OFDM joint data detection and phase noise cancellation for constant modulus modulations
IEEE Transactions on Signal Processing
A variational inference framework for soft-in soft-out detection in multiple-access channels
IEEE Transactions on Information Theory
Recursive channel estimation algorithms for iterative receiver in MIMO-OFDM systems
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 1
Adaptive iterative detectors for phase-uncertain channels via variational bounding
IEEE Transactions on Communications
Decision-directed least-squares phase perturbation compensation in OFDM systems
IEEE Transactions on Wireless Communications
MRC detection over SIMO OFDM systems in the peresence of phase noise
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Low complexity semi-blind Bayesian iterative receiver for MIMO-OFDM systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Low complexity variational bayes iterative receiver for MIMO-OFDM systems
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Code-aided maximum-likelihood ambiguity resolution through free-energy minimization
IEEE Transactions on Signal Processing
MCMC sampling for joint estimation of phase distortions and transmitted symbols in OFDM systems
Digital Signal Processing
| Hi-index | 35.75 |
This paper studies the mitigation of phase noise (PHN) in orthogonal frequency-division multiplexing (OFDM) data detection. We present a systematic probabilistic framework that leads to both optimal and near-optimal OFDM detection schemes in the presence of unknown PHN. In contrast to the conventional approach that cancels the common (average) PHN, our aim is to jointly estimate the complete PHN sequence and the data symbol sequence. We derive a family of low-complexity OFDM detectors for this purpose. The theoretical foundation on which these detectors are based is called variational inference, an approximate probabilistic inference technique associated with the minimization of variational free energy. In deriving the proposed schemes, we also point out that the expectation-maximization algorithm is a special case of the variational-inference-based joint estimator. Further complexity reduction is obtained using the conjugate gradient (CG) method, and only a few CG iterations are needed to closely approach the ideal joint estimator output