Dino Pjanicaffiliated with the university, Master of Science in EE, telecommunication
Research areas and keywords
UKÄ subject classification
- Signal Processing
- Communication Systems
I have more than 20 years of work experience, mostly at Ericsson AB in Lund. During this time, I have been involved in many different projects from both development and testing perspective which not only strengthened my technical skills but also widened my views when it comes to different aspects of software and hardware development.
Having had the opportunity to work with both mobile devices and mobile networks, I have developed skills in areas such as embedded systems and telecommunications. My strongest field of expertise is within communication protocols for the radio access part of mobile communication systems. This constitutes an essential part of today’s mobile communication systems, as it enables mobility as well as real time and low latency communication. I have good knowledge of today’s most deployed global radio access technologies such as WCDMA (3G) and LTE (4G). The latter technology paved the path towards the massive MIMO vision of today.
Beside the access part I also have solid understanding of the core part of the mobile networks, which includes authentication, security, gateways, and data control/switching. More specifically, the focus in my daily work has been on interaction between devices and the network as well as interaction between the devices themselves through different network nodes. In addition to the above mentioned, I have on my own initiative developed different SW tools used for device configurations.
- P72547 FAM, (Dynamic estimation of MPDCCH CSS Type2 repetitions),
- P75343 WO1 Detection mechanism of UEs non-compliant with CRS blanking optimization
- P76033 US1 Antenna Port ID and Scrambling Code ID selection for TM8 Multi-User MIMO UEs.
My personal research is within the area of massive MIMO where we try to find new ways to assist mobility management, where integrated perception and learning requires certain degree of autonomy. At the same time relevant data needs to be analyzed and used as statistical input in for important configuration decisions in massive MIMO scenarios. There are many challenges ahead before massive MIMO is fully autonomous system in reality.
We aim for a machine learning approach for efficient operation of cellular networks based on massive MIMO. The many antennas in massive MIMO base stations give access to details in the radio channel nd opens up for better prediction of both small scale behaviour such as user correlation as well as large scale behaviour such as mobility patterns. This in turn can lead to new opportunities with respect to scheduling approaches and handover strategies in order to provide low latency reliable user connection in mixed and dynamic environments.