TY - JOUR
T1 - Joint data detection and channel tracking for OFDM systems with phase noise
AU - Stefanatos, Stelios
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
Manuscript received May 8, 2007; revised March 27, 2008. First published May 23, 2008; last published August 13, 2008 (projected). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Manuel Davy. This work was supported in part by the project “EPEAEK II—PYTHAGORAS II—Support to Research Groups in Universities” and by the European Social Fund and Greek National Resources. The material in this paper was presented in part at the8th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Helsinki, Finland, June2007 .
PY - 2008
Y1 - 2008
N2 - This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.
AB - This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.
KW - Channel tracking
KW - Joint detection-estimation
KW - Orthogonal frequency division multiplexing (OFDM)
KW - Phase noise
KW - Variational Bayes method
UR - http://www.scopus.com/inward/record.url?scp=65449186303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65449186303&partnerID=8YFLogxK
U2 - 10.1109/TSP.2008.925968
DO - 10.1109/TSP.2008.925968
M3 - Article
AN - SCOPUS:65449186303
SN - 1053-587X
VL - 56
SP - 4230
EP - 4243
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 9
ER -