# LUP Student Papers

## LUND UNIVERSITY LIBRARIES

### Mitigation of Inter-Cell Interference for 5G using machine/deep learning

(2021) In Master's Theses in Mathematical Sciences FMSM01 20202
Mathematical Statistics
Abstract
Since the beginning of wireless communication, one of the central problems has
been resource management and maximization of network throughput. Solving
these recurring issues is an important topic in the field of wireless communication
to increase application options and the user experience. The existing methods for
solving the issue of inter cell interference by maximizing the network capacity are
mathematically complex.
In this thesis, I present a machine learning-based solution for the issue of
Square Error (WMMSE) for resource management, we can use a Deep Neural
Network (DNN) to map an input to a desired output. If the mapping can be
learned... (More)
Since the beginning of wireless communication, one of the central problems has
been resource management and maximization of network throughput. Solving
these recurring issues is an important topic in the field of wireless communication
to increase application options and the user experience. The existing methods for
solving the issue of inter cell interference by maximizing the network capacity are
mathematically complex.
In this thesis, I present a machine learning-based solution for the issue of
Square Error (WMMSE) for resource management, we can use a Deep Neural
Network (DNN) to map an input to a desired output. If the mapping can be
learned accurately by a DNN in offline learning, then such a DNN can be used for
resource management in real time; due to the simple matrix-operations utilized
when an output is calculated using a neural network.
In this work, I present the traditional WMMSE-solution, and then design a
DNN to approximate the input-output relation for all users in a cellular network.
The WMMSE and DNN are used to calculate transmission power and are then
compared to a baseline case where power is distributed equally, only dependent
on the number of users in a cell. In this comparison, I use known metrics to
demonstrate the ability of the DNN to approximate the WMMSE algorithm that
is designed for DLPC. It will also be shown that the abilities of the two methods,
especially the DNN, exceeds the ability of the baseline case in terms of network
throughput and user experienced SINR. (Less)
author
supervisor
organization
course
FMSM01 20202
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3405-2021
ISSN
1404-6342
other publication id
2021:E2
language
English
id
9060953
2021-07-06 09:46:56
date last changed
2021-07-06 15:00:40
```@misc{9060953,
abstract     = {{Since the beginning of wireless communication, one of the central problems has
been resource management and maximization of network throughput. Solving
these recurring issues is an important topic in the field of wireless communication
to increase application options and the user experience. The existing methods for
solving the issue of inter cell interference by maximizing the network capacity are
mathematically complex.
In this thesis, I present a machine learning-based solution for the issue of
Square Error (WMMSE) for resource management, we can use a Deep Neural
Network (DNN) to map an input to a desired output. If the mapping can be
learned accurately by a DNN in offline learning, then such a DNN can be used for
resource management in real time; due to the simple matrix-operations utilized
when an output is calculated using a neural network.
In this work, I present the traditional WMMSE-solution, and then design a
DNN to approximate the input-output relation for all users in a cellular network.
The WMMSE and DNN are used to calculate transmission power and are then
compared to a baseline case where power is distributed equally, only dependent
on the number of users in a cell. In this comparison, I use known metrics to
demonstrate the ability of the DNN to approximate the WMMSE algorithm that
is designed for DLPC. It will also be shown that the abilities of the two methods,
especially the DNN, exceeds the ability of the baseline case in terms of network
throughput and user experienced SINR.}},
author       = {{Bayomi, Eltayeb}},
issn         = {{1404-6342}},
language     = {{eng}},
note         = {{Student Paper}},
series       = {{Master's Theses in Mathematical Sciences}},
title        = {{Mitigation of Inter-Cell Interference for 5G using machine/deep learning}},
year         = {{2021}},
}

```