An Analytical Approach for Mobility Load Balancing in Wireless Networks

Musa, Sarhan M.; Mir, Nader F.

doi:10.2498/cit.1001843

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Knowledge of UE mobility can also be very beneficial to transmission scheduling [2], mobility load balancing [3], channel quality indicator (CQI) feedback enhancement [4], energy efficiency [5], and many resource management scenarios. Choosing either frequency selective scheduling (FSS) or frequency diversity scheduling (FDS) for LTE, could depend on the user speed as FSS shall be preferred for low speed UEs and FDS for high speed UEs.…”

Section: Introductionmentioning

confidence: 99%

Online mobile user speed estimation: Performance and tradeoff considerations

Haddad

Herculea

Chen

et al. 2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Section: Introductionmentioning

confidence: 99%

Online mobile user speed estimation: Performance and tradeoff considerations

Haddad

Herculea

Chen

et al. 2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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“…These challenges motivate the need for UE-specific and cell-specific handover parameter optimization, which typically require estimation of the UE's velocity for effective configuration of handover parameters [11]. A UE's velocity estimate may also be used for scheduling [12,13], mobility load balancing [14], channel quality indicator (CQI) feedback enhancements [15], and energy efficiency enhancements [16].…”

Section: Introductionmentioning

confidence: 99%

Handover Count Based Velocity Estimation and Mobility State Detection in Dense HetNets

Merwaday

Güvenç

2016

IEEE Trans. Wireless Commun.

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In wireless cellular networks with densely deployed base stations, knowing the velocities of mobile devices is a key to avoid call drops and improve the quality of service to the user equipments (UEs). A simple and efficient way to estimate a UE's velocity is by counting the number of handovers made by the UE during a predefined time window. Indeed, handover-count based mobility state detection has been standardized since Long Term Evolution (LTE) Release-8 specifications. The increasing density of small cells in wireless networks can help in accurate estimation of velocity and mobility state of a UE. In this paper, we model densely deployed small cells using stochastic geometry, and then analyze the statistics of the number of handovers as a function of UE velocity, small-cell density, and handover count measurement time window. Using these statistics, we derive approximations to the Cramer-Rao lower bound (CRLB) for the velocity estimate of a UE. Also, we determine a minimum variance unbiased (MVU) velocity estimator whose variance tightly matches with the CRLB. Using this velocity estimator, we formulate the problem of detecting the mobility state of a UE as low, medium, or high-mobility, as in LTE specifications. Subsequently, we derive the probability of correctly detecting the mobility state of a UE. Finally, we evaluate the accuracy of the velocity estimator under more realistic scenarios such as clustered deployment of small cells, random way point (RWP) mobility model for UEs, and variable UE velocity. Our analysis shows that the accuracy of velocity estimation and mobility state detection increases with increasing small cell density and with increasing handover count measurement time window.keywords: Cramer-Rao lower bound (CRLB), heterogeneous networks (HetNets), long term evolution (LTE), mobility state estimation, mobile velocity estimation, phantom cell, small cells.

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“…Knowledge of UE mobility can also be very beneficial to transmission scheduling [4], mobility load balancing [5], channel quality indicator (CQI) feedback enhancement [6], energy efficiency enhancement [7], and many resource management scenarios. For example, choosing the most suitable channel dependent scheduling scheme for LTE, either frequency selective scheduling (FSS) or frequency diversity scheduling (FDS), would depend on the user velocity.…”

Section: Introductionmentioning

confidence: 99%

Mobility state estimation in LTE

Haddad

Herculea

Altman

et al. 2016

2016 IEEE Wireless Communications and Networking Conference

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Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

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An Analytical Approach for Mobility Load Balancing in Wireless Networks

Cited by 5 publications

References 14 publications

Online mobile user speed estimation: Performance and tradeoff considerations

Online mobile user speed estimation: Performance and tradeoff considerations

Handover Count Based Velocity Estimation and Mobility State Detection in Dense HetNets

Mobility state estimation in LTE

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