Joint Ofdm Symbol Detection And Channel Estimation Over Doubly Selective Channels

"Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high data rate transmission, that is widely used in modern wireless communication systems because of its good performance over frequency selective channels. However OFDM systems are sensitive to channel time variation resulting in Inter-Carrier Interference (ICI), that without suitable detection methods can degrade performance. Channel State Information (CSI) is essential to various OFDM data detection schemes and its acquisition is a critical factor, in particular over time-varying channels. This work considers a technique for integrating the Kalman filter channel estimation with a version of Sphere Decoding (SD) adapted to OFDM detection. By modelling the OFDM system in frequency domain as a Multiple Input Multiple Output (MIMO) system, we derive a powerful SD technique for data detection. By approximating the channel matrix as banded, we are able to maintain a feasible complexity for SD and deliver competitive performance when compared to other existing scheme. For acquisition of CSI, the Basis Expansion (BE) method is used to model the time-varying channels and a Kalman filter is constructed for tracking. The Kalman filter employs decision-feedback from the SD, requiring only a low pilot symbol density, and hence improves bandwidth efficiency. The performance of this scheme is evaluated by Monte-Carlo computer simulations. Results show that compared with competing algorithm, this scheme provides performance gains. When it is used over moderate time-varying LTE channels, this scheme shows good performance even in the presence of carrier phase noise. " --

Orthogonal frequency division multiplexing (OFDM) due to its appealing features, such as robustness against frequency selective fading and simple channel equalization, is adopted in communications systems such as WLAN, WiMAX and DVB. However, OFDM systems are sensitive to synchronization errors caused by timing and frequency offsets. Besides, the OFDM receiver has to perform channel estimation for coherent detection. The goal of this thesis is to investigate new methods for timing and frequency synchronization and channel estimation in OFDM-based systems. First, we investigate new methods for preamble-aided coarse timing estimation in OFDM systems. Two novel timing metrics using high order statistics-based correlation and differential normalization functions are proposed. The performance of the new timing metrics is evaluated using different criteria including class-separability, robustness to the carrier frequency offset, and computational complexity. It is shown that the new timing metrics can considerably increase the class-separability due to their more distinct values at correct and wrong timing instants, and thus give a significantly better detection performance than the existing timing metrics do. Furthermore, a new method for coarse estimation of the start of the frame is proposed, which remarkably reduces the probability of inter-symbol interference (ISI). The improved performances of the new schemes in multipath fading channels are shown by the probabilities of false alarm, missed-detection and ISI obtained through computer simulations. Second, a novel pilot-aided algorithm is proposed for the detection of integer frequency offset (IFO) in OFDM systems. By transforming the IFO into two new integer parameters, the proposed method can largely reduce the number of trial values for the true IFO. The two new integer parameters are detected using two different pilot sequences, a periodic pilot sequence and an aperiodic pilot sequence. It is shown that the new scheme can significantly reduce the computational complexity while achieving almost the same performance as the previous methods do. Third, we propose a method for joint timing and frequency synchronization and channel estimation for OFDM systems that operate in doubly selective channels. 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 the timing and frequency offsets are estimated by using a maximum likelihood (ML) 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, indicating a remarkable performance improvement by the new method. Fourth, we present a new scheme for joint estimation of CFO and doubly selective channel in orthogonal frequency division multiplexing systems. In the proposed preamble-aided method, the time-varying channel is represented using BEM. CFO and BEM coefficients are estimated using the principles of particle and Kalman filtering. The performance of the new method in multipath time-varying channels is investigated in comparison with previous schemes. The simulation results indicate a remarkable performance improvement in terms of the mean square errors of CFO and channel estimates. Fifth, a novel algorithm is proposed for timing and frequency synchronization and channel estimation in the uplink of orthogonal frequency division multiple access (OFDMA) systems by considering high-mobility situations and the generalized subcarrier assignment. By using BEM to represent a doubly selective channel, a maximum likelihood (ML) approach is proposed to jointly estimate the timing and frequency offsets of different users as well as the BEM coefficients of the time-varying channels. A space-alternating generalized expectation-maximization algorithm is then employed to transform the maximization problem for all users into several simpler maximization problems for each user. The computational complexity of the new timing and frequency offset estimator is analyzed and its performance in comparison with that of existing methods using the mean square error is evaluated . Finally, two novel approaches for joint CFO and doubly selective channel estimation in the uplink of multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) systems are presented. Considering high-mobility situations, where channels change within an OFDMA symbol interval, and the time varying nature of CFOs, BEM is employed to represent the time variations of the channel. Two new approaches are then proposed based on Schmidt Kalman filtering (SKF). The first approach utilizes Schmidt extended Kalman filtering for each user to estimate the CFO and BEM coefficients. The second approach uses Gaussian particle filter along with SKF to estimate the CFO and BEM coefficients of each user. The Bayesian Cramer Rao bound is derived, and performance of the new schemes are evaluated using mean square error. It is demonstrated that the new schemes can significantly improve the mean square error performance in comparison with that of the existing methods.

The increasing demand for high data rate transmission over broadband radio channels has imposed significant challenges in wireless communications. Accurate channel estimation has a major impact onthe whole system performance. Specifically, reliable estimate of the channel state information (CSI) is more challenging for orthogonal frequency division multiplexing (OFDM) systems in doubly selective fading channels than for the slower fading channels over which OFDM has been deployed traditionally. With the help of a basis expansion model (BEM), a novel multivariate autoregressive (AR) process is developed to model the time evolution of the fast fading channel. Relying on pilot symbol aided modulation (PSAM), a novel Kalman smoothing algorithm based on a second-order dynamic model is exploited, where the mean square error (MSE) of the channel estimator is near to that of the optimal Wiener filter. To further improve the performance of channel estimation, a novellow-complexity iterative joint channel estimation and symbol detection procedure is developed for fast fading channels with a small number of pilots and low pilot power to achieve the bit errorrate (BER) performance close to when the CSI is known perfectly. The new channel estimation symbol detection technique is robust to variations of the radio channel from the design values and applicable to multiple modulation and coding types. By use of the extrinsic information transfer (EXIT) chart, we investigate the convergence behavior of the new algorithm and analyze the modulation, pilot density, and error correction code selection for good system performance for a given power level. The algorithms developed in this thesis improve the performance of the whole system requiring only low ratios of pilot to data for excellent performance in fast fading channels.

This book introduces the theoretical elements at the basis of various classes of algorithms commonly employed in the physical layer (and, in part, in MAC layer) of wireless communications systems. It focuses on single user systems, so ignoring multiple access techniques. Moreover, emphasis is put on single-input single-output (SISO) systems, although some relevant topics about multiple-input multiple-output (MIMO) systems are also illustrated. Comprehensive wireless specific guide to algorithmic techniques Provides a detailed analysis of channel equalization and channel coding for wireless applications Unique conceptual approach focusing in single user systems Covers algebraic decoding, modulation techniques, channel coding and channel equalisation

Abstract: We propose a factor-graph-based approach to joint channel-estimation-and-decoding (JCED) of bit-interleaved coded orthogonal frequency division multiplexing (BICMOFDM) over a Rayleigh Fading channel. A Basis Expansion model is applied to reduce inter-carrier interference (ICI) caused by channel variation over time. Guard interval structure is introduce to reduce inter-symbol interference (ISI) caused by multi-path delay. In contrast to existing designs, our is capable of exploiting not only sparsity in sampled channel taps but also time variation in both the tap amplitudes and the tap support. In order to exploit these channel-tap structures, we adopt a twostate Gaussian mixture prior in conjunction with a Gauss-Markov model on the tap amplitude trajectories and a discrete Binary Markov model on the tap state trajectories. For loopy belief propagation, we exploit the Generalized Approximate Message Passing algorithm (GAMP) recently developed in the context of compressive sensing, and show that it can be successfully coupled with soft-input soft-output decoding, as well as Markov trajectory inference, through a sum-product framework. We try to find out the optimal scheme to utilize the most of channel capacity as well as getting effective bit error rate (BER).

Frequency-Domain Receiver Design for Doubly-Selective Channels discusses broadband wireless transmission techniques, which are serious candidates to be implemented in future broadband wireless and cellular systems, aiming at providing high and reliable data transmission and concomitantly high mobility. This book provides an overview of the channel impairments that may affect performance of single carrier and multi-carrier block transmission techniques in mobile environments. Moreover, it also provides a new insight into the new receiver designs able to cope with double selectivity that affects present and future broadband high speed mobile communication systems.