A Handoff Algorithm Based on Estimated Load for Dense Green 5G Networks

Arani, Atefeh Hajijamali; Omidi, Mohammad; Mehbodniya, Abolfazl; Adachi, Fumiyuki

doi:10.1109/glocom.2015.7417634

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…In recent years, research on handoff is focused mainly on HetNets operating in the band of traditional frequency 900MHz-2.4GHz and considering one or more factors including RSRP, QoS of UEs, UE mobility characteristics, BS load, etc. [8], [9], [10], [11], [12]. In [8], a handoff policy is proposed that considers context parameters, such as user speed, channel gains and cell load information.…”

Section: Handoff Strategies For Traditional Hetnetsmentioning

confidence: 99%

See 1 more Smart Citation

The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

Sun

Feng

Qin

et al. 2018

IEEE Trans. on Mobile Comput.

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The millimeter wave (mmWave) radio band is promising for the next-generation heterogeneous cellular networks (HetNets) due to its large bandwidth available for meeting the increasing demand of mobile traffic. However, the unique propagation characteristics at mmWave band cause huge redundant handoffs in mmWave HetNets that brings heavy signaling overhead, low energy efficiency and increased user equipment (UE) outage probability if conventional Reference Signal Received Power (RSRP) based handoff mechanism is used. In this paper, we propose a reinforcement learning based handoff policy named SMART to reduce the number of handoffs while maintaining user Quality of Service (QoS) requirements in mmWave HetNets. In SMART, we determine handoff trigger conditions by taking into account both mmWave channel characteristics and QoS requirements of UEs. Furthermore, we propose reinforcement-learning based BS selection algorithms for different UE densities. Numerical results show that in typical scenarios, SMART can significantly reduce the number of handoffs when compared with traditional handoff policies without learning.

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Section: Handoff Strategies For Traditional Hetnetsmentioning

confidence: 99%

“…The BS selection decision is based on a Markov Decision Process (MDP) model with the aim of maximizing UE average capacity. In [9], the authors proposed a handoff algorithm based on BSs estimated load. They combined handoff decisions with BS sleeping policy so as to improve system energy efficiency.…”

Section: Handoff Strategies For Traditional Hetnetsmentioning

confidence: 99%

The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

Sun

Feng

Qin

et al. 2018

IEEE Trans. on Mobile Comput.

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“…The vast majority of the existing algorithms pri-oritize femtocell over macrocell access to attain better system performance. A context-aware mobility management scheme was proposed in [11] to improve the UE throughput and with load estimation, both the energy and spectrum efficiency can be improved [12], [13]. In combination with cell range extension and interference mitigation, UE throughput can be further improved with load balancing, as in [14], [15].…”

Section: Related Work a Handover In Heterogeneous Networkmentioning

confidence: 99%

$P$ -Persistent Energy-Aware Handover Decisions Employing RF Fingerprint for Adaptive-Sized Heterogeneous Cellular Networks

et al. 2019

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The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energyaware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-tier networks. INDEX TERMS Horizontal and vertical handover, cell discovery, RF fingerprint, dynamic localization, energy-aware, RAT selection.

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“…Recently, hand-off management has attracted the research community [8]. Arani et al [9] proposed an algorithm based on base stations (BSs) estimated load. In the algorithm proposed in [9], the hand-off task is shifted from highly loaded BSs to base stations with light load.…”

Section: Major Milestones Of Next Generation Networkmentioning

confidence: 99%

“…Arani et al [9] proposed an algorithm based on base stations (BSs) estimated load. In the algorithm proposed in [9], the hand-off task is shifted from highly loaded BSs to base stations with light load. Hence, this algorithm improves the quality of service.…”

Section: Major Milestones Of Next Generation Networkmentioning

confidence: 99%

Big Data Perspective and Challenges in Next Generation Networks

2018

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With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

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A Handoff Algorithm Based on Estimated Load for Dense Green 5G Networks

Cited by 14 publications

References 17 publications

The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

$P$ -Persistent Energy-Aware Handover Decisions Employing RF Fingerprint for Adaptive-Sized Heterogeneous Cellular Networks

Big Data Perspective and Challenges in Next Generation Networks

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