OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

Authors:
Yu Gong;Xia Hong
Affiliations:
School of Systems Engineering, The University of Reading, Reading RG6 6AY, UK;School of Systems Engineering, The University of Reading, Reading RG6 6AY, UK
Venue:
Signal Processing
Year:
2009

Citing 3
Cited 1

Bayesian interpolation

Neural Computation
The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM

IEEE Transactions on Signal Processing
Phase Noise Estimation and Mitigation for OFDM Systems

IEEE Transactions on Wireless Communications

OFDM joint data detection and phase noise cancellation for constant modulus modulations

IEEE Transactions on Signal Processing

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Abstract

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of ''overfitting'' such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.