Optimization of control parameters using averaging of handover indicator and received power for minimizing ping-pong handover in LTE

Hassan, Noha; Elkhazeen, Khalid; Raahemiafar, Kaarman; Fernando, Xavier

doi:10.1109/ccece.2015.7129166

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The method of solving the pingpong handovers beyond the scope of this paper. Many methods have been studied [14], [15], [16]. Basically, the operators redesign the cover areas of base stations changing the angle of antennas or the radio power.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Communication Quality Degradation in LTE Network Using Statistics of State Transition

Suzuki

Plessis

Kitahara

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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In order to continue to provide high quality communication services, mobile network operators want to detect the degradation of the communication quality when it occurs. Since actively evaluating LTE network performance is costly and time consuming, mobile network operators passively monitor the network. A finite state machine is a powerful approach for monitoring the network condition. However, in actual LTE network, it is not sufficient to validate the conformance between the specification machine and the implementation machine. Therefore, this paper proposes a LTE network monitoring method using statistics of state transition. In the proposed method, we first define the states according to 3GPP standards, then, we measure the state transition by referring to LTE C-Plane messages. Also, we measure the state transition probability and time interval for the transition. Finally we check the statistics of the state transition to detect the degradation of the communication quality. In order to evaluate the proposed method, we introduce an example of detecting ping-pong handovers using actual traffic data. We show the potential usefulness of the proposed method for practical use.

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Section: Discussionmentioning

confidence: 99%

Monitoring Communication Quality Degradation in LTE Network Using Statistics of State Transition

Suzuki

Plessis

Kitahara

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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“…Furthermore, the selection is usually kept unchanged for a significant time, typically much longer than the time dedicated to measuring the performance of the candidate networks. This is due to the overhead introduced by a network switching procedure, as well as to the discomfort caused to the user due to service interruption during network switch; indeed, a significant effort was devoted to designing algorithms that prevent frequent network switching procedures [21]. The muMAB model is designed so to grasp the cycle between measuring and using, and thus model real scenarios with higher accuracy than the classical MAB model.…”

Section: The Mumab Modelmentioning

confidence: 99%

muMAB: A Multi-Armed Bandit Model for Wireless Network Selection

et al. 2018

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Multi-armed bandit (MAB) models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT) device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (muMAB), aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to measure and to use; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1) is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI), is appositely designed for the new model so to take advantage of the new measure action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF) of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF. Results highlight, however, that as the ratio between the time required for using an arm and the time required to measure increases, the proposed algorithms guarantee the best performance, with muUCB1 emerging as the best candidate when the arms are characterized by similar mean rewards, and MLI prevailing when an arm is significantly more rewarding than others. This calls thus for the introduction of an adaptive approach capable of adjusting the behavior of the algorithm or of switching algorithm altogether, depending on the acquired knowledge on the PDF of the reward on each arm.

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Handover Performance Evaluation Under Dynamic User Characteristics

Lema

2021

Wireless Pers Commun

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Optimization of control parameters using averaging of handover indicator and received power for minimizing ping-pong handover in LTE

Cited by 4 publications

References 9 publications

Monitoring Communication Quality Degradation in LTE Network Using Statistics of State Transition

Monitoring Communication Quality Degradation in LTE Network Using Statistics of State Transition

muMAB: A Multi-Armed Bandit Model for Wireless Network Selection

Handover Performance Evaluation Under Dynamic User Characteristics

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