Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

Zihan Luo; Simon Armour; Joe McGeehan

doi:10.1007/978-3-319-23440-3_10

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

Zihan Luo, Simon Armour, Joe McGeehan

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

229 Downloads (Pure)

Abstract

This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implemented
to guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genetic algorithm can approximately achieve the optimum bandwidth efficiency whilst requiring only 24% of the computation effort
of the exhaustive search in the investigated network.

Original language	English
Title of host publication	Multiple Access Communications
Subtitle of host publication	th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings
Editors	Magnus Jonsson, Alexey Vinel, Boris Bellalta, Olav Tirkkonen
Publisher	Springer International Publishing AG
Pages	123-134
Number of pages	12
ISBN (Electronic)	9783319234403
ISBN (Print)	9783319234397
DOIs	https://doi.org/10.1007/978-3-319-23440-3_10
Publication status	Published - 3 Sep 2015

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer International Publishing
Volume	9305
ISSN (Print)	0302-9743

Keywords

Multi-cell
Exhaustive search
Genetic algorithm
Frequency reuse
Cooperative transmission

Access to Document

10.1007/978-3-319-23440-3_10

Full-text PDF (accepted author manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Springer Verlag at http://dx.doi.org/10.1007/978-3-319-23440-3_10. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 116 KB

Fingerprint Dive into the research topics of 'Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms'. Together they form a unique fingerprint.

Cite this

Luo, Z., Armour, S., & McGeehan, J. (2015). Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms. In M. Jonsson, A. Vinel, B. Bellalta, & O. Tirkkonen (Eds.), Multiple Access Communications: th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings (pp. 123-134). (Lecture Notes in Computer Science; Vol. 9305). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-23440-3_10

Luo, Zihan ; Armour, Simon ; McGeehan, Joe. / Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms. Multiple Access Communications: th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings. editor / Magnus Jonsson ; Alexey Vinel ; Boris Bellalta ; Olav Tirkkonen. Springer International Publishing AG, 2015. pp. 123-134 (Lecture Notes in Computer Science).

@inproceedings{fb77eaa1c0d542f7a1bfe3d42000229e,

title = "Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms",

abstract = "This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genetic algorithm can approximately achieve the optimum bandwidth efficiency whilst requiring only 24% of the computation effortof the exhaustive search in the investigated network. ",

keywords = "Multi-cell, Exhaustive search, Genetic algorithm, Frequency reuse, Cooperative transmission",

author = "Zihan Luo and Simon Armour and Joe McGeehan",

year = "2015",

month = sep,

day = "3",

doi = "10.1007/978-3-319-23440-3_10",

language = "English",

isbn = "9783319234397",

series = "Lecture Notes in Computer Science",

publisher = "Springer International Publishing AG",

pages = "123--134",

editor = "Magnus Jonsson and Alexey Vinel and Boris Bellalta and Olav Tirkkonen",

booktitle = "Multiple Access Communications",

address = "Switzerland",

}

Luo, Z, Armour, S & McGeehan, J 2015, Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms. in M Jonsson, A Vinel, B Bellalta & O Tirkkonen (eds), Multiple Access Communications: th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9305, Springer International Publishing AG, pp. 123-134. https://doi.org/10.1007/978-3-319-23440-3_10

Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms. / Luo, Zihan; Armour, Simon; McGeehan, Joe.

Multiple Access Communications: th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings. ed. / Magnus Jonsson; Alexey Vinel; Boris Bellalta; Olav Tirkkonen. Springer International Publishing AG, 2015. p. 123-134 (Lecture Notes in Computer Science; Vol. 9305).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms

AU - Luo, Zihan

AU - Armour, Simon

AU - McGeehan, Joe

PY - 2015/9/3

Y1 - 2015/9/3

N2 - This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genetic algorithm can approximately achieve the optimum bandwidth efficiency whilst requiring only 24% of the computation effortof the exhaustive search in the investigated network.

AB - This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genetic algorithm can approximately achieve the optimum bandwidth efficiency whilst requiring only 24% of the computation effortof the exhaustive search in the investigated network.

KW - Multi-cell

KW - Exhaustive search

KW - Genetic algorithm

KW - Frequency reuse

KW - Cooperative transmission

U2 - 10.1007/978-3-319-23440-3_10

DO - 10.1007/978-3-319-23440-3_10

M3 - Conference Contribution (Conference Proceeding)

SN - 9783319234397

T3 - Lecture Notes in Computer Science

SP - 123

EP - 134

BT - Multiple Access Communications

A2 - Jonsson, Magnus

A2 - Vinel, Alexey

A2 - Bellalta, Boris

A2 - Tirkkonen, Olav

PB - Springer International Publishing AG

ER -

Luo Z, Armour S, McGeehan J. Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms. In Jonsson M, Vinel A, Bellalta B, Tirkkonen O, editors, Multiple Access Communications: th International Workshop, MACOM 2015, Helsinki, Finland, September 3-4, 2015, Proceedings. Springer International Publishing AG. 2015. p. 123-134. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-23440-3_10