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

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

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