TY - GEN
T1 - Statistical learning and layered space-time architecture for point-to-point wireless communications
AU - Sellathurai, Mathini
AU - Haykin, Simon
PY - 1998
Y1 - 1998
N2 - The BLAST architecture has been proposed for high-capacity and spectrally-efficient wireless communications in an indoor environment. The method relies on multi-transmit and receive antennas to send and receive information-bearing signals in parallel. The architecture assumes a rich independent-ray scattering mechanism to make the parallel information separable at the receiving ends. In practice, with the increased number of parallel sub-streams, the scattering may be less favorable so that signal decoding algorithms are needed. In this paper, we propose a statistical learning demodulating scheme for this task.
AB - The BLAST architecture has been proposed for high-capacity and spectrally-efficient wireless communications in an indoor environment. The method relies on multi-transmit and receive antennas to send and receive information-bearing signals in parallel. The architecture assumes a rich independent-ray scattering mechanism to make the parallel information separable at the receiving ends. In practice, with the increased number of parallel sub-streams, the scattering may be less favorable so that signal decoding algorithms are needed. In this paper, we propose a statistical learning demodulating scheme for this task.
UR - http://www.scopus.com/inward/record.url?scp=0032285014&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.1998.751429
DO - 10.1109/ACSSC.1998.751429
M3 - Conference contribution
AN - SCOPUS:0032285014
SN - 0780351487
T3 - Conference Record of the Asilomar Conference on Signals, Systems and Computers
SP - 1084
EP - 1088
BT - Conference Record of the Asilomar Conference on Signals, Systems and Computers
PB - IEEE
ER -