Near ML detection using Dijkstra's algorithm with bounded list size over MIMO channels

Simple item page
dc.creatorOkawado, Atsushi
dc.creatorMatsumoto, Ryutaroh
dc.creatorUyematsu, Tomohiko
dc.date2008-02-13
dc.date.accessioned2026-07-07T09:59:33Z
dc.date.available2026-07-07T09:59:33Z
dc.descriptionWe propose Dijkstra's algorithm with bounded list size after QR decomposition for decreasing the computational complexity of near maximum-likelihood (ML) detection of signals over multiple-input-multiple-output (MIMO) channels. After that, we compare the performances of proposed algorithm, QR decomposition M-algorithm (QRD-MLD), and its improvement. When the list size is set to achieve the almost same symbol error rate (SER) as the QRD-MLD, the proposed algorithm has smaller average computational complexity.
dc.description8 pages, two column, 14 figures, LaTeX2e
dc.identifierhttps://arxiv.org/abs/0802.1785
dc.identifierhttp://arxiv.org/abs/0802.1785
dc.identifierProceedings of 2008 IEEE International Symposium on Information Theory, pp. 2022-2025, 2008
dc.identifierdoi:10.1109/ISIT.2008.4595344
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/168061
dc.subjectInformation Theory
dc.titleNear ML detection using Dijkstra's algorithm with bounded list size over MIMO channels
dc.typetext

Files

Collections

arXiv