An advanced SOM algorithm applied to handover management within LTE

Neil Sinclair, David Harle, Ian Glover, James Irvine, Robert Atkinson

Research output: Contribution to journalSpecial issue

27 Citations (Scopus)

Abstract

Abstract—A novel approach to handover management for LTE femtocells is presented. Within LTE, the use of Self Organizing Networks is included as standard and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexities of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified Self Organizing Map is used to allow the femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the system’s overall efficiency will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-n-play functionality required
from Self Organizing Networks in LTE systems.
Original languageEnglish
Pages (from-to)1883-1894
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume62
Issue number5
Early online date8 Mar 2013
DOIs
Publication statusPublished - Jun 2013

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Femtocell
Handover
Self organizing maps
Base stations
Learning systems
Self-organizing
Neural networks
Processing
Self-organizing Map
Use Case
Assign
Machine Learning

Keywords

  • handover management
  • femtocell
  • macrocell
  • advanced SOM algorithm
  • LTE
  • self-organizing networks (SON)
  • Handover
  • long-term evolution (LTE)
  • neural networks
  • self-organizing feature maps

Cite this

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title = "An advanced SOM algorithm applied to handover management within LTE",
abstract = "Abstract—A novel approach to handover management for LTE femtocells is presented. Within LTE, the use of Self Organizing Networks is included as standard and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexities of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified Self Organizing Map is used to allow the femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70{\%} while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the system’s overall efficiency will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-n-play functionality requiredfrom Self Organizing Networks in LTE systems.",
keywords = "handover management, femtocell, macrocell, advanced SOM algorithm, LTE, self-organizing networks (SON), Handover, long-term evolution (LTE) , neural networks, self-organizing feature maps",
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An advanced SOM algorithm applied to handover management within LTE. / Sinclair, Neil; Harle, David; Glover, Ian; Irvine, James; Atkinson, Robert.

In: IEEE Transactions on Vehicular Technology, Vol. 62, No. 5, 06.2013, p. 1883-1894.

Research output: Contribution to journalSpecial issue

TY - JOUR

T1 - An advanced SOM algorithm applied to handover management within LTE

AU - Sinclair, Neil

AU - Harle, David

AU - Glover, Ian

AU - Irvine, James

AU - Atkinson, Robert

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N2 - Abstract—A novel approach to handover management for LTE femtocells is presented. Within LTE, the use of Self Organizing Networks is included as standard and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexities of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified Self Organizing Map is used to allow the femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the system’s overall efficiency will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-n-play functionality requiredfrom Self Organizing Networks in LTE systems.

AB - Abstract—A novel approach to handover management for LTE femtocells is presented. Within LTE, the use of Self Organizing Networks is included as standard and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexities of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified Self Organizing Map is used to allow the femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the system’s overall efficiency will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-n-play functionality requiredfrom Self Organizing Networks in LTE systems.

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KW - self-organizing feature maps

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JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

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ER -