TY - GEN
T1 - QoS-aware power management with deep learning
AU - Zhou, Junxiu
AU - Liu, Xian
AU - Tao, Yangyang
AU - Yu, Shucheng
N1 - Publisher Copyright:
© 2019 IFIP.
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Network densification is becoming an overwhelming phenomenon in many emerging wireless communication paradigms. Although network densification may promote system metrics like the throughputs, the quality-of-service (QoS) issue needs to be carefully investigated. Commonly, the QoS-aware power management is tightly restricted by the complicated patterns of interferences among multiple active communication devices. Conventional approaches in optimizing the QoS-aware power management problem may either fail to convergence or the overall power rate is unsatisfactory. In this paper, we make an effort to solve the QoS-aware power management problem with the aid of the deep learning (DL) methodology. Recently DL has shone light on wide variety of research fields, such as image processing and natural language processing. It is our intensive interest in exploring the role that DL plays to solve the QoS-aware power management problem. In the presented extensive experimental analysis work, we show that the DL-based method can well match the solution generated by a conventional optimization procedure. It is impressed that the convergence of DL is quite fast. Moreover, the DL-based approach demonstrates the better performance when the conventional method enters the infeasible region.
AB - Network densification is becoming an overwhelming phenomenon in many emerging wireless communication paradigms. Although network densification may promote system metrics like the throughputs, the quality-of-service (QoS) issue needs to be carefully investigated. Commonly, the QoS-aware power management is tightly restricted by the complicated patterns of interferences among multiple active communication devices. Conventional approaches in optimizing the QoS-aware power management problem may either fail to convergence or the overall power rate is unsatisfactory. In this paper, we make an effort to solve the QoS-aware power management problem with the aid of the deep learning (DL) methodology. Recently DL has shone light on wide variety of research fields, such as image processing and natural language processing. It is our intensive interest in exploring the role that DL plays to solve the QoS-aware power management problem. In the presented extensive experimental analysis work, we show that the DL-based method can well match the solution generated by a conventional optimization procedure. It is impressed that the convergence of DL is quite fast. Moreover, the DL-based approach demonstrates the better performance when the conventional method enters the infeasible region.
KW - Deep learning
KW - Feedforward neural networks
KW - Power management
KW - QoS-aware
KW - Wireless communications
UR - http://www.scopus.com/inward/record.url?scp=85067020336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067020336&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85067020336
T3 - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
SP - 289
EP - 294
BT - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
T2 - 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
Y2 - 8 April 2019 through 12 April 2019
ER -