Name
Title | ||
|---|---|---|
Optimization and Learning in Energy Efficient Resource Allocation for Cognitive Radio Networks |
Abstract | ||
|---|---|---|
The recent surge in real-time traffic has led to serious energy efficiency concerns in cognitive radio networks (CRNs). Network infrastructure such as base stations (BSs) host different service classes of traffic with stringent quality-of-service (QoS) requirements that need to be satisfied. Thus, maintaining the desired QoS in an energy efficient manner requires a good trade-off between QoS and energy saving. To deal with this problem, this paper proposes a deep learning-based computational-resource-aware energy consumption technique. The proposed scheme uses an exploration technique of the systems' state-space and traffic load prediction to come up with a better trade-off between QoS and energy saving. The simulation results show that the proposed exploration technique performs 9% better than the traditional random tree technique even when the provisioning priority shifts away from energy saving towards QoS, i.e., α ≥ 0.5. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/VTCSpring.2019.8746632 | 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) |
Keywords | Field | DocType |
energy efficient resource allocation,cognitive radio networks,real-time traffic,deep learning-based computational-resource-aware energy consumption technique,quality-of-service,QoS,random tree technique | Random tree,Base station,Computer science,Efficient energy use,Computer network,Quality of service,Provisioning,Artificial intelligence,Deep learning,Energy consumption,Cognitive radio | Conference |
ISSN | ISBN | Citations |
1090-3038 | 978-1-7281-1218-3 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mduduzi C. Hlophe | 1 | 0 | 0.68 |
| B. T. Maharaj | 2 | 7 | 2.50 |