Mobility Sensitivity Analysis for LTE-Advanced HetNet Deployments with Dual Connectivity

Barbera, Simone; Gimenez, Lucas Chavarria; Sanchez, Laura Luque; Pedersen, Klaus I.; Michaelsen, Per Henrik

doi:10.1109/vtcspring.2015.7145937

Cited by 6 publications

(15 citation statements)

References 9 publications

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“…On each time-step the RSRP, RSRQ and signal-to-interference-plusnoise-ratio (SINR) for each user are calculated, followed by the SINR to throughput mapping estimation. The tool has been used in several standardization and research studies, such as [3], [11]. Additional simulator modeling can be found in [12].…”

Section: Scenario and Simulation Methodologymentioning

confidence: 99%

“…The most common triggering conditions used for initiating the cell management events, are shown in Figure 1. MeNB handovers are typically triggered by the A3 event (neighboring cell becomes an offset better than the serving cell), based on the RSRP [3]. Moreover, the addition of a secondary data link, or SeNB addition, is normally triggered by the A4 event (a neighbor small cell becomes better than a certain threshold), based on the RSRQ.…”

Section: Network-controlled and Ue-assisted Mobilitymentioning

confidence: 99%

“…Previous studies on mobility performance show that scenarios with DC are affected by high rates of mobility (or cell management) events [3], [4]; therefore, challenging the mobility management, specially in scenarios with users traveling at high-speeds. The mobility management in current cellular networks relies on a network-controlled mechanism, assisted by the user equipment (UE), in which the network decides when mobility events should take place based on radio resource management (RRM) measurements reported by the UE.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

UE autonomous cell management in a high-speed scenario with dual connectivity

Gimenez

Michaelsen²,

Pedersen

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-This study compares the amount of control signaling required by traditional network-controlled mobility management with the one required by user equipment autonomous cell management operations in a real-life highway scenario. The scenario is covered by macros and densely-deployed small cells. Different strategies for preparing the small cells for autonomous operations are studied. Our results show that traditional dual connectivity requires an average of 4.9 messages, per user per second, to be exchanged between the user equipment and the network, and 11.6 messages between e-NodeBs. On the other hand, autonomous cell management operations considerably decrease the amount of signaling. The highest reductions can be achieved by preparing all cells along the highway, cutting the signaling overhead by 92 % over the air, and 39 % between e-NodeBs. Furthermore, the approach of applying a newly developed window-based feature for preparing the cells brings significant benefits.

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Section: Scenario and Simulation Methodologymentioning

confidence: 99%

Section: Network-controlled and Ue-assisted Mobilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UE autonomous cell management in a high-speed scenario with dual connectivity

Gimenez

Michaelsen²,

Pedersen

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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“…The SmallCs are placed along urban streets, and we use a frequency reuse scheme deployment as proposed in [9,Scheme 4] in order to avoid inter-cell interference. The MgNB establishes a control interface to the core network (CN), while both the MgNB and the SgNB manage the radio resource control (RRC) in the UE [5]. Moreover, the MgNB RRC connection is established before MC, and is used to configure and control CSI feedback and channel measurements in the UE.…”

Section: A Multi-connectivity and Small Cell Mobilitymentioning

confidence: 99%

Predictive Network Control in Multi-Connectivity Mobility for URLLC Services

Guzman

Schoeffauer

Wunder

2019

2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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This paper proposes a centralized predictive flow controller to handle multi-connectivity for ultrareliable low latency communication (URLLC) services. The prediction is based on channel state information (CSI) and buffer state reports from the system nodes. For this, we extend CSI availability to a packet data convergence protocol (PDCP) controller. The controller captures CSI in a discrete time Markov chain (DTMC). The DTMC is used to predict forwarding decisions over a finite time horizon. The novel mathematical model optimizes over finite trajectories based on a linear program. The results show performance improvements in a multi-layer small cell mobility scenario in terms of end-to-end (E2E) throughput. Furthermore, 5G new radio (NR) complaint system level simulations (SLS) and results are shown for dual connectivity as well as for the general multi-connectivity case.

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“…The simulator has been utilized in several mobility studies, for 3G and 4G, involving 3GPP ( [7], [8]) and site-specific scenarios ( [6], [9]). Mobility parametrization discovered by the measurements and the network information provided by the operator are taken into account to align simulations with measurements.…”

Section: Measurements and Simulations Comparisonmentioning

confidence: 99%

Mobility Performance in Slow- and High-Speed LTE Real Scenarios

Gimenez

Cascino

Stefan

et al. 2016

2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)

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Abstract-Mobility performance and handover data interruption times in real scenarios are studied by means of field measurements in an operational LTE network. Both slow-and high-speed scenarios are analyzed by collecting results from two different areas: Aalborg downtown and the highway which encircles the same city. Measurements reveal that the terminal is configured by the network with different handover parametrization depending on the serving cell, which indicates the use of mobility robustness optimization. Although the network is dominated by threesector sites, no intra-site handovers are observed in the city center as cells on the same site often cover different noncrossing street canyons. Moreover, no handover failures are experienced in the measurements which confirms robust LTE mobility performance. The average interruption time, which is at least equal to the handover execution time, lays within a 24-29 ms interval. Nevertheless, examples of delays larger than 100 ms are occasionally observed. The studied scenarios are replicated in a system level simulator to investigate whether simulations are capable of reproducing similar mobility performance.

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Mobility Sensitivity Analysis for LTE-Advanced HetNet Deployments with Dual Connectivity

Cited by 6 publications

References 9 publications

UE autonomous cell management in a high-speed scenario with dual connectivity

UE autonomous cell management in a high-speed scenario with dual connectivity

Predictive Network Control in Multi-Connectivity Mobility for URLLC Services

Mobility Performance in Slow- and High-Speed LTE Real Scenarios

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