A Dynamic Hysteresis-Adjusting Algorithm in LTE Self-Organization Networks

Li, Wenyu; Duan, Xiaoyu; Jia, Shucong; Zhang, Lin; Liu, Yu; Lin, Jiaru

doi:10.1109/vetecs.2012.6240199

Cited by 11 publications

(11 citation statements)

References 5 publications

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“…Heuristic methods are proposed in [17][18][19]. In [17], a ripple algorithm is adopted to resolve load unbalance problem.…”

Section: Related Workmentioning

confidence: 99%

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UE-Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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A user changes its serving cell if the quality of experience (QoE) provided by the current serving cell is not satisfactory. Since users reselect cells to increase their QoEs selfishly, the system resource efficiency can be deteriorated and a system can be unstable if users are not driven to cooperate appropriately. In this paper, inspired by the minority game (MG) model, we design a UE-initiated cell reselection policy. The MG has a salient characteristic that the number of players who win the game converges to a prespecified value even though players act selfishly without knowing the actions taken by the other players. Using the MG model, we devise a rule by which each UE plays a cell reselection game. We also design a criterion that a system controller uses to determine the result of a game and public information sent by a system controller to induce implicit cooperation among UEs. The simulation results show that compared with noncooperative method the proposed method increases not only the system performance, such as cell load balance index and system utility, but also the performance of UEs in terms of a downlink data rate and an outage probability received from a system.

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“…Heuristic methods are proposed in [17][18][19]. In [17], a ripple algorithm is adopted to resolve load unbalance problem.…”

Section: Related Workmentioning

confidence: 99%

“…Heuristic methods are proposed in [17][18][19]. In [17], a ripple algorithm is adopted to resolve load unbalance problem. The authors in [18] propose a heuristic algorithm to solve a load balancing problem in HetNets which are known to be NP-hard.…”

Section: Related Workmentioning

confidence: 99%

UE-Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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“…The condition in (12) implies that the total transmitted power over all RBs does not exceed the maximum transmission power of BS k. Since our optimization approach in (6) aims at maximizing the total rate, the last condition (13) dictates that the instantaneous rate is larger than a minimum rate for BS k. Due to the distributed nature of this optimization problem in (6), we will investigate two reinforcement learning techniques in this paper, as will be discussed in Sections 6.1 and 6.2.…”

Section: Problem Formulation For Throughput Maximizationmentioning

confidence: 99%

“…MM mechanisms, which have been included in the first release of the long-term evolution (LTE) standard (Rel-8), were originally developed for networks that only involve macrocells [4]. The defined MM procedures for the macrocell-only scenarios have been widely discussed in the literature, e.g., in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. It has been shown that MM for macrocell-only scenarios yield highly reliable handover execution, where HOFs and PPs can be typically avoided due to large cell sizes [20].…”

Section: Introductionmentioning

confidence: 99%

Mobility management in HetNets: a learning-based perspective

Simsek

Bennis

Güvenç

2015

J Wireless Com Network

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Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three.

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“…However, those papers only provided a general analysis. In [6], a dynamic hysteresis-adjusting method was proposed which uses the maximum allowed RLF ratio to find the optimal hysteresis to reduce ping-pong and call-drop ratio. However, it cannot find a suitable hysteresis to reduce the RLF ratio.…”

Section: Introductionmentioning

confidence: 99%

Dynamic user equipment‐based hysteresis‐adjusting algorithm in LTE femtocell networks

et al. 2014

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In long-term evoluation (LTE) femtocell networks, hysteresis is one of the main parameters which affects the performance of handover with a number of unnecessary handovers, including ping-pong, early, late and incorrect handovers. In this study, the authors propose a hybrid algorithm that aims to obtain the optimised unique hysteresis for an individual mobile user moving at various speeds during the inbound handover process. This algorithm is proposed for two-tier scenarios with macro and femto. The centralised function in this study evaluates the overall handover performance indicator. Then, the handover aggregate performance indicator (HAPI) is used to determine an optimal configuration. Based on the received reference signal-to-interference-plus-noise ratio, the distributed function residing on the user equipment (UE) is able to obtain an optimal unique hysteresis for the individual UE. Theoretical analysis with three indication boundaries is provided to evaluate the proposed algorithm. A system-level simulation is presented, and the proposed algorithm outperformed the existing approaches in terms of handover failure, call-drop and redundancy handover ratios and also achieved better overall system performance.

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A Dynamic Hysteresis-Adjusting Algorithm in LTE Self-Organization Networks

Cited by 11 publications

References 5 publications

UE-Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

UE-Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Mobility management in HetNets: a learning-based perspective

Dynamic user equipment‐based hysteresis‐adjusting algorithm in LTE femtocell networks

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