Communications Engineering
The research focus areas include:
Dr. Kalle Ruttik, Dr. Usman Sheik, Dr. Fayezeh Ghavimi, M.Sc. Estefanos Menta, M.Sc Behnam Bahidi, M.Sc. Hamidreza Shariatrmadari, M.Sc. Aleksi Marttinen
- 5G Ultra dense networks (UDNs)
Network densification is one of the key approaches to meet the 5G data rate targets in the enhanced Mobile Broadband (eMBB) scenario. In UDNs, the base stations are embedded in lampposts. If UDNs are used to serve vehicular traffic, the number of handovers is going to be large and unless the handover procedures is changed, the associated handover signaling will become a bottleneck. In Take-5 project, we are developing an UDN testbed system to test new mobility concept, in which the network follows the user based on uplink beacons and the transmission and selects the reception points (TRPs) in a transparent manner without the need for explicit signaling between the network and user equipment. This work we do in close collaboration with Huawei. - 5G Ulra reliable low latency communications (URRLC)
URRLC is targeted for mission critical communication applications that have tight requirements in terms of link reliability and latency. To meet the set reliability target of >99.999%, not just the communication link need to be reliable but also all the control signaling associated with the communication need to be reliable. We have been studying the impact of control channel reliability on URLLC. We are subcontracted by Nokia to perform this research. The work belongs to WIVE project. - Capillary networks for machine type communications
In capillary networks, a short range wireless (sensor) network is connected to Internet through cellular connectivity. An example application is tracking of people, material and tools in construction sites based on Bluetooth beacons. This we have implemented in iCONS project. - Cloudification of Narrowband IoT base station
We have implemented NB-IoT base station using software defined radio using C++. The base station does all base band processing in a standard Linux computer or container.
Y. D. Beyene et al., "NB-IoT Technology Overview and Experience from Cloud-RAN Implementation," in IEEE Wireless Communications, vol. 24, no. 3, pp. 26-32, June 2017.
Dr. Ruifeng Duan, M.Sc Behnam Bahidi, M.Sc M.Sc Xiyu Wangm, M.Sc. Hany Elemy
- Ambient backscatter communications (AmBC)
In AmBC, a device communicates by modulating and backscattering the ambient signal imping at its antenna. In our research, we have focused on developing methods to jointly decode the ambient signal transmitted by a legacy system and the backscattered signal at the receiver. This work is done in collaboration with University of Houston (Prof. Zhu Han and Pan Miao) in Academy of Finland – NSF WiFIUS research program project AIMHIS. Our analysis suggest that in a fading channel, the presence of AmBC nodes increase the total achievable rate in the system. This excess capacity can be shared between the AmBC and legacy systems.
R. Duan, R. Jäntti, H. Yiğitler and K. Ruttik, "On the Achievable Rate of Bistatic Modulated Rescatter Systems," in IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 9609-9613, Oct. 2017. - Quantum backscatter communications (QBC)
Quantum illumination (QI) is a revolutionary photonic quantum sensing paradigm that enhances the sensitivity of photodetection in noisy and lossy environments. The scheme is not limited to any particular frequency and has been demonstrated in microwave domain as well. Just like the backscatter communications bears close resemblance with radar, the quantum backscatter communciations scheme proposed by us is similar to the quantum radar. Our research work aims at investigating the possibilities of using QI to enhance the backscatter communications in extremely low signal-to-noise ratio conditions. Our first paper on the topic is
R. Jäntti, R. Di Candia, R. Duan and K. Ruttik, “Multiantenna Quantum Backscatter Communications,” In Prof. IEEE Globecom 2017, Singapore, December 2017.
Dr. Ossi Kaltiokallio, Dr. Hüseyin Yiğitler
- Device free localization (DFL)
In recent years, Received Signal Strength based DFL has emerged as an attractive technology for passive indoor localization and a comprehensive number of works have proven its accuracy in indoor environments. However, multipath propagation and improper modeling of the indoor propagation channel degrade the performance of these works significantly. The objective of our research is to accurately model human-induced changes in the propagation channel and to mitigate the undesirable effects of multipath propagation. The research also focuses on developing algorithms to enable real world DFL deployments. Through these contributions, the research enables improvements in localization accuracy, cost efficiency and usability of DFL. This work is done in Academy of Finland funded “RF Inference” project. - Respiration rate monitoring
Besides DFL, we have applied the improved models to develop novel estimation methods for human respiration rate estimation based on received signal strength measurements.
Cloud RAN for Indoor DAS:
LTE network controlled D2D for mission critical MTC:
Low-power Remote Intrusion Monitoring Using Radio Tomographic Imaging:
System-level simulator of Bluetooth Low Energy version 5
This simulator is written in C++ and developed to work in MiXiM framework of the OMNeT++ engine. This simulation, which simulates the PHY and MAC layer of BLE 5, is developed on top of BLE 4.2 simulator. This simulator has been used for publishing the following article:
- B. Badihi, F. Ghavimi and R. Jäntti, "On the System-level Performance Evaluation of Bluetooth 5 in IoT: Open Office Case Study," 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, 2019, pp. 485-489, doi: 10.1109/ISWCS.2019.8877223.
The following features of BLE5 have been developed:
- New PHY layers including, LE Coded S2, LE Coded S8, LE 2M PHY in addition to LE 1M PHY.
- Channel Selection Algorithm 2#.
In addition to the above-mentioned features, a dedicated decider for BLE in PHY layer, flow control in MAC layer and new auxiliary advertising packets are developed. It is worth mentioning that this simulator is developed and tested with OMNeT++ version 4.4.1 and MiXiM version 2.3. The higher versions of OMNeT++ may or may not work with the tool.
In order to use this tool, the user must accept the GNU v3 license by confirming that she/he has read, understood and agree with the conditions of this licence. In order to use this tool, the user need the password for openning the zip file. The password is "I hereby accept the license terms" without all spaces. After unzipping the file, the is a tarball that can be directly imported by OMNet++.