Persönlicher Status und Werkzeuge

Dr.-Ing. Georg Böcherer

Georg Böcherer

Dr.-Ing. Georg Böcherer
Senior researcher, lecturer

Institute for Communications Engineering
Theresienstrasse 90
Building N4, 3. Floor
D-80333 Munich

Room: N3404
Phone: +49 89 289-23458


Georg Böcherer obtained his MSc degree in Electrical Engineering and Information Technology from the ETH Zürich. He spent one semester at the EPF Lausanne and wrote his master thesis at the Federal University of Pernambuco in Recife, Brazil. From 2007 to 2012 he worked towards his PhD degree at the Institute of Theoretical Information Technology at RWTH Aachen University. Since April 2012, he is with the Institute for Communications Engineering at the Technical University of Munich. His PhD thesis on probabilistic shaping received the E-Plus Dissertation Award. His work on coding for the ICT Cubes received the best paper award at ISWCS 2011. His Probabilistic Amplitude Shaping (PAS) scheme for capacity achieving and rate adaptive communication won a Bell Labs Prize in 2015. He was a co-organizer of the Munich Workshop on Coding and Modulation (MCM 2015).



Teaching Assistant

Available Theses

Bachelor Thesis - LDPC Decoder implementation on FPGA
Feasible decoding complexity is a major criteria for any decoding algorithm. LDPC Codes have linear decoding complexity, which makes them interesting for various applications. The aim of this bachelor thesis is to implement one specific LDPC Code on hardware starting from a software implementation.
Week 1: get familiar with LDPC Codes
Week 2: implement LDPC decoder in software
Week 3-8: implement LDPC decoder on FPGA
Week 8-9: write your thesis

Requirements: some experience with FPGAs
Supervisors: Peihong Yuan, Patrick Schulte, Georg Böcherer

Research Interests

Coded Modulation with Probabilistic Shaping

In Shannon theory, the key step in calculating capacity of a communication channel is to determine the capacity-achieving input distribution. In practical systems however, almost exclusively uniform input distributions are employed. This results in the so called shaping gap, i.e., a reduction of the maximum theoretically achievable rate for reliable communication. The goal is to develop practical tools that provably allow to overcome the shaping gap. To this end, the generation of capacity-achieving distributions, forward error correction and modulation are jointly considered.

Simulation of Random Processes

The generation of random numbers with a distribution that matches (resembles) a given target distribution is a core problem relevant to many communication scenarios such as data compression, constrained coding, coded modulation, and information theoretic security. The appropriate measure of resemblance from an information theoretic point of view is the informational divergence between the generated distribution and the target distribution. I am interested in theoretic limits, algorithms, and applications.