Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning

Handle URI:
http://hdl.handle.net/10754/625251
Title:
Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning
Authors:
Alqerm, Ismail; Shihada, Basem ( 0000-0003-4434-4334 )
Abstract:
The multi-tier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multi-tier 5G network structure using a non-cooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
AlQerm I, Shihada B (2017) Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning. IEEE Transactions on Vehicular Technology: 1–1. Available: http://dx.doi.org/10.1109/TVT.2017.2731798.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Vehicular Technology
Issue Date:
25-Jul-2017
DOI:
10.1109/TVT.2017.2731798
Type:
Article
ISSN:
0018-9545; 1939-9359
Additional Links:
http://ieeexplore.ieee.org/document/7990595/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlqerm, Ismailen
dc.contributor.authorShihada, Basemen
dc.date.accessioned2017-07-26T06:19:09Z-
dc.date.available2017-07-26T06:19:09Z-
dc.date.issued2017-07-25en
dc.identifier.citationAlQerm I, Shihada B (2017) Energy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learning. IEEE Transactions on Vehicular Technology: 1–1. Available: http://dx.doi.org/10.1109/TVT.2017.2731798.en
dc.identifier.issn0018-9545en
dc.identifier.issn1939-9359en
dc.identifier.doi10.1109/TVT.2017.2731798en
dc.identifier.urihttp://hdl.handle.net/10754/625251-
dc.description.abstractThe multi-tier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multi-tier 5G network structure using a non-cooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7990595/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subject5G networksen
dc.subjectonline learningen
dc.subjectenergy efficiencyen
dc.subjectpower allocationen
dc.titleEnergy Efficient Power Allocation in Multi-tier 5G Networks Using Enhanced Online Learningen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalIEEE Transactions on Vehicular Technologyen
dc.eprint.versionPost-printen
kaust.authorAlqerm, Ismailen
kaust.authorShihada, Basemen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.