Dynamic Optimization of Neighbor Cell List for Femtocells

Bečvář, Zdeněk; Vondra, Michal; Mach, Pavel

doi:10.1109/vtcspring.2013.6692526

Cited by 16 publications

(16 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other ways to dynamically build the NCL via crowdsourcing are presented in [18,24]. A similar work applied to WiMax is presented in [25], and a closely related approach for the femtocell case is presented in [26].…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, small cells are often not able to detect first-and secondhop neighbours reliably due to hidden-node effects and the absence of an efficient sniffing Common Pilot Channel (CPICH) mechanism. A technique to construct the NCL via crowdsourcing has been proposed in [26]. However, the implementation of such a technique would first require the evaluation of the time scale of the NCL construction and its comparison with the time scale of the convergence of the code allocation algorithm.…”

Section: Small Cells Self-mentioning

confidence: 99%

See 1 more Smart Citation

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

Checco

Lancia

Leith

2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In modern wireless networks deployments, each serving node needs to keep its Neighbour Cell List (NCL) constantly up to date to keep track of network changes. The time needed by each serving node to update its NCL is an important parameter of the network's reliability and performance. An adequate estimate of such parameter enables a significant improvement of self-configuration functionalities. This paper focuses on the update time of NCLs when an approach of crowdsourced user reports is adopted. In this setting, each user periodically reports to the serving node information about the set of nodes sensed by the user itself. We show that, by mapping the local topological structure of the network onto states of increasing knowledge, a crisp mathematical framework can be obtained, which allows in turn for the use of a variety of user mobility models. Further, using a simplified mobility model we show how to obtain useful upper bounds on the expected time for a serving node to gain Full Knowledge of its local neighbourhood.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Small Cells Self-mentioning

confidence: 99%

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

Checco

Lancia

Leith

2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…It means the scanning period suitable for one MeNB with a given density of SCeNBs can lead to ignorance of the SCeNBs or to redundant scanning in another MeNB. Modification of the scanning interval of individual cells is exploited also in [16]. The authors suggest to select scanned cells according to the probability of handover to the given cell and SINR observed by the UE from its serving cell.…”

Section: Related Workmentioning

confidence: 99%

“…The movement of those UEs is based on Manhattan mobility model with Points of Interests (POIs) using graph theory approach as described in [16]. The model in [16] assumes constant speed of users. For our algorithms, the speed can influence the performance significantly.…”

Section: Simulation Model and Scenariomentioning

confidence: 99%

Distance-Based Neighborhood Scanning for Handover Purposes in Network with Small Cells

Vondra

Bečvář

2016

IEEE Trans. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

Deployment of small cells into existing mobile networks can improve throughput and users quality of service. However, the new tier composed of the small cells raises problems related to management of user's mobility. Moving users must be able to discover cells in their neighborhood. For this purpose, the users perform neighborhood scanning. The scanning process should be frequent enough to avoid situations when the user is not aware of a close cell as this one has not been scanned. However, the frequent scanning of a high number of neighboring cells leads to wasting battery of user equipments and to a reduction of user's throughput. Contrary, rare scanning can lead to a situation when a small cell is missing in the list of scanned cells and, thus, handover to it is not performed. It results in underutilization of the small cells and consequent overloading of macro cells. In this paper, we propose an efficient scanning algorithm suitable for future mobile networks. The objective of the proposed scheme is to maximize utilization of the small cells and to minimize energy consumption due to scanning. The proposal exploits graph theory to represent a principle of obstructed paths in combination with knowledge of previous visited cell and estimated distance between cells. As the results presented in the paper show, our algorithm reduces energy consumption due to scanning and enables higher exploitation of small cells by offloading macrocells.

show abstract

“…This move without stopping does not affect the simulation results. Detailed description of the user's movement, including movement types and probability of visiting particular POIs can be found in [14]. Other simulation parameters are listed in Table 1.…”

Section: Simulation Modelmentioning

confidence: 99%

Self-configured Neighbor Cell List of macro cells in network with Small Cells

Vondra

Bečvář

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

Self Cite

View full text Add to dashboard Cite

To ensure faultless handover procedure of mobile users, each cell in the network must establish a Neighbor Cell List (NCL). The NCL contains adjacent cells to which handover can be performed. A problem related to the handover from a Macro/Micro cell (MeNB) appears with the rising density of cells with the limited coverage, so-called Small Cells (SCeNBs). By using method of the NCL creation described in standards or literature, the number of neighboring cells suitable for handover from the MeNB can be extensive. Therefore, we propose an algorithm for automatic creation of the NCL with reduced number of included cells. The proposed algorithm exploits knowledge of the last visited cell in combination with the statistical information on performed handovers in the past to determine the possibility of transition to the neighboring cells. As the results show, the proposed algorithm significantly reduces the number of cells in the NCL while the probability of missing handover target cell in the NCL is kept negligible.

show abstract

Dynamic Optimization of Neighbor Cell List for Femtocells

Cited by 16 publications

References 13 publications

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

Updating Neighbour Cell List via Crowdsourced User Reports: A Framework for Measuring Time Performance

Distance-Based Neighborhood Scanning for Handover Purposes in Network with Small Cells

Self-configured Neighbor Cell List of macro cells in network with Small Cells

Contact Info

Product

Resources

About