Title :
Using artificial neural networks to improve performance in a wireless ATM network
Author :
Lohi, Mahboubeh ; Madani, K. ; Weerakoon, D. ; Aghvami, A.H.
Author_Institution :
Dept. of Electron. Eng., King´´s Coll., London, UK
Abstract :
This paper examines some of the limitations of existing handover schemes and the potential performance and efficiency gains that can be achieved through hybrid variants that utilise neural networks. The virtual connection tree (VCT) is chosen as a candidate handover scheme because it statistically demonstrates a fast handover methodology that uses resource pre-allocation to maintain the QoS requirements for wireless ATM traffic. A hybrid multicast re-routing; scheme is introduced which aims to utilise the available bandwidth more efficiently and consequently to handle a higher number of concurrent calls
Keywords :
asynchronous transfer mode; cellular radio; learning (artificial intelligence); multicast communication; neural nets; packet radio networks; quality of service; telecommunication computing; telecommunication network routing; telecommunication traffic; ANN; QoS requirements; artificial neural networks; bandwidth utilisation; cellular radio; efficiency; fast handover method; handover schemes; hybrid multicast re-routing; macrocellular systems; microcellular systems; neural network training; new call blocking probability; performance improvement; resource pre-allocation; supervised learning; virtual connection tree; wireless ATM network; wireless ATM traffic; Artificial neural networks; Bandwidth; Base stations; Educational institutions; Electronic mail; Intelligent networks; Mobile communication; Quality of service; Wireless communication; Wireless networks;
Conference_Titel :
Vehicular Technology Conference, 1999 IEEE 49th
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-5565-2
DOI :
10.1109/VETEC.1999.778400