Master Thesis: Macro Offload Benefits from an Indoor Deployment
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About this opportunity:
This is an opportunity for a Master of Science student to work with real network data together with state-
of-the-art network simulator.
In an area, a mobile operator deploys both indoors and outdoors radio access network (RAN) to serve
both indoor and outdoor users. Indoor deployments are usually low-power small-cell solution for
commercial buildings, e.g. stadiums, shopping malls, railways stations, & airports, to serve in-building
users. On the other hand, an outdoor high-power consuming base-station is a macro/large cell solution
serving both indoor and outdoor users. However, in a typical urban scenario, 70-90% of the data-traffic in
outdoor macro base-stations are still generated or consumed by in-building/indoor users. This leads to an
interesting research question: "How can we optimize the power consumption of the whole network with an
optimal mix of indoor and outdoor deployment without compromising on the user-experience".
What you will do:
The objective of the Master Thesis is to investigate what gain (if any) we get in terms of capacity,
throughput, and power consumption by offloading macro traffic to an optimal mix of macro and indoor
deployment in an area type. One possible way could be by deploying indoor solution into some of the
selected buildings and comparing the network level results with no indoor deployment. Moreover, if we
know that in an area, most of the traffic is generated or consumed indoors, especially in commercial
areas, possible gain in terms of energy consumption can be estimated by only indoor deployment when
compared to only outdoor macro covering that commercial area. Another objective of the thesis is also
investigating an optimal mix of indoor small-cells and outdoor macro solution without compromising on the
user-experience of both indoors and outdoors users. The analysis can be done by analyzing the real
network data (performance management counters) and doing investigations by simulating in a state-of-
the-art network simulator.
The project is intended for one master thesis student and is expected to be performed in Kista for a
duration of 6 months, starting in 2026 Q1.
The skills you bring:
You should be a Master of Science student in Electrical Engineering, Computer/Data Science, or similar.
Courses in digital communications and signal processing, as well as programming skills in MATLAB or
Python are required. Experience of wireless communication systems, statistical approach and machine
learning are valuable merits but are not required.
The successful candidate must have
• Excellent grades
• Fluent in English, both written and spoken
• Good MATLAB/Python skills
• Good communications skills
• You are a self-motivated and positive person.
• Experience with Statistical and Machine Learning approach is a bonus.
About this opportunity:
This is an opportunity for a Master of Science student to work with real network data together with state-
of-the-art network simulator.
In an area, a mobile operator deploys both indoors and outdoors radio access network (RAN) to serve
both indoor and outdoor users. Indoor deployments are usually low-power small-cell solution for
commercial buildings, e.g. stadiums, shopping malls, railways stations, & airports, to serve in-building
users. On the other hand, an outdoor high-power consuming base-station is a macro/large cell solution
serving both indoor and outdoor users. However, in a typical urban scenario, 70-90% of the data-traffic in
outdoor macro base-stations are still generated or consumed by in-building/indoor users. This leads to an
interesting research question: "How can we optimize the power consumption of the whole network with an
optimal mix of indoor and outdoor deployment without compromising on the user-experience".
What you will do:
The objective of the Master Thesis is to investigate what gain (if any) we get in terms of capacity,
throughput, and power consumption by offloading macro traffic to an optimal mix of macro and indoor
deployment in an area type. One possible way could be by deploying indoor solution into some of the
selected buildings and comparing the network level results with no indoor deployment. Moreover, if we
know that in an area, most of the traffic is generated or consumed indoors, especially in commercial
areas, possible gain in terms of energy consumption can be estimated by only indoor deployment when
compared to only outdoor macro covering that commercial area. Another objective of the thesis is also
investigating an optimal mix of indoor small-cells and outdoor macro solution without compromising on the
user-experience of both indoors and outdoors users. The analysis can be done by analyzing the real
network data (performance management counters) and doing investigations by simulating in a state-of-
the-art network simulator.
The project is intended for one master thesis student and is expected to be performed in Kista for a
duration of 6 months, starting in 2026 Q1.
The skills you bring:
You should be a Master of Science student in Electrical Engineering, Computer/Data Science, or similar.
Courses in digital communications and signal processing, as well as programming skills in MATLAB or
Python are required. Experience of wireless communication systems, statistical approach and machine
learning are valuable merits but are not required.
The successful candidate must have
• Excellent grades
• Fluent in English, both written and spoken
• Good MATLAB/Python skills
• Good communications skills
• You are a self-motivated and positive person.
• Experience with Statistical and Machine Learning approach is a bonus.
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