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Vienna University of Technology (VUT 24) in Cluster 2 “Signal Processing for MIMO Systems”. Gerald Matz, Franz Hlawatsch, and Dominik Seethaler. Outline. Our focus in MIMO signal processing Offers for cooporation Example of joint activity of CNRS (19) and VUT (24).
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Vienna University of Technology (VUT 24)in Cluster 2 “Signal Processing for MIMO Systems” Gerald Matz, Franz Hlawatsch, and Dominik Seethaler
Outline • Our focus in MIMO signal processing • Offers for cooporation • Example of joint activity of CNRS (19) and VUT (24)
Our Focus in MIMO Signal Processing • MIMO receivers: • Efficient detection algorithms • Efficient soft demodulation algorithms for MIMO-BICM • Channel estimation • MIMO transmission: • Efficient precoding algorithms • Space-Time Coding • MIMO signal processing with mismatched CSI
Possible Cooporation (1) Efficient detection and precoding algorithms for MIMO-OFDM: • Algorithms are usually designed for flat-fading and are based on knowledge of channel realization • Straightforward extension to MIMO-OFDM: Apply these algorithms for each subcarrier • However, subcarriers are strongly correlated • Goal: Exploit these correlations to reduce the computational complexity of detection, demodulation and precoding algorithms • In particular, complexity could be strongly reduced for algorithms with many „channel computations“ (e.g. LLL-based schemes)
Possible Cooporation (2) „Line search detection“ (LSD) for precoding and demodulation • Conventional efficient (sub)optimal detection schemes fail in the case of ill-conditioned (i.e. „bad“) channel realizations • The LSD (and other versions of it) can efficiently achieve near-ML performance by being robust to of bad channels • Goal: Extend the LSD principle to precoding and soft demodulation • First result: ICC05
Possible Cooporation (3) Efficient detection algorithms for higher order modulation: • Many detection algorithms for MIMO systems emerged from multiuser detection just considering BPSK (or 4-QAM) modulation • No efficient detection algorithms with near-ML performance exist for higher order modulation (16-QAM, 64-QAM …) • Goal: Development of efficient near-ML detection algorithms tailored to higher order modulation
Cluster 2: Joint Activity of CNRS and VUT Analysis and design of MIMO Transceivers with mismatched CSI • Goals: • Information theoretic analysis of MIMO transmission with mismatched CSI • Development of corresponding transeiver design guidelines (signaling, decoding, etc…) • People involved: • Samson Lasaulce and Pablo Piantanida (CNRS/LSS), • Gerald Matz (VUT) • Planned exchanges: • Pablo at VUT from mid-May to mid-August • Samson at VUT from mid-July to mid-August • Gerald at LSS for two weeks in September