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Reena Sahu

dblp:274/5207 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless networking · 33% Cellular and mobile networks · 33% Content delivery and video streaming · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless networking › broadcast
broadcast network
0.612022
Coverage Analysis of Broadcast Networks With Users Having Heterogeneous Content/Advertisement Preferences · IEEE Trans. Commun. 2022
Content delivery and video streaming
content delivery optimization
0.612022
Coverage Analysis of Broadcast Networks With Users Having Heterogeneous Content/Advertisement Preferences · IEEE Trans. Commun. 2022
Cellular and mobile networks
coverage analysis
0.612022
Coverage Analysis of Broadcast Networks With Users Having Heterogeneous Content/Advertisement Preferences · IEEE Trans. Commun. 2022

Methods — techniques the papers use, named apart from their topics

stochastic geometry · 0.6numerical analysis · 0.6
YearPublicationVenuePosition
2022 Coverage Analysis of Broadcast Networks With Users Having Heterogeneous Content/Advertisement Preferences
abstract
This work is focused on the system-level performance of a broadcast network. Since all transmitters in a broadcast network transmit identical signals, received signals from multiple transmitters can be combined to improve system performance. We develop a stochastic geometry-based analytical framework to derive the coverage of a typical receiver. We show that there may exist an optimal connectivity radius that maximizes the rate coverage. Our analysis includes the fact that users may have their individual content/advertisement preferences. We assume that there are multiple classes of users with each user class preferring a particular type of content/advertisements and the users will pay the network only when they can see content aligned with their interest. The operator may choose to transmit multiple contents simultaneously to cater to more users’ interests to increase its revenue. We present revenue models to study the impact of the number of contents on the operator revenue. We consider two scenarios for users’ distribution- one where users’ interest depends on their geographical location and the other where it doesn’t. With the help of numerical results and analysis, we show the impact of various parameters including content granularity, connectivity radius, and rate threshold and present important design insights.
Kanchan K. Chaurasia, Reena Sahu, Shuchi Tripathi, Abhishek K. Gupta
IEEE Trans. Commun.2