Department of Information Theory and Communication Systems
The Department lead by Prof. Eduard Jorswieck studies novel methods and tools from applied information theory, which are applied to analyse, optimize and design modern communications systems.
The research interests comprise essentially the following four areas: PhySec - physical layer security, CelCom - cellular communications, WiFi - modern wireless local area networks, WBAN - wireless body area networks. The research concentrates on methodological and mathematical core topics (network information theory, multi criteria programming, game theory, information-theoretic security, multi-channel signal processing and machine learning) and their applications and implementations in timely and relevant communications techniques and systems (IoT, Industrie 4.0, Cyber Physical Systems, 5G and beyond).
PhySec: The realization of the vision of IoT and Industrie 4.0 (and following), in which multiple heterogeneous devices, actors, sensors, communicate reliably and securely, requires a novel security architecture, which scales with the number of devices and which does not require infrastructure. Physical hardware parameters and channel properties allow the development of novel information-theoretic secure primitives. The group studies the secure transmission over unreliable and unknown wireless channels, over optical multi-mode fiber links with wiretappers and over channels with states and active attackers.
CelCom: The are comprises mobile communications systemes of the fourth (LTE/A) and fifth (5G, NR) as well as future generations. The analysis and design of novel transmissions schemes (PHY+MAC) contains channel coding and decoding, signal processing at tranmitters, receivers (or relays), as well as novel algorithms for resource allocation, scheduling and multiple access. In particular the flexibility by Software-Definied Networks enables an efficient resource allocation and robust and resilient communications. We develop novel algorithms for resource allocation and transmit strategy optimization in time varying dynamic complex interference networks.
WiFi: In the future there will emerge more dense private wireless local area networks, which operate interference limited and without coordination (compared to managed WiFi) and which congest the unlicensed spectrum. Additionally, data rates are increasing by the pure number of wireless devices and by data off-loading from macro cellular systems. Therefore, we need novel approaches and techniques for distributed interference management. We apply approaches from machine learning to optimize the wireless resources. Novel techniques for channel coding and decoding are found via atuo-encoding. WiFi networks (IEEE 802.11) are modelled, analyzed, optimized, efficiently designed and deployed, and their co-existence with other, e.g., cellular networks, studied.
WBAN: In medial applications, sensors and actors are placed on the human body in order to efficiently measure physical quantities and to report them to a wireless fusion center. Based on IEEE 802.15 standards, we develop novel techniques for distributed signal processing and coding, for handling the big data with low latencies and low energy consumption and sending them to central nodes. We consider signal processing and communications for wireless body area networks as well as applications of machine learning for reconstruction and removal or disturbing artefacts.
Updated: Wednesday, 23 October 2019