A Vertical Handover Algorithm in Integrated Macrocell Femtocell Networks

Deswal, Suman; Singhrova, Anita

doi:10.11591/ijece.v7i1.pp299-308

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…The LTE-Sim simulator [28] is used to evaluate the performance of the proposed algorithm depending on the number of the handovers with compare to the algorithm introduced by Suman [17]. The topology consists of two macrocells (eNB) with a radius of 1km each and various femtocells (HeNBs) density, the femtocell number is configured as 30, 50, 70 and 90 in each macrocell, and all femtocells are covered by open access type to allow the user equipment UE to handover to each femtocell.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, a novel handover decision structure is proposed for choosing the target station based on UE mobility history by applying a Q-learning based technique that takes a handover decision according to the current and past history of the environment. We compared our handover proposed algorithm with Suman work [17] in terms of average number of handovers. LTE-Sim simulator has been developed to evaluate the system performance.…”

Section: Introductionmentioning

confidence: 99%

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Q-Learning vertical handover scheme in two-tier LTE-A networks

Bathich¹,

Mansor²,

Suliman³

et al. 2020

IJECE

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Global mobile communication necessitates improved capacity and proper quality assurance for services. To achieve these requirements, small cells have been deployed intensively by long term evolution (LTE) networks operators beside conventional base station structure to provide customers with better service and capacity coverage. Accomplishment of seamless handover between Macrocell layer (first tier) and Femtocell layer (second tier) is one of the key challenges to attain the QoS requirements. Handover related information gathering becomes very hard in high dense femtocell networks, effective handover decision techniques are important to minimize unnecessary handovers occurred and avoid Ping-Pong effect. In this work, we proposed and implemented an efficient handover decision procedure based on users’ profiles using Q-learning technique in an LTE-A macrocell-femtocell networks. New multi-criterion handover decision parameters are proposed in typical/dense femtocells in microcells environment to estimate the target cell for handover. The proposed handover algorithms are validated using the LTE-Sim simulator under an urban environment. The simulation results showed noteworthy reduction in the average number of handovers.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Q-Learning vertical handover scheme in two-tier LTE-A networks

Bathich¹,

Mansor²,

Suliman³

et al. 2020

IJECE

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“…A new cell selection technique is proposed to pick the nominated cell depending on the path of the UE by using a reinforcement learning algorithm. Suman's algorithm [18] is used as the benchmark in terms of the control signaling rate and the packet loss ratio. We built our own module in the LTE-Sim simulator to test the network output results.…”

Section: Figurementioning

confidence: 99%

Untitled

2021

J. ICT Res. Appl.

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Universal mobile networks require enhanced capability and appropriate quality of service (QoS) and experience (QoE). To achieve this, Long Term Evolution (LTE) system operators have intensively deployed femtocells (HeNBs) along with macrocells (eNBs) to offer user equipment (UE) with optimal capacity coverage and best quality of service. To achieve the requirement of QoS in the handover stage among macrocells and femtocells we need a seamless cell selection mechanism. Cell selection requirements are considered a difficult task in femtocell-based networks and effective cell selection procedures are essential to reduce the ping-pong phenomenon and to minimize needless handovers. In this study, we propose a seamless cell selection scheme for macrocell-femtocell LTE systems, based on the Q-learning environment. A novel cell selection mechanism is proposed for high-density femtocell network topologies to evaluate the target base station in the handover stage. We used the LTE-Sim simulator to implement and evaluate the cell selection procedures. The simulation results were encouraging: a decrease in the control signaling rate and packet loss ratio were observed and at the same time the system throughput was increased.

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“…When an area is highly populated by M2M devices and all these devices try to access the network simultaneously congestion is created on the network, which in turn degrades the performance of the network. One way to mitigate this problem is by injecting more access points into the network, and this can be done by deploying HetNet underneath the macrocell eNB [7]. The M2M communication devices are expected to be the focal point around which the future Internet of Things (IoT) is expected to be built.…”

Section: Introductionmentioning

confidence: 99%

Random access improvement for M2M communication in LTE-A using femtocell

Sackey¹,

Paulus²

2019

IJECE

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<p><span>When an area is highly populated with Machine-to-Machine devices and all these devices attempt to access the Random Access Network Simultaneously, congestion is created on the network which degrades the performance of the network to other users. In this paper, the researchers are seeking to improve network accessibility by deploying more Femtocell into the network. They engaged the use of Extended Access Barring to restrict the M2M devices from accessing the network via macrocell eNB when a minimum load threshold is attained, thereby preventing the macrocell eNB from being congested. Deploying these Femtocells underneath the macrocell eNB comes with the issue of Inter-Cell Interference which nullifies any gains made by this deployment. The researchers employed Fractional Frequency Reuse and Complete Frequency Reuse schemes to mitigate the negative effects of ICI to augment the throughput of the network, improve the system capacity and enhanced the user experience within the network.</span></p>

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A Vertical Handover Algorithm in Integrated Macrocell Femtocell Networks

Cited by 10 publications

References 15 publications

Q-Learning vertical handover scheme in two-tier LTE-A networks

Q-Learning vertical handover scheme in two-tier LTE-A networks

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Random access improvement for M2M communication in LTE-A using femtocell

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