading. DOI: 10.17762/ijritcc2321-8169.150513

Joint channel estimation and data detection for OFDM systems over doubly selective channels

In this paper, a joint channel estimation and data detection algorithm is proposed for OFDM systems under doubly selective channels (DSCs). After representing the DSC using Karhunen-Loève basis expansion model (K-L BEM), the proposed algorithm is developed based on the expectationmaximization (EM) algorithm. Basically, it is an iterative algorithm including two steps at each iteration. In the first step, the unknown coefficients in K-L BEM are first integrated out to obtain a function which only depends on data, and meanwhile, a maximum a posteriori (MAP) channel estimator is obtained. In the second step, data are directly detected by a novel approach based on the function obtained in the first step. Moreover, a Bayesian Cramer-Rao Lower Bound (BCRB) which is valid for any channel estimator is also derived to evaluate the performance of the proposed channel estimator. The effectiveness of the proposed algorithm is finally corroborated by simulation results. ©2009 IEEE.published_or_final_versionThe 20th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2009), Tokyo, Japan. 13-16 September 2009. In Proceedings of the 20th PIMRC, 2009, p. 446-45

Optimum Averaging of Superimposed Training Schemes in OFDM under Realistic Time-Variant Channels

The current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.This work was supported by the Spanish National Project Hybrid Terrestrial/Satellite Air Interface for 5G and Beyond - Areas of Dif-cult Access (TERESA-ADA) [Ministerio de Economía y Competitividad (MINECO)/Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2017-90093-C3-2-R

Superimposed training-based channel estimation and data detection for OFDM amplify-and-forward cooperative systems under high mobility

In this paper, joint channel estimation and data detection in orthogonal frequency division multiplexing (OFDM) amplify-and-forward (AF) cooperative systems under high mobility is investigated. Unlike previous works on cooperative systems in which a number of subcarriers are solely occupied by pilots, partial data-dependent superimposed training (PDDST) is considered here, thus preserving the spectral efficiency. First, a closed-form channel estimator is developed based on the least squares (LS) method with Tikhonov regularization and a corresponding data detection algorithm is proposed using the linear minimum mean square error (LMMSE) criterion. In the derived channel estimator, the unknown data is treated as part of the noise and the resulting data detection may not meet the required performance. To address this issue, an iterative method based on the variational inference approach is derived to improve performance. Simulation results show that the data detection performance of the proposed iterative algorithm initialized by the LMMSE data detector is close to the ideal case with perfect channel state information. © 2006 IEEE.published_or_final_versio

Pilot Design for Enhanced Channel Estimation in Doubly Selective Channels

This paper investigates pilot design for enhanced channel estimation in single carrier communication systems over doubly-selective channels (DSC). Our contribution is twofold: first, we propose to use Huffman sequences as pilot clusters with low peak-to-average power ratio (PAPR), yet with good channel estimation performance when periodic pilot placement is adopted; second, we propose a low-complexity pilot placement strategy based on the analysis of the complex-exponential basis expansion model (CE-BEM) of the DSC. The latter leads to improved channel estimation performance with useful insights for pilot placement

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Signal Detection for OFDM-Based Virtual MIMO Systems under Unknown Doubly Selective Channels, Multiple Interferences and Phase Noises

In this paper, the challenging problem of signal detection under severe communication environment that plagued by unknown doubly selective channels (DSCs), multiple narrowband interferences (NBIs) and phase noises (PNs) is investigated for orthogonal frequency division multiplexing based virtual multiple-input multiple-output (OFDM-V-MIMO) systems. Based on the Variational Bayesian Inference framework, a novel iterative algorithm for joint signal detection, DSC, NBI and PN estimations is proposed. Simulation results demonstrate quick convergence of the proposed algorithm, and after convergence, the bit-error-rate performance of the proposed signal detection algorithm is very close to that of the ideal case which assumes perfect channel state information, no PN, and known positions and powers of NBIs plus additive white Gaussian noise. Furthermore, simulation results show that the proposed signal detection algorithm outperforms other state-of-the-art methods.published_or_final_versio