DocumentCode :
2491230
Title :
Binary versus symbolic performance prediction methods for iterative MMSE-IC multiuser MIMO joint decoding
Author :
Visoz, Raphaël ; Lalam, Massinissa ; Berthet, Antoine O.
Author_Institution :
Orange Labs., Issy-les-Moulineaux, France
fYear :
2009
fDate :
21-24 June 2009
Firstpage :
131
Lastpage :
135
Abstract :
This paper presents a dual mode fast performance prediction method for iterative minimum-mean square error interference cancellation based joint decoding in a very general multiuser MIMO setting, considering a transmission over block-fading frequency-selective channels. The symbolic mode relies on a signal to interference-plus-noise ratio compression at symbol level, by means of the mutual information effective signal-to-noise metric (MIESM), bridging the gap between the extrinsic information transfer (EXIT) charts and the compression methods developed for link-to-system interface. The binary mode follows the classical EXIT chart approach relying on a new analytical formula of the bit interleaved coded modulation average mutual information in presence of non-uniform a priori on input bits. This paper demonstrates the superiority of the symbolic mode for high order modulation.
Keywords :
MIMO communication; fading channels; interference suppression; interleaved codes; iterative decoding; least mean squares methods; modulation coding; multiuser channels; MMSE-IC multiuser MIMO joint decoding; bit interleaved coded modulation; block-fading frequency-selective channels; extrinsic information transfer; interference cancellation; iterative decoding; minimum mean square error methods; mutual information effective signal-to-noise metric; Frequency; Information analysis; Interference cancellation; Interleaved codes; Iterative decoding; Iterative methods; MIMO; Modulation coding; Mutual information; Prediction methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Advances in Wireless Communications, 2009. SPAWC '09. IEEE 10th Workshop on
Conference_Location :
Perugia
Print_ISBN :
978-1-4244-3695-8
Electronic_ISBN :
978-1-4244-3696-5
Type :
conf
DOI :
10.1109/SPAWC.2009.5161761
Filename :
5161761
Link To Document :
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