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

Abstract

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