A channel selection strategy for WLAN in urban areas by regression analysis

Kajita, Shugo; Yamaguchi, Hirozumi; Higashino, Teruo; Umehara, Shigeki; Saitou, Fumiya; Urayama, Hirofumi; Yamada, Masaya; Maeno, Taka; Kaneda, Shigeru; Takai, Mineo

doi:10.1109/wimob.2014.6962238

Cited by 10 publications

(5 citation statements)

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“…Therefore, wireless networks usually present a higher spectrum usage (in bits/s/MHz) as population density increases. This relationship is accepted in the literature for licensed (Clarke, 2014), and unlicensed spectrum (De Filippi and Tréguer, 2015;Kajita et al, 2014) .…”

Section: Population Density Effect On Operator Investmentmentioning

confidence: 93%

The mitigating role of regulation on the concentric patterns of broadband diffusion. The case of Finland

Benseny

Töyli

Hämmáinen

et al. 2019

Telematics and Informatics

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This article analyzes the role of Finnish regulation in achieving the broadband penetration goals defined by the National Regulatory Authority. It is well known that in the absence of regulatory mitigation the population density has a positive effect on broadband diffusion. Hence, we measure the effect of the population density on the determinants of broadband diffusion throughout the postal codes of Finland via Geographically Weighted Regression. We suggest that the main determinants of broadband diffusion and the population density follow a spatial pattern that is either concentric with a weak/medium/strong strength or non-concentric convex/concave. Based on 10 patterns, we argue that the Finnish spectrum policy encouraged Mobile Network Operators to satisfy ambitious Universal Service Obligations without the need for a Universal Service Fund. Spectrum auctions facilitated infrastructure-based competition via equitable spectrum allocation and coverage obligation delivery via low-fee licenses. However, state subsidies for fiber deployment did not attract investment from nationwide operators due to mobile preference. These subsidies encouraged demand-driven investment, leading to the emergence of fiber consumer cooperatives. To explain this emergence, we show that when population density decreases, the level of mobile service quality decreases and community commitment increases. Hence, we recommend regulators implementing market-driven strategies for 5G to stimulate local investment. For example, by allocating the 3.5 GHz and higher bands partly through local light licensing.

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Section: Population Density Effect On Operator Investmentmentioning

confidence: 93%

The mitigating role of regulation on the concentric patterns of broadband diffusion. The case of Finland

Benseny

Töyli

Hämmáinen

et al. 2019

Telematics and Informatics

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“…4.2.3 the advantages of adding kernel 𝑇 3 . We use a GCN with 4 graph convolution layers, each having 100 units, that is, 𝑑 (1) , 𝑑 (2) , 𝑑 (3) , 𝑑 (4) = 100, 𝑑 (5) = 1 and 𝑑 (0) = 1 or 1 + 𝑛, depending if node IDs are used. Using multiple convolutions allows to use distant neighbor information as well as to learn representations that capture structural equivalence between different nodes.…”

Section: Graph Convolutional Modelmentioning

confidence: 99%

“…Prior work estimated different network metrics and events. The most prominent ones treat throughput prediction [5,7,13], RSSI for handover prediction [6,8,9], interference [12], and AP selection based on other factors [1,6,14]. In last two cases, the studies rely on past measurements to predict future values, designing the problem as an auto-regressive process [9].…”

Section: Related Workmentioning

confidence: 99%

Cross-network transferable neural models for WLAN interference estimation

Fernandes¹,

Krolikowski²,

Houidi³

et al. 2022

Proceedings of the 1st International Workshop on Graph Neural Networking

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Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissions before to transmitting or receiving. Being able to accurately estimate interference resulting from a given state change (e.g., channel, bandwidth, power) would allow a better control of WLAN resources, assessing the impact of a given configuration before actually implementing it.In this paper, we adopt a principled approach to interference estimation in WLANs. We first use real data to characterize the factors that impact it, and derive a set of relevant synthetic workloads for a controlled comparison of various deep learning architectures in terms of accuracy, generalization and robustness to outlier data. We find, unsurprisingly, that Graph Convolutional Networks (GCNs) yield the best performance overall, leveraging the graph structure inherent to campus WLANs. We notice that, unlike e.g. LSTMs, they struggle to learn the behavior of specific nodes, unless given the node indexes in addition. We finally verify GCN model generalization capabilities, by applying trained models on operational deployments unseen at training time.

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“…I. (Lyons, 2014) Fiber (Izydorek et al, 2019;Lee et al, 2009) WiMAX (Smura et al, 2007) negative Mobile (Johansson et al, 2004;Ovando et al, 2015) Spectrum usage positive Unlicensed spectrum (De Filippi and Tréguer, 2015;Kajita et al, 2014) positive 4G bands (Clarke, 2014) Competition positive Number of operators (Durairajan and Barford, 2016;Grubesic, 2010) positive (no source)…”

Section: National Diffusionmentioning

confidence: 99%