Reducing the average complexity of ML detection using semidefinite relaxation
2005 (English)In: 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2005, 1021-1024 p.Conference paper (Refereed)
Maximum likelihood (ML) detection of symbols transmitted over a MIMO channel is generally a difficult problem due to its NP-hard nature. However, not every instance of the detection problem is equally hard. Thus, the average complexity of an ML detector may be significantly smaller than its worst-case counterpart. This is typically true in the high SNR regime where the received signals are closer to the noise free transmitted signals. Herein, a method which may be used to lower the average complexity of any ML detector is proposed. The method is based on the ability to verify if a symbol estimate is ML, using an optimality condition provided by the near-ML semidefinite relaxation technique. The average complexity reduction advantage of the proposed method is confirmed by numerical results.
Place, publisher, year, edition, pages
2005. 1021-1024 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Detection algorithms, Detectors, MIMO, Maximum likelihood decoding, Maximum likelihood detection, Maximum likelihood estimation, Sensor systems, Signal to noise ratio, Testing, Vectors
Signal Processing Telecommunications
IdentifiersURN: urn:nbn:se:kth:diva-34976DOI: 10.1109/ICASSP.2005.1415886ISI: 000229404202256ScopusID: 2-s2.0-33646793650ISBN: 0-7803-8874-7OAI: oai:DiVA.org:kth-34976DiVA: diva2:426970
30th IEEE International Conference on Acoustics, Speech, and Signal Processing Philadelphia, PA, MAR 19-23, 2005
QC 201106272011-06-272011-06-172012-01-23Bibliographically approved