TY - GEN
T1 - Site specific knowledge for improving frequency allocations in wireless LAN and cellular networks
AU - Chen, Jeremy K.
AU - Rappaport, Theodore S.
AU - De Veciana, Gustavo
PY - 2007
Y1 - 2007
N2 - This paper is the first analytical work to exhibit the substantial gains resulting from applying site specific knowledge to frequency allocation in wireless networks. Two new site-specific knowledge-based frequency allocation algorithms are shown to outperform all other published work. Site specific knowledge refers to knowledge of building layouts, the locations and electrical properties of APs, users, and physical objects. We assume that a central network controller communicates with all APs, and has site specific knowledge which enables the controller to predict, a priori, the received power from any transmitter to any receiver. Optimal frequency assignments are based on predicted powers to minimize interference and maximize throughput. Our algorithms consistently yield high throughput gains irrespective of network topology, AP activity level, and the number of APs, rogue interferers, and available channels. Our algorithms outperform the best published algorithm by up to 3.68%, 8.95%, 13.6%, 15.1%, 25.8%, and 84.9% for 50, 25, 20, 15, 10, and 5 percentiles of user throughputs, respectively.
AB - This paper is the first analytical work to exhibit the substantial gains resulting from applying site specific knowledge to frequency allocation in wireless networks. Two new site-specific knowledge-based frequency allocation algorithms are shown to outperform all other published work. Site specific knowledge refers to knowledge of building layouts, the locations and electrical properties of APs, users, and physical objects. We assume that a central network controller communicates with all APs, and has site specific knowledge which enables the controller to predict, a priori, the received power from any transmitter to any receiver. Optimal frequency assignments are based on predicted powers to minimize interference and maximize throughput. Our algorithms consistently yield high throughput gains irrespective of network topology, AP activity level, and the number of APs, rogue interferers, and available channels. Our algorithms outperform the best published algorithm by up to 3.68%, 8.95%, 13.6%, 15.1%, 25.8%, and 84.9% for 50, 25, 20, 15, 10, and 5 percentiles of user throughputs, respectively.
UR - http://www.scopus.com/inward/record.url?scp=47649131835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47649131835&partnerID=8YFLogxK
U2 - 10.1109/VETECF.2007.305
DO - 10.1109/VETECF.2007.305
M3 - Conference contribution
AN - SCOPUS:47649131835
SN - 1424402646
SN - 9781424402649
T3 - IEEE Vehicular Technology Conference
SP - 1431
EP - 1435
BT - 2007 IEEE 66th Vehicular Technology Conference, VTC 2007-Fall
T2 - 2007 IEEE 66th Vehicular Technology Conference, VTC 2007-Fall
Y2 - 30 September 2007 through 3 October 2007
ER -