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

Semi-blind CFO, channel estimation and data detection for ofdm systems over doubly selective channels

Proceedings of the IEEE International Symposium on Circuits and Systems, 2010, p. 1887-1890Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 1887-189

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch

Multipath Parameter Estimation from OFDM Signals in Mobile Channels

We study multipath parameter estimation from orthogonal frequency division multiplex signals transmitted over doubly dispersive mobile radio channels. We are interested in cases where the transmission is long enough to suffer time selectivity, but short enough such that the time variation can be accurately modeled as depending only on per-tap linear phase variations due to Doppler effects. We therefore concentrate on the estimation of the complex gain, delay and Doppler offset of each tap of the multipath channel impulse response. We show that the frequency domain channel coefficients for an entire packet can be expressed as the superimposition of two-dimensional complex sinusoids. The maximum likelihood estimate requires solution of a multidimensional non-linear least squares problem, which is computationally infeasible in practice. We therefore propose a low complexity suboptimal solution based on iterative successive and parallel cancellation. First, initial delay/Doppler estimates are obtained via successive cancellation. These estimates are then refined using an iterative parallel cancellation procedure. We demonstrate via Monte Carlo simulations that the root mean squared error statistics of our estimator are very close to the Cramer-Rao lower bound of a single two-dimensional sinusoid in Gaussian noise.Comment: Submitted to IEEE Transactions on Wireless Communications (26 pages, 9 figures and 3 tables

Successive interference cancellation schemes for time-reversal space-time block codes

In this paper, we propose two simple signal detectors that are based on successive interference cancellation (SIC) for time-reversal space-time block codes to combat intersymbol interference in frequency-selective fading environments. The main idea is to treat undetected symbols and noise together as Gaussian noise with matching mean and variance and use the already-detected symbols to help current signal recovery. The first scheme is a simple SIC signal detector whose ordering is based on the channel powers. The second proposed SIC scheme, which is denoted parallel arbitrated SIC (PA-SIC), is a structure that concatenates in parallel a certain number of SIC detectors with different ordering sequences and then combines the soft output of each individual SIC to achieve performance gains. For the proposed PA-SIC, we describe the optimal ordering algorithm as a combinatorial problem and present a low-complexity ordering technique for signal decoding. Simulations show that the new schemes can provide a performance that is very close to maximum-likelihood sequence estimation (MLSE) decoding under time-invariant conditions. Results for frequency-selective and doubly selective fading channels show that the proposed schemes significantly outperform the conventional minimum mean square error-(MMSE) like receiver and that the new PA-SIC performs much better than the proposed conventional SIC and is not far in performance from the MLSE. The computational complexity of the SIC algorithms is only linear with the number of transmit antennas and transmission rates, which is very close to the MMSE and much lower than the MLSE. The PA-SIC also has a complexity that is linear with the number of SIC components that are in parallel, and the optimum tradeoff between performance and complexity can be easily determined according to the number of SIC detectors