Adaptive Hysteresis Margin for Handover in Femtocell Networks

Bečvář, Zdeněk; Mach, Pavel

doi:10.1109/icwmc.2010.17

Cited by 59 publications

(50 citation statements)

References 7 publications

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“…Algorithms in this class directly or indirectly account for the interference at the UEs or the cell sites [7]. The main decision parameters include the Received Signal Quality (RSQ) at the UEs or the Received Interference Power (RIP) at the cells.…”

Section: Interference-aware Algorithmsmentioning

confidence: 99%

“…The RSQ refers to the ratio of the RSS from a target cell to the interference at the UE site, while the RIP refers to the total received power from all cells or non-associated users in proximity. RSQ-based algorithms compare the RSQ of the serving and the neighbor cells, or allow inbound mobility to a small cell whenever the respective RSQ is higher than a fixed threshold [7]. RIP-based algorithms emphasize on reducing interference in the uplink direction.…”

Section: Interference-aware Algorithmsmentioning

confidence: 99%

“…The emphasis is given both on revealing the strong and weak aspects for each HO decision approach, as well as on algorithm performance comparisons. The HO algorithms under scope are the ones in [5], [6], [7], [8], and [9], which will be referred to as RSS-based, speed-based, interference-aware, energy-efficient, and cost-function, respectively. The system-level simulations are conducted according to the Small Cell Forum evaluation methodology [4].…”

Section: Simulation Studymentioning

confidence: 99%

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Handover decision for small cells: Algorithms, lessons learned and simulation study

et al. 2016

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More and more cellular network operators enable the unplanned deployment of small-sized cellular stations by the end users into the predominant macrocellular network layout. This increases the spatial capacity of the cellular system and reduces the costs for installing, managing, and operating the radio access network. However, the impact of such an unplanned network densification on the robustness of cell handover (HO) still remains unclear and needs to be studied. For this purpose, in this paper we highlight the key aspects of the cell HO process in the presence of small cells and identify the main issues that affect its robustness. We summarize lessons learned from the rich literature on HO decision algorithms for small cells, and present an algorithm for alleviating interference in the cellular uplink while prolonging the battery lifetime of the user terminal. Based on the evaluation methodology of the Small Cell Forum, we conduct a comprehensive system-level simulation study to validate the accuracy of our findings and provide valuable insights on the key performance trade-offs inherent to the HO decision for small cells . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Abstract -More and more cellular network operators enable the unplanned deployment of small-sized cellular stations by the end users into the predominant macrocellular network layout. This increases the spatial capacity of the cellular system and reduces the costs for installing, managing, and operating the radio access network. However, the impact of such an unplanned network densification on the robustness of cell handover (HO) still remains unclear and needs to be studied. For this purpose, in this paper we highlight the key aspects of the cell HO process in the presence of small cells and identify the main issues that affect its robustness. We summarize lessons learned from the rich literature on HO decision algorithms for small cells, and present an algorithm for alleviating interference in the cellular uplink while prolonging the battery lifetime of the user terminal. Based on the evaluation methodology of the Small Cell Forum, we conduct a comprehensive system-level simulation study to validate the accuracy of our findings and provide valuable insights on the key performance trade-offs inherent to the HO decision for small cells.

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Section: Interference-aware Algorithmsmentioning

confidence: 99%

Section: Interference-aware Algorithmsmentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

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Handover decision for small cells: Algorithms, lessons learned and simulation study

et al. 2016

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“…Thus, these networks are prone to unplanned deployment in many cases (the client is free to locate the femtocell anywhere). The authors in [18], proposed a method to adjust hysteresis margins depending on an estimation of the distance from the base station to the user equipment (UE), reduces the number of redundant handovers (HOs) while keeping the throughput of femtocells as high as possible. The work shown in [19] studied the persistent congestion problems on traffic distribution in LTE femtocell enterprise scenario.…”

Section: Related Workmentioning

confidence: 99%

Improving load balancing techniques by location awareness at indoor femtocell networks

Aguilar-Garcı́a

Fortes

Garrido³

et al. 2016

J Wireless Com Network

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Femtocells are the auspicious solution for the coverage and capacity challenges that mobile operators are currently facing indoors. Whereas some operators are focused on offloading data traffic from macrocells to femtocells, others prefer to ensure indoor coverage and prioritize voice traffic in order to mitigate the negative impact of blocked or dropped calls. In this context, this paper proposes a novel self-optimizing mechanism to prevent congestion problems at indoor environments, focused on operators that prioritize voice traffic connections. A challenge specific to femtocells is that they normally can only serve a small number of simultaneous connections. Based on this restriction, the proposed mechanism resizes femtocell coverage areas by tuning their transmission powers for load balancing purposes. How information on user location can enhance the optimization process is also investigated in this paper. According to this, two methods to estimate the received signal level per user are evaluated. Finally, the performance is studied by means of both simulations and a real field trial.

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“…Thus, the actual HM level according to SINR is derived as follows: HM is the minimum HM that can be set up equal to 0 [9].…”

mentioning

confidence: 99%

Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

Ray¹,

Tang²

2015

IJFCC

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Abstract-Long term evolution (LTE) heterogeneous network can represent improvement of cell coverage and network capacity by adopting advanced physical layer techniques. In heterogeneous network, one of the major challenges is the handover decision between different eNBs. The utilization of the picocells results to use more network resources and handover procedure. In this paper, we investigate the hysteresis margin and load balancing problem in a 3GPP LTE heterogeneous network. Firstly, we consider A3 event, Hysteresis Margin (HM) and Cell Individual Offset (CIO) objectives are not fixed for every cells. Then we analyze the complexity of the problem and propose a practical algorithm which calculate adaptive hysteresis margin and load balancing in heterogeneous network. The results show that our algorithm can lead to significantly better performances, such as redundant handovers reduction and improvement of the network performance.Index Terms-LTE heterogeneous network, hysteresis margin, cell individual offset, load balancing.

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Adaptive Hysteresis Margin for Handover in Femtocell Networks

Cited by 59 publications

References 7 publications

Handover decision for small cells: Algorithms, lessons learned and simulation study

Handover decision for small cells: Algorithms, lessons learned and simulation study

Improving load balancing techniques by location awareness at indoor femtocell networks

Hysteresis Margin and Load Balancing for Handover in Heterogeneous Network

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