Pilot-symbol-aided iterative channel estimation for OFDM-based systems

13th European Signal Processing Conference, EUSIPCO 2005; Antalya; Turkey; 4 September 2005 through 8 September 2005In this paper, we propose a pilot-symbol-aided iterative channel estimation for coded OFDM-based systems. We use the symbol APP provided by the channel decoder to form groups of virtual pilots. According to their reliabilities, we combine these groups to improve the channel estimation. We also compare the proposed algorithm with the EM algorithm. Orthogonal frequency division multiplexing (OFDM) based systems are strong candidates for an air interface of future fourth-generation mobile wireless systems which provide high data rates and high mobility. In order to achieve the potential advantages of OFDM-based systems, the channel coefficients should be estimated with minimum error. The channel estimation can be improved using more pilot symbols [1]. However, it causes data rate reduction or bandwidth expansion. Therefore, spectrally efficient channel estimation techniques should be considered. In this case, the iterative techniques provide an improvement on the channel estimator performance without requiring additional pilots

Orthogonal STBC MC-CDMA system with channel estimation over realistic high mobility MIMO channels

Non

EM-based Enhancement of the Wiener Pilot-aided Channel Estimation in MIMO-OFDM Systems

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Iterative (turbo processing) receiver design of OFDM systems in the presence of carrier frequency offset.

In this paper, based on the principle of turbo processing, we propose two iterative receiver schemes for carrier fre- quency offset (CFO) compensation in orthogonal frequency division multiplexing (OFDM) systems. Our CFO compensation designs, one in time domain and the other in frequency domain, are based on joint estimation of time-varying channel and CFO. In our schemes, the random CFO problem, a challenge for conventional pilot-aid methods, can be effectively solved using iter- ative (turbo processing) schemes. Furthermore, our comparative study shows that time domain compensation (TDC) is simpler to implement but frequency domain cancellation consisting of an iterative equalizer (FDC-IE) has better bit error rate (BER) performance

Advanced Statistical Signal Processing Methods in Sensing, Detection, and Estimation for Communication Applications

The applications of wireless communications and digital signal processing have dramatically changed the way we live, work, and learn over decades. The requirement of higher throughput and ubiquitous connectivity for wireless communication systems has become prevalent nowadays. Signal sensing, detection and estimation have been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal sensing, detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal sensing, detection and estimation schemes for wireless communications applications, such as spectrum sensing, symbol-detection/channel-estimation, and encoder identification. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Implementation of a testbed for MISO OFDM communication systems.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.The thesis presents an implementation of a multiple input single output orthogonal frequency division multiplex (MISO OFDM) communication system testbed. The project was developed in order to evaluate whether the channel estimation algorithms developed by Dr Oyerinde [1] could be implemented in a real time communication system that uses today’s technology. This implementation based validation would help determine the practicality of algorithms and methods that promise better performance for communication systems from a simulation point of view. The benefits of using multiple orthogonal carriers are discussed as well as how an OFDM system works. The benefits of using multiple antennas at the transmitter, as opposed to using just one, are also discussed. The Alamouti scheme which allows space diversity to be achieved without the cost of having a lower data rate is presented. Modules common to all communication systems, such as those dedicated to synchronization, channel estimation, symbol detection and channel coding, are discussed. The different methods of synchronization for OFDM communication systems are presented and compared. The channel estimation algorithm developed by Dr Oyerinde is presented and is adopted for an indoor channel. Most of the system blocks and parameters used in the testbed are the same as those used in [1] in order to easily compare the results obtained by simulation and those obtained by implementation. The system bandwidth required for the project was too high for the processor chosen for the testbed. A qualitative evaluation of the practicality of Dr Oyerinde’s channel estimation algorithms was performed instead. From this evaluation it was derived that Dr Oyerinde’s non-iterative decision directed channel estimation algorithm was more suitable for real time non-iterative decision directed channel estimation communication systems than for iterative versions. Apart from processing demands that couldn’t be met, the other aspects of the project were implemented successfully

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Channel estimation for SISO and MIMO OFDM communications systems.

Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2010.Telecommunications in the current information age is increasingly relying on the wireless link. This is because wireless communication has made possible a variety of services ranging from voice to data and now to multimedia. Consequently, demand for new wireless capacity is growing rapidly at a very alarming rate. In a bid to cope with challenges of increasing demand for higher data rate, better quality of service, and higher network capacity, there is a migration from Single Input Single Output (SISO) antenna technology to a more promising Multiple Input Multiple Output (MIMO) antenna technology. On the other hand, Orthogonal Frequency Division Multiplexing (OFDM) technique has emerged as a very popular multi-carrier modulation technique to combat the problems associated with physical properties of the wireless channels such as multipath fading, dispersion, and interference. The combination of MIMO technology with OFDM techniques, known as MIMO-OFDM Systems, is considered as a promising solution to enhance the data rate of future broadband wireless communication Systems. This thesis addresses a major area of challenge to both SISO-OFDM and MIMO-OFDM Systems; estimation of accurate channel state information (CSI) in order to make possible coherent detection of the transmitted signal at the receiver end of the system. Hence, the first novel contribution of this thesis is the development of a low complexity adaptive algorithm that is robust against both slow and fast fading channel scenarios, in comparison with other algorithms employed in literature, to implement soft iterative channel estimator for turbo equalizer-based receiver for single antenna communication Systems. Subsequently, a Fast Data Projection Method (FDPM) subspace tracking algorithm is adapted to derive Channel Impulse Response Estimator for implementation of Decision Directed Channel Estimation (DDCE) for Single Input Single Output - Orthogonal Frequency Division Multiplexing (SISO-OFDM) Systems. This is implemented in the context of a more realistic Fractionally Spaced-Channel Impulse Response (FS-CIR) channel model, as against the channel characterized by a Sample Spaced-Channel Impulse Response (SS)-CIR widely assumed by other authors. In addition, a fast convergence Variable Step Size Normalized Least Mean Square (VSSNLMS)-based predictor, with low computational complexity in comparison with others in literatures, is derived for the implementation of the CIR predictor module of the DDCE scheme. A novel iterative receiver structure for the FDPM-based Decision Directed Channel Estimation scheme is also designed for SISO-OFDM Systems. The iterative idea is based on Turbo iterative principle. It is shown that improvement in the performance can be achieved with the iterative DDCE scheme for OFDM system in comparison with the non iterative scheme. Lastly, an iterative receiver structure for FDPM-based DDCE scheme earlier designed for SISO OFDM is extended to MIMO-OFDM Systems. In addition, Variable Step Size Normalized Least Mean Square (VSSNLMS)-based channel transfer function estimator is derived in the context of MIMO Channel for the implementation of the CTF estimator module of the iterative Decision Directed Channel Estimation scheme for MIMO-OFDM Systems in place of linear minimum mean square error (MMSE) criterion. The VSSNLMS-based channel transfer function estimator is found to show improved MSE performance of about -4 MSE (dB) at SNR of 5dB in comparison with linear MMSE-based channel transfer function estimator