Abstract
The emergence of Ultra-Dense Networks (UDNs), a technology that boosts the performance
of fifth-generation (5G) telecommunication networks, has introduced significant
throughput loss due to network interference. Numerous types of interference are largely
reported in the literature. These include inter-cell interference (ICI) characterized by
distinct user equipment (UEs) competing for the use of the same frequency bandwidths.
In addition, there is also cell-edge interference (CEI) that results from UEs which
simultaneously receive signals from 5G equipment at different frequency bandwidths.
Co-channel interference (CCI) is another notable form of interference that is caused
by the presence of dense materials in between satellite and UEs (CRI) that leads to
two distinct points in the network using the same channels. Limitations in resource
allocation can also cause cross-tier interference if the number of UEs that are anticipated
to use the same resources grows significantly large. Co-tier interference (CTI)
is yet another example in which excessive use of frequency spectrum through reuse
causes interference among the supported UEs. This thesis presents distinct solutions
to these various types of network interference. The first solution in the thesis is a
hybridized model with a Long Short-Term Memory (LSTM) neural network component.
This LSTM model inputs twenty one features namely: uplink times, downlink times,
total online time, user displacement, user velocity, user position and many others,
and returns lag one future values of only six features namely: uplink times, downlink
times, total online time, user displacement, user velocity and user traffic load definition.
Upon successful training of the LSTM model, the model is used to forecast future
values of the 6 features with historic values of 21 features supplied as inputs to the
model. This is performed using the first set of unseen data to avoid training bias. The
estimated future values that are retrieved from the trained LSTM model are injected
as inputs in a Generalized Linear Model (GLM) that has its target variable as UEs’
required frequency bandwidths. In this manner, the thesis has successfully introduced
self-aware UDNs that can self-configure and restrict various UEs access to specific
frequency bandwidths in a manner that prohibits network interference and enhances