Hakan Dogan

Nondata-aided channel estimation for OFDM systems with space-frequency transmit diversity

This paper proposes a computationally efficient nondata-aided maximum a posteriori (MAP) channel-estimation algorithm focusing on the space-frequency (SF) transmit diversity orthogonal frequency division multiplexing (OFDM) transmission through frequency-selective channels. The proposed algorithm properly averages out the data sequence and requires a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates the complex channel parameters of each subcarrier iteratively, using the expectation maximization (EM) method. To further reduce the computational complexity of the proposed MAP algorithm, the optimal truncation property of the KL expansion is exploited. The performance of the MAP channel estimator is studied based on the evaluation of the modified Cramer-Rao bound (CRB). Simulation results confirm the proposed theoretical analysis and illustrate that the proposed algorithm is capable of tracking fast fading and improving overall performance.

Iterative Channel Estimation and Decoding of Turbo Coded SFBC-OFDM Systems

We consider the design of turbo receiver structures for space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in the presence of unknown frequency and time selective fading channels. The turbo receiver structures for SFBC-OFDM systems under consideration consists of an iterative MAP expectation/maximization (EM) channel estimation algorithm, soft MMSE-SFBC decoder and a soft MAP outer-channel-code decoder. MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The optimal truncation property of the KL expansion can reduce computational load on the iterative estimation approach. The performance of the proposed approaches are studied in terms of mean square error and bit-error rate. Through computer simulations, the effect of a pilot spacing on the channel estimator performance and sensitivity of turbo receiver structures on channel estimation error are studied. Simulation results illustrate that receivers with turbo coding are very sensitive to channel estimation errors compared to receivers with convolutional codes. Moreover, superiority of the turbo coded SFBC-OFDM systems over the turbo coded STBC-OFDM systems is observed especially for high Doppler frequencies.

Low-Complexity MAP-Based Successive Data Detection for Coded OFDM Systems Over Highly Mobile Wireless Channels

This paper is concerned with the challenging and timely problem of data detection for coded orthogonal frequency-division multiplexing (OFDM) systems in the presence of frequency-selective and very rapidly time varying channels. New low-complexity maximum a posteriori probability (MAP) data detection algorithms are proposed based on sequential detection with optimal ordering (SDOO) and sequential detection with successive cancellation (SDSC). The received signal vector is optimally decomposed into reduced dimensional subobservations by exploiting the banded structure of the frequency-domain channel matrix whose bandwidth is a parameter to be adjusted according to the speed of the mobile terminal. The data symbols are then detected by the proposed algorithms in a computationally efficient way by means of the Markov chain Monte Carlo (MCMC) technique with Gibbs sampling. The impact of the imperfect channel state information (CSI) on the bit error rate (BER) performance of these algorithms is investigated analytically and by computer simulations. A detailed computational complexity investigation and simulation results indicate that, particularly, the algorithm based on SDSC has significant performance and complexity advantages and is very robust against channel estimation errors compared with existing suboptimal detection and equalization algorithms proposed earlier in the literature.

Low-complexity joint data detection and channel equalisation for highly mobile orthogonal frequency division multiplexing systems

This study is concerned with the challenging and timely problem of channel equalisation and data detection for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency-selective and very rapidly time-varying channels. The algorithm is based on the space alternating generalised expectation-maximisation (SAGE) technique which is particularly well suited to multicarrier signal formats and can be easily extended to multi-input multi-output-OFDM systems. In fast fading channels, the orthogonality between subcarriers is destroyed by the time variation of a fading channel over an OFDM symbol duration which causes severe inter-carrier interference (ICI) and, in conventional frequency-domain approaches, results in an irreducible error floor. The proposed joint data detection and equalisation algorithm updates the data sequences in series leading to a receiver structure that also incorporates ICI cancellation, enabling the system to operate at high vehicle speeds. A computational complexity investigation as well as detailed computer simulations indicate that this algorithm has significant performance and complexity advantages over existing suboptimal detection and equalisation algorithms proposed earlier in the literature.

MAP channel-estimation-based PIC receiver for downlink MC-CDMA systems

We propose a joint MAP channel estimation and data detection technique based on the expectation maximization (EM) method with paralel interference cancelation (PIC) for downlink multicarrier (MC) code division multiple access (CDMA) systems in the presence of frequency selective channels. The quality of multiple access interference (MAI), which can be improved by using channel estimation and data estimation of all active users, affects considerably the performance of PIC detector. Therefore, data and channel estimation performance obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. The EM algorithm derived estimates the complex channel parameters of each subcarrier iteratively and generates the soft information representing the data a posterior probabilities. The soft information is then employed in a PIC module to detect the symbols efficiently. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach is studied in terms of bit-error rate (BER) and mean square error (MSE). Throughout the simulations, extensive comparisons with previous works in literature are performed, showing that the new scheme can offer superior performance.

Full length article: Iterative joint data detection and channel estimation for uplink MC-CDMA systems in the presence of frequency selective channels

This paper is concerned with joint multiuser detection and multichannel estimation (JDE) for uplink multicarrier code-division multiple-access (MC-CDMA) systems in the presence of frequency selective channels. The detection and estimation, implemented at the receiver, are based on a version of the expectation maximization (EM) algorithm and the space-alternating generalized expectation-maximization (SAGE) which are very suitable for multicarrier signal formats. The EM-JDE receiver updates the data bit sequences in parallel, while the SAGE-JDE receiver reestimates them successively. The channel parameters are updated in parallel in both schemes. Application of the EM-based algorithm to the problem of iterative data detection and channel estimation leads to a receiver structure that also incorporates a partial interference cancelation. Computer simulations show that the proposed algorithms have excellent BER end estimation performance.

Maximum a posteriori channel estimation for cooperative diversity orthogonal frequencydivision multiplexing systems in amplify-andforward mode

Transmit diversity-orthogonal frequency-division multiplexing (OFDM) systems have been proposed to mitigate the detrimental effects of channel fading. However, owing to the space and power limitations, the use of multiple transmit antennas is not practical in certain wireless devices, such as portable terminals and wireless sensors. Therefore cooperation among users at the physical layer has been proposed recently. Here, space-time block coded in amplify-and-forward (AF) relaying mode has been proposed as cooperative diversity for OFDM systems (CO-OFDM) in the presence of perfect channel-state information. Then, the channel estimation techniques for CO-OFDM systems in AF mode based on pilot symbols are investigated over frequency-selective channels. In particular, expectation-maximisation (EM) based maximum a posteriori (MAP) channel estimation is developed and compared with comp-type pilot-aided channel estimation (PACE) based the maximum likelihood (ML) estimator and the least minimum mean-square error (LMMSE) channel estimation techniques for CO-OFDM systems. To overcome the drawback owing to the receiver complexity, the Karhunen-Loeve expansion with the optimal truncation property is also considered. Simulation results that demonstrate the overall performance advantage of the EM-MAP based receiver over the PACE-ML and PACE-LMMSE based receivers are presented.

A Gibbs Sampling Based MAP Detection Algorithm for OFDM over Rapidly Varying Mobile Radio Channels

In orthogonal frequency-division multiplexing (OFDM) systems operating over rapidly time-varying channels, the orthogonality between subcarriers is destroyed leading to inter-carrier interference (ICI) and resulting in an irreducible error floor. In this paper, a new and low-complexity maximum a posteriori probability (MAP) detection algorithm is proposed for OFDM systems operating over rapidly time-varying multipath channels. The detection algorithm exploits the banded structure of the frequency-domain channel matrix whose bandwidth is a parameter to be adjusted according to the speed of the mobile terminal. Based on this assumption, the received signal vector is decomposed into reduced dimensional sub-observations in such a way that all components of the observation vector contributing to the symbol to be detected are included in the decomposed observation model. The data symbols are then detected by the MAP algorithm by means of a Markov chain Monte Carlo (MCMC) technique in an optimal and computationally efficient way. Computational complexity investigation as well as simulation results indicate that this algorithm has significant performance and complexity advantages over existing suboptimal detection and equalization algorithms proposed earlier in the literature.

EM/SAGE Based ML Channel Estimation for Uplink DS-CDMA Systems over Time-Varying Fading Channels

The matrix inversion for the maximum likelihood (ML) channel estimation requires high complexity for the direct-sequence code-division multiple-access (DS-CDMA) systems. The prime motivation of the paper is to propose channel estimators that achieve mean square error (MSE) performance of ML channel estimator in an iterative manner without any matrix inversion. Therefore, two computationally efficient solutions to the problem of ML channel estimation are proposed.We compare the both algorithms in terms of the number of used iteration and show that the proposed algorithms converge the same MSE performance of the ML estimator as the increasing number of iterations.

Channel estimation for OFDM systems with high mobility fading channels

In fading channels with very high mobility, the time variation of channel over an orthogonal frequency-division multiplexing (OFDM) symbol period results in a loss of sub-channel orthogonality which leads to inter-carrier interference (ICI). Receivers based on conventional estimation techniques that assume time-invariant channel for one OFDM symbol has error floor for high mobility cases. In this paper we present two-dimensional (2D) pilot-symbol assisted channel estimation for wireless OFDM . This linear interpolation algorithm has the advantage of minimizing the system complexity and processing delay while giving a good approximation to real mobile channel. The performance of the 2D frequency domain estimation algorithm is compared to coherent modulation with perfect channel estimation as well as other conventional methods. We also investigate the performance of proposed channel estimation for different detection techniques. Therefore, we demonstrate the importance of detection methods to assess the channel estimation performance.