P-persistent energy-Aware handover decisions employing rf fingerprint for adaptive-sized heterogeneous cellular networks

Research output: Contribution to journalArticle

Abstract

The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energy-Aware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-Tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-Tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-Tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-Tier networks.

Original languageEnglish
Article number8695833
Pages (from-to)52929-52944
Number of pages16
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Macros
Energy efficiency
Femtocell
Energy resources
Explosions
Energy utilization
Trajectories
Throughput
Topology

Keywords

  • cell discovery
  • dynamic localization
  • energy-Aware
  • Horizontal and vertical handover
  • RAT selection
  • RF fingerprint

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Cite this

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title = "P-persistent energy-Aware handover decisions employing rf fingerprint for adaptive-sized heterogeneous cellular networks",
abstract = "The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energy-Aware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-Tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-Tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-Tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-Tier networks.",
keywords = "cell discovery, dynamic localization, energy-Aware, Horizontal and vertical handover, RAT selection, RF fingerprint",
author = "Zhenni Pan and Megumi Saito and Jiang Liu and Shigeru Shimamoto",
year = "2019",
month = "1",
day = "1",
doi = "10.1109/ACCESS.2019.2912328",
language = "English",
volume = "7",
pages = "52929--52944",
journal = "IEEE Access",
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T1 - P-persistent energy-Aware handover decisions employing rf fingerprint for adaptive-sized heterogeneous cellular networks

AU - Pan, Zhenni

AU - Saito, Megumi

AU - Liu, Jiang

AU - Shimamoto, Shigeru

PY - 2019/1/1

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N2 - The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energy-Aware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-Tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-Tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-Tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-Tier networks.

AB - The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energy-Aware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-Tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-Tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-Tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-Tier networks.

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KW - Horizontal and vertical handover

KW - RAT selection

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