Rahul Kumar Jaiswal has followed the PhD Programme at the Faculty of Engineering and Science, specialisation in ICT, Artifical Intelligence.
- Trial lecture starts at 11.15
- Public defence starts at 13.15
Title of trial lecture: "Generative AI for network management and planning".
Disputation chair: Folke Haugland, Department of Information and Communication Technology, UiA
Assessment committee:
- First opponent: Principal Research Scientist: Dr. Ahmed Elmokashfi, Amazon Research Labs
- Second opponent: Associate Professor Ferhat Özgur Catak, UiS
- Chair of assessment committee: Professor Lei Jiao, UiA
Supervisors in the doctoral work:
- Main supervisor: Baltasar Beferull Lozano, WISENET/UiA
- Co-supervisor: Siddharth Deshmukh, UiA
- Co-supervisor: Postdoctoral Fellow Mohamed Ghafar Ahmed Elnourani, SIMULA
Summary of thesis
Radio maps provide information about spatial signal strength and network coverage
in a designated geographical area. The estimation of accurate radio maps is necessary
to improve the performance of many applications of future wireless networks.
For instance, localization, network planning, and resource allocation, to name a
few. To obtain accurate radio maps, the exact knowledge of transmitter (Tx) and
receiver (Rx) locations can be used. This is known as the location-based method.
However, in practice, wireless networks incur a high degree of multipath. As a
result, it is difficult to obtain accurate locations of Rxs. Alternatively, time of arrival
(ToA) features of radio signals, which are easier to obtain, can be used. This
is known as the location-free method. One of the ways to incorporate both methods
is the mixture of experts (MoE).
Due to changes in the propagation characteristics of wireless networks, a radio
map model designed under a particular wireless environment (source environment)
can not be directly used in a new wireless environment (target environment). Moreover,
designing a new radio map model for each new wireless environment requires
a huge amount of measurement samples and may need substantial computational
resources and data acquisition costs.
To address these issues, in this dissertation, we propose a series of transfer learning
(TL) schemes using each of the aforementioned methods to estimate radio maps
in new wireless environments where there is a scarcity of measurement samples. To
this end, we first train a radio map model in a source wireless environment and then
transfer it to another similar but still different target wireless environment. It is
then fine-tuned using a small amount of samples of the target wireless environment
to reduce the data acquisition cost.
For such a scheme, the similarity between two wireless environments controls the
effectiveness of the TL operation. Therefore, to quantify the similarity, we investigate
different classical similarity measures including the widely used Wasserstein
distance. Numerically, we show that these classical measures do not perform well in
the context of TL for radio map estimation. To overcome the limitations of these
classical measures, we design a data-driven similarity measure (DDS), which is able
to capture all the variations of wireless environments and can learn the wireless propagation characteristics directly from the data. Additionally, our DDS can predict
the amount of training data needed to estimate radio maps in new target wireless
environments when performing the TL operation. Experiments show that our proposed TL schemes perform efficiently with high model accuracy and save a substantial amount of sensor measurement data. Different models are designed for each of the cases of location-based, location-free, and MoE-based radio map estimation. Numerical experiments showcase the performance of each case, respectively.
Finally, we investigate the application of TL between two different optimization
problems of joint resource allocation (channel assignment and power allocation) in
underlay D2D communication. The resource allocation model trained on the dataset
obtained from the perfect channel state information (CSI) scenario is transferred
to the imperfect CSI scenario and then fine-tuned. The experiment shows that TL
improves the performance of the imperfect CSI scenario with less amount of training data.
What to do as an online audience member
The disputation is open to the public. To follow the trial lecture and the public defence online, register on Zoom.
We ask online audience members to join no earlier than 10 minutes in advance. After these times, you can leave and rejoin the meeting at any time.
Opponent ex auditorio:
Deadline for the public to pose questions is during the break between the two opponents. Questions ex auditorio can be submitted to Emma Horneman.
Contact person
- kristine.reinfjord@uia.no
- Phone
- +47 37 23 30 14