Algorithms for Joint Phase Estimation and Decoding for MIMO Systems in the Presence of Phase Noise and Quasi-Static Fading Channels
Journal article, 2015

In this work, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators. As in single-antenna systems, the computation of the optimum receiver is an analytically intractable problem and is unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. Our first algorithm is obtained by means of the sum-product algorithm, where we use the multivariate Tikhonov canonical distribution approach. In our next algorithm, we derive an approximate MAP symbol detector based on the smoother-detector framework, wherein the detector is properly designed by incorporating the phase noise statistics from the smoother. The third algorithm is derived based on the variational Bayesian framework. By simulations, we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions, and we observe that the proposed techniques significantly outperform the other important algorithms from prior works, which are considered in this work. Index Terms – Maximum a posteriori (MAP) detection, phase noise, sum-product algorithm (SPA), variational Bayesian (VB) framework, extended Kalman smoother (EKS), MIMO.

phase noise

Extended Kalman smoother (EKS)

maximum a posteriori (MAP) detection

MIMO

sum-product algorithm (SPA)

variational Bayesian (VB) framework

Author

Rajet Krishnan

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Giulio Colavolpe

University of Parma

Alexandre Graell i Amat

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Thomas Eriksson

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 63 13 3360-3375 7080901

Signal Recovery: Compressed Sensing meets Coding Theory

Swedish Research Council (VR), 2012-01-01 -- 2015-12-31.

Subject Categories

Telecommunications

DOI

10.1109/TSP.2015.2420533

More information

Latest update

3/19/2018