Multi-input multi-output fading channel tracking and equalization using Kalman estimation

By Christos Komninakis, Christina Fragouli, Ali H. Sayed, Richard D. Wesel. IEEE Transactions on Signal Processing, 2002

485 citations recorded by the source corpus.

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-varying frequency-selective channels. These channels model the effects of inter-symbol interference (ISI), co-channel interference (CCI), and noise. A low-order autoregressive model approximates the MIMO channel variation and facilitates tracking via a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay /spl Delta/ > 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multiuser fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ffef7a7fca554c33af3d337b8145c0eef54e45a3

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Citations in the corpus

Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory (2013, background): “In temporally correlated channels, a Kalman ﬁlter or particle ﬁlter can be used at the user to track the channel variation between the training signal intervals [19], [20].”
OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting (2003, methodology): “Komninakis, Fragouli, Sayed and Wesel [103] Fading channel tracking and equalization was proposed for employment in MIMO systems assisted by Kalman estimation and channel prediction.”
A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems (2005, background): “The multipath coefficient vectors are assumed to be independent AR processes following where is typically diagonal with nonzero elements computed according to a nominal Doppler spread [18].”
Time-variant channel estimation using discrete prolate spheroidal sequences (2005, methodology): “In [1] and [2], the autocorrelation of the channel, i.e., its second-order statistic, is assumed to be known, and a Kalman filter or a Karhunen–Loéve transform are applied.”
Great expectations: the value of spatial diversity in wireless networks (2004, background): “While some encouraging steps have been made in this direction [162], [288], the allowable Doppler rates (which depend on the mobile speed and carrier frequency) for high performance are still quite limited.”