Coverage Analysis of Broadcast Networks with Users Having Heterogeneous Content/Advertisement Preferences

TL;DR

This paper develops an analytical framework to evaluate broadcast network coverage considering users with diverse content preferences, revealing optimal connectivity strategies and the impact of content diversity on revenue.

Contribution

It introduces a stochastic geometry model that accounts for heterogeneous user preferences and analyzes how content diversity affects coverage and operator revenue.

Findings

Existence of an optimal connectivity radius for maximum coverage.

Content diversity influences revenue and coverage performance.

Geographical and non-geographical user interest scenarios analyzed.

Abstract

This work is focused on the system-level performance of a broadcast network. Since all transmitters in a broadcast network transmit the identical signal, 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 prefers a particular type of content/advertisements and the users will pay the network only when then can see content aligned with their interest. The operator may choose to transmit multiple contents simultaneously to cater 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 one 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.