TY - GEN
T1 - Deep Spatiotemporal Partially Overlapping Channel Allocation
T2 - 2018 IEEE Global Communications Conference, GLOBECOM 2018
AU - Tang, Fengxiao
AU - Mao, Bomin
AU - Fadlullah, Zubair Md
AU - Kato, Nei
N1 - Funding Information:
ACKNOWLEDGEMENT This work was partly supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 16H05858.
PY - 2018
Y1 - 2018
N2 - The high-speed transmission has become extremely important with the rapid growth of network traffic in wireless networks. Because the available bandwidth of wireless channels are limited, Partially Overlapping Channels (POCs) are widely used in wireless networks to maximize the utilization of channel resources. However, with the traffic patterns of wireless networks becoming huge and dynamic, conventional POC assignment algorithms only designed for constantly generated network traffic are not suitable for the new generation wireless networks. Therefore, in this article, a joint deep Covolutional Neural Network (CNN) and activity vector based intelligent channel assignment algorithm is proposed, which is referred to as CNNAV. With the proposed CNNV approach, the network can learn from the historical traffic patterns and intelligently assign POCs to wireless links. The simulation result shows that, the network performance of our proposal in terms of both packets loss rate and network throughput are better than conventional POC assignment algorithms.
AB - The high-speed transmission has become extremely important with the rapid growth of network traffic in wireless networks. Because the available bandwidth of wireless channels are limited, Partially Overlapping Channels (POCs) are widely used in wireless networks to maximize the utilization of channel resources. However, with the traffic patterns of wireless networks becoming huge and dynamic, conventional POC assignment algorithms only designed for constantly generated network traffic are not suitable for the new generation wireless networks. Therefore, in this article, a joint deep Covolutional Neural Network (CNN) and activity vector based intelligent channel assignment algorithm is proposed, which is referred to as CNNAV. With the proposed CNNV approach, the network can learn from the historical traffic patterns and intelligently assign POCs to wireless links. The simulation result shows that, the network performance of our proposal in terms of both packets loss rate and network throughput are better than conventional POC assignment algorithms.
KW - CNN
KW - Channel Assignment (CA)
KW - Deep learning
KW - Partially Overlapping Channels (POCs)
KW - activity vector
KW - capsule network
UR - http://www.scopus.com/inward/record.url?scp=85063475351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063475351&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2018.8647254
DO - 10.1109/GLOCOM.2018.8647254
M3 - Conference contribution
AN - SCOPUS:85063475351
T3 - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
BT - 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2018 through 13 December 2018
ER -