Optimization of SINR-based Neighbor Cell List for networks with small cells

Bečvář, Zdeněk; Mach, Pavel; Vondra, Michal

doi:10.1109/pimrc.2013.6666537

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Furthermore, to construct a self-organised network as one of the future requirements, a newly deployed eNB has to be capable of automatically self-configuring the NCL and continuously self-optimising it. Among the most common algorithms for this purpose, an eNB autonomously scans the broadcast pilot signals from adjacent cells and creates an initial NCL based on the signal-to-interference-plusnoise ratio (SINR) [23], [24], after which the eNB maintains the NCL based on the SINR and other information collected by UEs [24], [25]. Since the self-organisation of the NCL goes beyond the scope of the study in this paper, it is assumed that each eNB B i ∈ B initialises the NCL N i of B i based on the pilot signals and the NCL is not updated during the lifetime of B i .…”

Section: B Neighbour Cell Listmentioning

confidence: 99%

Traffic-Aware Cell Management for Green Ultradense Small-Cell Networks

Grace

Mitchell

2017

IEEE Trans. Veh. Technol.

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To reduce the power consumption of 5G ultradense small cell networks, base stations can be switched to low power sleep modes when local traffic levels are low. In this paper, a novel sleep mode control algorithm is proposed to control such sleep modes. The algorithm innovates a concept called Traffic-Aware Cell Management (TACM). It involves cell division, cell death and cell migration to represent adaptations of networks, where state transitions of base stations are controlled. Direction of arrival is adopted for distributed decision making. The TACM algorithm aims at reducing the network power consumption while alleviating the impacts of applying sleep modes, such as mitigating system overheads and reducing user transmission power. The TACM algorithm is compared with a recent consolidated baseline scheme by simulation on networks with unbalanced traffic distributions and with base stations at random locations. In contrast, the TACM algorithm shows a significant improvement in mitigating system overheads due to no load information exchange overhead and up to 72 times less switching frequency. Up to 81% network power consumption can be reduced compared with the baseline scheme if considering high energy consumption of switching transient states. In addition, at a low traffic level, average uplink transmission power is reduced by 79% comparatively. Furthermore, the impact of important performance governing parameters of the TACM algorithm is analysed. The insensitivity to the estimation accuracy of direction of arrival is also demonstrated. The results show that the proposed TACM algorithm has a comprehensive advantage of power reduction and overhead mitigation over the baseline scheme.

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Section: B Neighbour Cell Listmentioning

confidence: 99%

Traffic-Aware Cell Management for Green Ultradense Small-Cell Networks

Grace

Mitchell

2017

IEEE Trans. Veh. Technol.

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“…Note that the model is easily extensible to other systems, such as Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX), since most systems already dispose of methods to measure the received power, which is used, at least, during connection establishment [1], [15].…”

Section: A Collecting Power Measurements From Neighbor Cell Listmentioning

confidence: 99%

“…• Phases 1 to 4: may be done at every 200 ms to 1 s or 200 to 1000 Transmission Time Intervals (TTIs) [15]; • Phases 5 to 7: may be done at each new service request or whenever the MS is scheduled. …”

Section: Storage and Exchange Of Neighborhood Matrixmentioning

confidence: 99%

Network-Assisted Neighbor Discovery Based on Power Vectors for D2D Communications

Silva¹,

Maciel²,

Batista³

et al. 2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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Device-to-Device (D2D) communication is seen as a promising technology to increase the capacity of current wireless systems without extra electromagnetic spectrum bands. However, before starting a D2D communication, Mobile Stations (MSs) must be aware of their neighbors, through a discovery process. While operating in cellular networks, such process may benefit from the network-assistance. In this paper, we propose a method based on the available network power measures to improve the discovery process. Results show that our proposal is less complex but still outperforms traditional methods when considering the time to detect all neighbors.

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