#### Event Title

The Study of Bandwidth Allocation and Price Point on Revenue in Hetergeneous Networks

#### Session Number

H01

#### Advisor(s)

Randall Berry, Northwestern University

#### Location

B-108

#### Start Date

28-4-2016 9:15 AM

#### End Date

28-4-2016 9:40 AM

#### Abstract

In order to satisfy the popularity of internet-connected devices and the demands for faster speeds, network companies have moved towards heterogeneous networks. Cellular providers have a limited bandwidth and must allocate it across its different cell types in order to best optimize revenue. We used MatLab to create a model which optimizes bandwidth allocation across a heterogeneous network consisting of both femtocells and macrocells. With the model we input utility- functions describing a user’s willingness to pay for a given rate. The computational model justifies other similar mathematical based models. This permits us, to in the future, test other more complicated utility functions with greater ease. Using the model, we can also extrapolate patterns in reference to the some of the model’s parameters. For example, we plan to examine the relationship between max revenue and bandwidth allocation with the density of users of both cell types. By assuming a pre-existing macrocell network and assigning a constant installation cost for femtocells, we plan to use the model to determine the optimal number of femtocells for a network.

The Study of Bandwidth Allocation and Price Point on Revenue in Hetergeneous Networks

B-108

In order to satisfy the popularity of internet-connected devices and the demands for faster speeds, network companies have moved towards heterogeneous networks. Cellular providers have a limited bandwidth and must allocate it across its different cell types in order to best optimize revenue. We used MatLab to create a model which optimizes bandwidth allocation across a heterogeneous network consisting of both femtocells and macrocells. With the model we input utility- functions describing a user’s willingness to pay for a given rate. The computational model justifies other similar mathematical based models. This permits us, to in the future, test other more complicated utility functions with greater ease. Using the model, we can also extrapolate patterns in reference to the some of the model’s parameters. For example, we plan to examine the relationship between max revenue and bandwidth allocation with the density of users of both cell types. By assuming a pre-existing macrocell network and assigning a constant installation cost for femtocells, we plan to use the model to determine the optimal number of femtocells for a network.