Researchers have called out for more transparency from The Public Health Agency of Sweden regarding the COVID-19 estimates for Sweden. Recently, a report has been released covering such estimates for the Stockholm region. Along the report, the code used for these estimates was uploaded to Github, which makes it possible for others to review and critique the work. In this post we will take a look at the reproducibility aspects of this release. We find that it is possible to some extent reproduce the figures in the report, and we suggest many improvements to the repository.
Machine Learning (ML) is an important enabler for optimizing, securing and managing mobile networks. This leads to increased collection and processing of data from network functions, which in turn may increase threats to sensitive end-user information. Consequently, mechanisms to reduce threats to end-user privacy are needed to take full advantage of ML. We seamlessly integrate Federated Learning (FL) into the 3GPP 5G Network Data Analytics (NWDA) architecture, and add a Multi-Party Computation (MPC) protocol for protecting the confidentiality of local updates. We evaluate the protocol and find that it has much lower overhead than previous work, without affecting ML performance.
Figures in scientific papers often look less than professional, and sometimes this can even get in the way of understanding the figure. In this blog post we show how to use matplotlib and tikzplotlib to make publication ready figures that look great and can be styled from the document preamble. Beautiful and understandable figures can possibly lead to higher publication acceptance rate, at least I hope so…
In the future, different intelligent systems will need to share data and experiences with each other to become good enough for certain tasks. RISE Industrial PhD student Martin Isaksson’s research is an important step on the way. This area is highly relevant for Ericsson and the development of its 5G but lessons learned along the way might open for many more solutions.
There is a rapid evolution in telecommunication with denser networks and systems operating on an increasing number of frequency bands. Also in the next generation 5G networks, even further densification is needed to be able to reach the tight requirements, implying more nodes on each carrier. Denser networks and more frequencies makes it challenging to ensure the best possible cell and frequency carrier assignment to a User Equipment (UE), without the UE needing to perform an excessive amount of inter-frequency measurements and reporting. In this paper, we propose a procedure of predicting the strongest cell of a secondary carrier, and the procedure is exemplified in a UE load-balancing use case. The prediction is based on only measurements on the primary carrier cells, avoiding costly inter-frequency measurements. Simulations of a realistic network deployment show that a UE selection based on the proposed secondary carrier prediction is significantly better than a random UE selection for load balancing.
Sometimes, when I come across a Powerpoint presentation with multiple multi-slice pie charts on a single slide my head hurts and I have to go air-poop for half-an-hour. Now I have decided to take action!