TY - GEN
T1 - An analysis of the effect of synaptic weight configuration for a neural network enabled handover for heterogeneous networks
AU - Hayes, Sean
AU - Fallon, Enda
AU - Flynn, Ronan
AU - Murray, Niall
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Traditionally, Received Signal Strength (RSS) has been the primary indicator informing network selection strategies. However, approaches based on RSS are limited as they do not consider how (a) dynamic network conditions and (b) potential predictability of movement affects network performance. The wider research focus analyses the potential effect of weather on network handover decisions. In this context, a modulation strategy typically used in poor weather conditions is chosen and an analysis is done of the relative importance of the key dynamic performance metrics; loss, delay and RSS. In neural networks, synaptic weights reflect the relative importance of each performance metric. This work informs our selection of optimal synaptic weights when implementing a neural network controlled network handover decision within the context of the IEEE 802.21 Media Independent Handover (MIH) standard.
AB - Traditionally, Received Signal Strength (RSS) has been the primary indicator informing network selection strategies. However, approaches based on RSS are limited as they do not consider how (a) dynamic network conditions and (b) potential predictability of movement affects network performance. The wider research focus analyses the potential effect of weather on network handover decisions. In this context, a modulation strategy typically used in poor weather conditions is chosen and an analysis is done of the relative importance of the key dynamic performance metrics; loss, delay and RSS. In neural networks, synaptic weights reflect the relative importance of each performance metric. This work informs our selection of optimal synaptic weights when implementing a neural network controlled network handover decision within the context of the IEEE 802.21 Media Independent Handover (MIH) standard.
KW - Artificial neural network
KW - Heterogeneous networking
KW - Media independent handover
UR - http://www.scopus.com/inward/record.url?scp=84926679173&partnerID=8YFLogxK
U2 - 10.1109/UKSim.2014.58
DO - 10.1109/UKSim.2014.58
M3 - Conference contribution
AN - SCOPUS:84926679173
SN - 9781479949236
T3 - Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014
SP - 479
EP - 484
BT - Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014
A2 - Yunus, Jasmy
A2 - Cant, Richard
A2 - Saad, Ismail
A2 - Al-Dabass, David
A2 - Ibrahim, Zuwairie
A2 - Orsoni, Alessandra
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th UKSim-AMSS International Conference on Computer Modelling and Simulation, UKSim 2014
Y2 - 26 March 2014 through 28 March 2014
ER -