On the Optimization of User Association and Resource Allocation in HetNets With mm-Wave Base Stations

This article investigates the problem of joint user association and resource allocation, defined by the number of allocated time-slots, in hybrid heterogeneous networks with the coexistence of sub-6-GHz base stations and millimeter wave (mm-Wave) base stations. To do so, we formulate a joint optimiz...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE systems journal Jg. 14; H. 3; S. 3957 - 3967
Hauptverfasser: Chaieb, Cirine, Mlika, Zoubeir, Abdelkefi, Fatma, Ajib, Wessam
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Base stations
Computer simulation
Heuristic algorithms
Hybrid HetNets
Interference
Machine learning
millimeter wave communications
Millimeter waves
Optimization
Radio equipment
Reinforcement learning
reinforcement learning algorithms
Resource allocation
Resource management
Resource utilization
Signal to noise ratio
Stations
user association
ISSN:1932-8184, 1937-9234
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This article investigates the problem of joint user association and resource allocation, defined by the number of allocated time-slots, in hybrid heterogeneous networks with the coexistence of sub-6-GHz base stations and millimeter wave (mm-Wave) base stations. To do so, we formulate a joint optimization problem to improve the efficiency of resource utilization by maximizing the number of associated users and minimizing the number of allocated time-slots. The optimization problem is formulated as a binary integer linear program and is proved to be NP-hard. Accordingly, we propose two efficient heuristic algorithms to solve it. The first one is centralized and relies on complete information, whereas the second one is distributed and is based on a reinforcement learning approach. The proposed distributed learning algorithm aims to find the best association for each user based on its past experience, automatically and independently from others. Simulation results show that the performances of both proposed algorithms are close-to-optimal with an important reduction in computational complexity.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2020.2984596