Digitale Kommunikationssysteme

Vorlesungen: Internet of Things

The goal of the cour­se is to pro­vi­de a sur­vey of the sta­te-of-the-art on aspects of the Internet of Things. What is re­al­ly the best way to ope­ra­te com­mu­ni­ca­ti­on net­works? What are the fun­da­men­tal li­mits to com­mu­ni­ca­te over IoT net­works? Which is the op­ti­mal stra­te­gy? Is se­cu­re or con­fi­den­ti­al com­mu­ni­ca­ti­on pos­si­ble? What fea­tures will fu­ture IoT net­works have? Those are the ques­ti­ons ad­dres­sed in this cour­se, which pro­vi­des stra­te­gic gui­dance and gui­de­lines for the de­sign of IoT net­works. The following aspects will be introduced throughout the course:

  • Network Coding: Network coding is a modern communications technique that can vastly reduce the duration of transmission. This is done by combining packets at intermediate nodes in a multi-user communication scenario. This combination is done in a certain way that allows all users to recover their packets. Both physical-layer and non-physical-layer network coding will be discussed.
  • Full-duplex Communications: In order to handle the increasing demand for higher data rates in wireless communication systems, there is rising interest in transmitting and receiving at the same time and frequency, therefore reusing resources. The fundamental concepts and of full-duplex transceiver designs are introduced. Furthermore, recent advances of practical implementations are discussed as well as still existing challenges.
  • Caching: Caching is the technique to reduce the peak traffic rates by pre-fetching the popular contents into end-users memoires. The goal of this module is to introduce the state-of-the-art tools used in the literature and to highlight the benefits offered by cooperative caching even though the users memories may not be connected physically.
  • Delay-limited Communications: In many applications, it is essential that communication takes place within a finite duration in order to ensure low-latency. In this module concepts such das delay-limited capacity and coding for finite block length are introduced and discussed.
  • Side-information in Communications: In the big data era, information is omnipresent, thus available at either or both sides of the communication. How to take this prior knowledge as an advantage to achieve a more robost, efficient and secure communication, is of great interest. In this module, the basic models and coding techniques will be introduced, as well as their real-life applications in watermarking (e.g. copyright protection) and data compression (e.g. 3D video coding).
  • Physical Layer Security: In the conventional network, secrecy issues are dealt mainly in the higher layers of protocols stacks by cryptographic means. Physical layer security offers an alternate approach by utilizing the randomness of physical layer to hide information from adversaries. In this module the goal is to give an overview of basic models, for instance, wiretap channels and connections to practical system design.
  • Channel Coding: Channel coding is a technique used for controlling errors in data transmission over noisy communication channels. In this module, the fundamental issue how the probability of error drops with respect to the code rate/length, as well the lastest development of the capacity-approaching codes, principally turbo codes and LDPC codes, will be addressed.

The cour­se is re­le­van­t to gra­dua­te stu­dents in­te­rested in com­mu­ni­ca­ti­on and si­gnal pro­ces­sing. The pre­sen­ta­ti­on style of the lec­tu­res is ra­ther in­for­mal, fa­vo­r­ing broad in­tui­ti­on over ma­the­ma­ti­cal rigor. The tech­ni­cal (and finer) de­tails have to be in­ves­ti­ga­ted by the stu­dents wi­t­hin the pro­ject part of the cour­se. The cour­se is taught from a si­gnal pro­ces­sing and com­mu­ni­ca­ti­on theo­ry per­spec­tive with oc­ca­sio­nal de­via­ti­on to in­for­ma­ti­on theo­ry. Fun­da­men­tals as well as se­ver­al ad­van­ced to­pics such as

  • con­vex op­ti­miza­t­i­on and al­go­rith­ms,
  • in­for­ma­ti­on theo­re­tic pri­va­cy and se­cu­ri­ty,
  • net­work in­for­ma­ti­on theo­ry,
  • wire­less com­mu­ni­ca­ti­on theo­ry

are co­ver­ed, de­pen­ding on the in­te­rests and back­ground of the stu­dents. All the above men­tio­ned to­pics are of high im­port­an­ce for cur­rent net­works and also use­ful in de­ve­lo­ping new tech­ni­ques and al­go­rith­ms for fu­ture IoT net­works.

Re­com­men­ded know­ledge

  • Ma­the­ma­tics I-IV
  • Com­mu­ni­ca­ti­ons En­gi­nee­ring
  • Si­gnals and Sys­tems
  • Pro­ba­bi­li­ty theo­ry
  • In­for­ma­ti­on theo­ry
  • Wire­less Com­mu­ni­ca­ti­on


  1. Boyd, S., Van­den­berg­he, L. "Con­vex Op­ti­miza­t­i­on", Cam­bridge Uni­ver­si­ty Press, 2004
  2. Cover, T., Tho­mas, J. "Ele­ments of In­for­ma­ti­on Theo­ry", Wiley & Sons, 2006
  3. El-Ga­mal, A., Kim, Y.-H. "Net­work In­for­ma­ti­on Theo­ry", Cam­bridge Uni­ver­si­ty Press, 2011
  4. Bert­se­kas, Di­mit­ri P. "Non­line­ar Pro­gramming", Athena Sci­en­ti­fic, 1999

Weitere Informationen zu dieser Vorlesung finden Sie unter folgendem Link:
Fakultät für Elektrotechnik und Informationstechnik » Studium » Lehrveranstaltungen » Theo­re­ti­cal In­for­ma­ti­on Tech­no­lo­gy