Adaptive measurement for energy efficient mobility management in ultra-dense small cell networks

Kao, Hsu; Wei, Chen-Yu; Lin, Hsiao-Ching; Chiang, Yi-Han; Liao, Wanjiun

doi:10.1109/icc.2016.7511217

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Emphasizing on reducing connection failures due to mobility in HetNets, Chen et al 34 have proposed a scheme that ranks candidate BS for handover using a multicriteria decision making algorithm and the information on mobility of devices. Similarly, stressing on reducing power consumption in handover process, Kao et al 35 analyze radio link failure rate and timing of handover to propose a mechanism for adjusting the frequency of measurements. In alternate work, Lim and Hong 36 have provided a technical overview of mobility and handover management where they identify specific criteria for designing an efficient handover strategy.…”

Section: Related Research Workmentioning

confidence: 99%

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

Tiwari

Deshmukh

2019

Trans Emerging Tel Tech

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In this paper, we present an efficient handoff decision strategy and a mobility state detection (MSD) scheme for heterogeneous networks (HetNets). Due to base station (BS) densification, HetNets occasionally face unnecessary handovers and service failures. Since velocity plays a crucial role in handover process, its knowledge is imperative for effective mobility management. Furthermore, due to limited power in mobile devices, emphasis is in investigation of velocity estimators that are at service provider end. Thus, we propose two maximum likelihood-based velocity estimators exploiting handover count and sojourn time measurements, respectively. Our analysis illustrates that both the estimators are asymptotically unbiased and efficient. In addition, we also notice that variance of sojourn time-based estimator is smaller than that of handover count-based estimator. Considering superiority of sojourn time-based estimator, we propose a handover decision strategy to minimize unnecessary handovers. Here, we first predict the sojourn time of upcoming BSs on the basis of velocity estimated via sojourn time samples. Next, BSs whose predicted sojourn time are greater than a prespecified threshold are then considered to identify the next serving BS. Finally, we select next serving BS on the basis of minimum cost for satisfying quality-of-service parameters such as bandwidth, security, monetary cost, and available power at user device. This work also exploits estimated velocity to investigate MSD specified in LTE standards. The numerical and simulation results validate our approach by illustrating accuracy in velocity estimation, reduction in frequent handovers and service failures, and improvement in MSD.

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Section: Related Research Workmentioning

confidence: 99%

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

Tiwari

Deshmukh

2019

Trans Emerging Tel Tech

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“…Users with poor channel quality may not get normal services in extreme cases. The other is to solve the power allocation problem based on the idea of optimization, in which the cost is power and optimization objective is the difference between throughput and power [46]- [52].…”

Section: Resource Allocationmentioning

confidence: 99%

Overview on Interference Management Technology for Ultra-Dense Network

Zhang¹,

Tian²

2018

OALib

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The explosive growth of the mobile traffic demand has triggered the investigation of 5th generation mobile networks (5G) for the future development of wireless communications. The ultra-dense network (UDN) has the ability to effectively improve system throughput and spectrum utilization by deploying a large number of low-power, low-cost, low-power home base stations or micro base stations. The dense distribution of micro base stations poses new challenges in terms of energy loss and interference management. Firstly, the overview of 5G is briefly introduced in this paper. Then, interference management method for UDN is summarized. So far, interference management methods for UDN mainly include interference alignment, clustering, and resource allocation. The principles of the three methods are described in detail. Finally, we conclude that clustering and resource allocation are more suitable for UDN. bile data services. With the increasing demand for broadband mobile data, the next-generation mobile communication system (5G) is in urgent need to meet user needs.Compared with 4G, 5G has a great improvement in terms of transmission rate, user experience rate, terminal-to-terminal delay, number of connected devices, mobility, and standby time in different service scenarios. According to these key technical indicators, in order to respond to the needs of future 5G network service development and maintenance operations, comprehensive consideration must be taken from various aspects such as wireless spectrum, wireless access technologies, and network architecture. The requirements of spectrum resource for future network technologies can be met by the deep development and utilization of high frequency bands and even ultra-high frequency bands. The utilization of wireless spectrum resources can also be improved by a series of advanced wireless transmission technologies. Among them, millimeter wave, massive MIMO and ultra dense networks (UDN) are regarded as the most promising key technologies for improving spectrum utilization, increasing system capacity, and enhancing 5G network performance.

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A Handover Management Strategy Using Residence Time Prediction in 5G Ultra-Dense Networks

Qin

Feng

Yue

et al. 2020

Lecture Notes in Electrical Engineering

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Adaptive measurement for energy efficient mobility management in ultra-dense small cell networks

Cited by 5 publications

References 5 publications

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets

Overview on Interference Management Technology for Ultra-Dense Network

A Handover Management Strategy Using Residence Time Prediction in 5G Ultra-Dense Networks

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