Predicting cell phone adoption metrics using machine learning and satellite imagery

Oughton, Edward J.; Mathur, Jatin

doi:10.1016/j.tele.2021.101622

Cited by 12 publications

(5 citation statements)

References 85 publications

(58 reference statements)

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“…Technically, 5G is expected to support 1 million connected devices per km 2 [13], although this is a high-level target. In this evaluation, x is a 40% take-up rate [97], and the rural region of interest is treated as having between 1,000 to 25,000 mobile subscribers, along with thousands of devices for private networks and IoT applications. The upgraded 5G network would predominantly help to provide eMBB in rural areas while supporting other 5G rural applications relating to vertical sectors, such as health and transportation.…”

Section: B Cost Savings For Different Sharing Strategiesmentioning

confidence: 99%

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Kumar¹,

Oughton²

2023

Preprint

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Section: B Cost Savings For Different Sharing Strategiesmentioning

confidence: 99%

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Kumar¹,

Oughton²

2023

Preprint

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“…In this study, the population density for the base scenario is 36 pop/km 2 . Therefore, the rural region of interest in this study, especially sensitivity analysis, is treated as having between 1,000 to 25,000 mobile subscribers along with thousands of devices for private networks and IoT applications [139]. These subscribers could belong to local businesses or corporate companies.…”

Section: A Description Of the Study Areamentioning

confidence: 99%

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Kumar¹,

Oughton²

2023

Preprint

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“…this evaluation, for the existing technology with a takeup rate (x), 50% of P, consider that 30% of those users would upgrade to 5G services, and an additional 20% of P would join the network for 5G services. Therefore, the rural region of interest in this study, especially sensitivity analysis, is treated as having between 1,000 to 25,000 mobile subscribers along with thousands of devices for private networks and IoT applications (Oughton and Mathur, 2021). These subscribers could belong to local businesses or corporate companies.…”

Section: Description Of the Study Areamentioning

confidence: 99%

Techno-economic assessment of 5G infrastructure sharing business models in rural areas

Koratagere Anantha Kumar,

Oughton

2023

Front. Comput. Sci.

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How cost-efficient are potential infrastructure sharing business models for the 5G era (and beyond)? This significant question needs to be addressed if we are to deliver universal affordable broadband in line with Target 9.1 of the UN Sustainable Development Goals. Although almost two-thirds of the global population is now connected, many users still lack access to high-speed and reliable broadband connectivity. Indeed, some of the largest connectivity issues are associated with those living in areas of low economic viability. Consequently, this assessment evaluates the cost implications of different infrastructure sharing business models using a techno-economic assessment framework. The results indicate that a rural 5G neutral host network (NHN) strategy helps to reduce total cost between 10 and 50% compared with other sharing strategies. We also find that, compared to a baseline strategy with No Sharing, the net present value of rural 5G sharing strategies can earn between 30 and 90% more profit. The network upgrades to 5G using various sharing strategies are most sensitive to changes in the average revenue per user, the adoption rate, and the amount of existing site infrastructure. For example, the results from this study show that a 20% variation in demand revenue is estimated to increase the net present value of the sharing strategies by 2–5 times compared to the No Sharing strategy. Similarly, a 10% increase in existing infrastructure lowers the net present value by 8–30%. The infrastructure sharing strategies outlined in this study have the potential to enhance network viability while bridging the digital divide in remote and rural locations.

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Predicting cell phone adoption metrics using machine learning and satellite imagery

Cited by 12 publications

References 85 publications

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Techno-Economic Assessment of 5G Infrastructure Sharing Business Models in Rural Areas

Techno-economic assessment of 5G infrastructure sharing business models in rural areas

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