Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions

Addali, Khaled M.; Melhem, Suhib Bani; Khamayseh, Yaser; Zhang, Zhenjiang; Kadoch, Michel

doi:10.1109/access.2019.2939936

Cited by 50 publications

(34 citation statements)

References 15 publications

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“…The uneven distribution of UEs within the network due to random cell positioning and UEs mobility makes some cells to become more loaded than others as a result of more UEs associating with those cells than others. This load imbalance among the cells causes frequent HO and degradation in the QoS of the UEs [100]. Also, in an attempt to prevent frequent HO, most of the HO optimization methods proposed in literature suggest HO skipping or prolonged user connection to a BS [191], which could lead to load imbalance in the network.…”

Section: Load Balancingmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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Section: Load Balancingmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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“…• It considers all the important cell selection factors; namely, RSSI, the speed and direction of the vehicle, and cell loads. A small BS that has a high load will not be chosen, because the load distribution among small cells is important to improve the QoS and to enhance system capacity [57]. shows the vehicle association variable between vehicle i and BS j.…”

Section: A Main Features Of Ada-cs Schemementioning

confidence: 99%

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

Alablani

Arafah

2021

IEEE Access

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Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low-and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

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“…The inter‐AP interference is estimated as

\begin{matrix} I_{italicij} = \underset{k \neq j}{false\sum} P_{i, k} \cdot {RBUR}_{k}, where \\ {RBUR}_{k} = \frac{\sum_{τ \in (t ‐ T, t)} \sum_{i} I_{italicij} N_{i, k} false(τ false)}{T \cdot # RB} \end{matrix}

is the RB utilization ratio, I ij = 1 if UE i is connected to AP j and I ij = 0 otherwise, N ij ( t ) is the number of RBs allocated to user i by AP j , and T is the time window for the computation of the average RBUR. The data rate that can be transmitted by a single RB is computed using the best modulation coding scheme [58] with the Shannon formula

r_{italicij} = B_{RB} \log_{2} (1 + {SINR}_{italicij})

, where B RB is the bandwidth of a single RB (that is, 12·15·2 μ KHz). Finally, the number of RBs needed to satisfy UE requirements is

n_{ij} = \frac{R_{i}}{r_{ij}} .

…”

Section: Dynamic Resource Management For 3d Connectivitymentioning

confidence: 99%

6G in the sky: On‐demand intelligence at the edge of 3D networks (Invited paper)

et al. 2020

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Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions

Cited by 50 publications

References 15 publications

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

6G in the sky: On‐demand intelligence at the edge of 3D networks (Invited paper)

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