Picture of Daniel Plabst

M.Sc. Daniel Plabst

Technical University of Munich

Associate Professorship of Line Transmission Technology (Prof. Hanik)

Postal address

Theresienstr. 90
80333 München


I received my M.Sc. degree from the Technical University of Munich (TUM) in 2018.

In my Master's thesis I worked on signal processing for energy-efficient wireless communication systems at the Associate Professorship of Signal Processing (MSV), while staying with the Wireless Networking and Communications Group at the University of Texas at Austin (UT), USA.

Since 2019 I am a research assistant at the Institute for Communications Engineering in the group Line Transmission Technology of Prof. Dr.-Ing. Norbert Hanik.

Research Interests

Direct-Detection (DD) is a cost-effective approach for short-reach fiber-optic communication. A DD receiver uses a single photodiode to measure the intensity of the impinging signal. This suggests that DD is not compatible with phase modulation. However, using oversampling, the phase can be recovered by exploiting inter-symbol interference (ISI).

We investigate DD for short-reach communication in the context of information theory and estimation strategies.


Available Theses

Capacity upper Bounds for ISI Channels with Direct Detection


We are interested in computing upper bounds (on capacity) for frequency-selective channels with a memoryless nonlineary at the transmitter/receiver.

One application for these bounds are short-reach fiber-optic communication systems with a single photodiode at the receiver. The photodiode is a memoryless nonlinearity, as it produces an output that is proportional to the squared magnitude of the input signal.

A simple upper bound for the above model is given in [Sec. III D, 2].

D. Plabst et al., "Achievable Rates for Short-Reach Fiber-Optic Channels With Direct Detection," in Journal of Lightwave Technology, vol. 40, no. 12, pp. 3602-3613, 15 June15, 2022, doi: 10.1109/JLT.2022.3149574.




Information Theory

Linear System Theory


Capacity Lower Bounds for ISI Channels


Capacity and capacity-achieving distributions are available for some memoryless channels. For the average-power constrained memoryless channel with additive white Gaussian noise, Shannon provided a closed form description of capacity. Closed form solutions for capacity also exist for some simple discrete memoryless channels (DMCs). In addition, numerical algorithms for calculating the capacity (and capacity-achieving discrete distributions) of general DMCs are available through the works of Arimoto and Blahut.

However, often wireless or fiber-optic channels of practical interest contain intersymbol interference (ISI), and the memory due to ISI can no longer be neglected. When considering discrete channel inputs, one may be interested in determining the capacity and capacity-achieving distribution for these ISI channels. In fact, discrete stationary Markov processes asymptotically achieve the capacity of finite-state ISI channels as the order of the Markov process goes to infinity.

An algorithm [1] for optimizing discrete Markov processes of a certain order M is presented to maximize the information rate between channel input and output. As the order of the Markov process increases, tighter lower bounds on capacity are obtained.

[1] A. Kavcic, "On the capacity of Markov sources over noisy channels," GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270), 2001, pp. 2997-3001 vol.5, doi: 10.1109/GLOCOM.2001.965977.

Further reading (if required):

[2] P. O. Vontobel, A. Kavcic, D. M. Arnold and H. -A. Loeliger, "A Generalization of the Blahut–Arimoto Algorithm to Finite-State Channels," in IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 1887-1918, May 2008, doi: 10.1109/TIT.2008.920243.

[3] T. Lang, A. Mezghani and J. A. Nossek, "Channel-matched trellis codes for finite-state intersymbol-interference channels," 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2010, pp. 1-5, doi: 10.1109/SPAWC.2010.5670890.


Information Theory


Theses in Progress

Distributed Acoustic Sensing


Distributed Acoustic Sensing (DAS) employs advanced signal processing to enhance the performance for strain and vibrations sensing. However, the algorithms exhibit high complexity as well as inaccuracy due to nonlinear effects.

The task of the student is to replace this conventional signal processing blocks by a neural network (NN). An appropriate NN type/architecture needs to be selected. Finally, the performance of the NN has to be evaluated and compared to conventional algorithms.

[1] Karapanagiotis, Christos, Konstantin Hicke, and Katerina Krebber. "A collection of machine learning assisted distributed fiber optic sensors for infrastructure monitoring.", 2023.




Daniel Plabst - Maximilian Schädler ()



  • Plabst, D.; Prinz, T.; Wiegart, T.; Rahman, T.; Stojanovic, N.; Calabro, S.; Hanik, N.; Kramer, G.: Achievable Rates for Short-Reach Fiber-Optic Channels with Direct Detection. IEEE/OSA J. Lightw. Technol., 2022 more… Full text ( DOI )
  • Prinz, T.; Plabst, D.; Wiegart, T.; Calabro, S.; Hanik, N.; Kramer, G.: Successive interference cancellation for fiber-optic channels with direct detection. IEEE/OSA J. Lightw. Technol., 2022 more…
  • Prinz, T.; Plabst, D.; Wiegart, T.; Calabrò, S.; Hanik, N.; Kramer, G.: Successive Interference Cancellation for Fiber-Optic Channels with Direct Detection. Submitted to Journal of Lightwave Technology, 2022 more… Full text ( DOI )


  • Mezghani A., Plabst D., Swindlehurst L. A. , Fijalkow I., Nossek J. A.: Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO. IEEE Statistical Signal Processing Workshop 2021, 2021 more…
  • Prinz, T.; Wiegart, T.; Plabst, D.; Calabrò, S.; Böcherer, G.; Stojanovic, N.; Rahman, T.;: PAM-6 Coded Modulation for IM/DD Channels with a Peak-Power Constraint. International Symposium on Topics in Coding (ISTC) 2021, 2021 more…
  • Wiegart, T.; Prinz, T.; Plabst, D.: Recovering the Phase in DD Receivers with Oversampling: A Toy Example. Huawei Joint Lab Workshop, 2021 more…


  • Benedikt Leible, Daniel Plabst, Norbert Hanik: Back-to-Back Performance of the Full Spectrum Nonlinear Fourier Transform and Its Inverse. Entropy 22 (10), 2020, 1131 more… Full text ( DOI ) Full text (mediaTUM)
  • Benedikt Leible, Daniel Plabst, Norbert Hanik: Stability of the Full Spectrum Nonlinear FourierTransform. International Conference on Transparent Optical Networks (ICTON) 2020, 2020 more… Full text (mediaTUM)
  • Plabst, D.; Hanik, N.: Phase-Retrieval for Short-Reach IM/DD Links. Workshop der Informationstechnischen Gesellschaft (ITG) in Karlsruhe, 2020 more…
  • Plabst, Daniel; García-Gómez, Francisco Javier; Wiegart, Thomas; Hanik, Norbert: Wiener Filter for Short-Reach Fiber-Optic Links. IEEE Communications Letters 24 (11), 2020, 2546 - 2550 more… Full text ( DOI )


  • Plabst, D.; Jedda, H.; Mezghani A.: Linear Transmit Signal Processing in 1-Bit Quantized Massive MIMO Systems. 2019 Munich Doctoral Seminar on Communications, 2019 more… Full text (mediaTUM)


  • Munir, J.; Plabst, D.; Nossek, J.A.: Efficient Equalization Method for Cyclic Prefix-Free Coarsely Quantized Massive MIMO Systems. 2018 IEEE International Conference on Communications (ICC), 2018 more… Full text ( DOI )
  • Plabst, D.; Munir J.; Mezghani A.; Nossek, J.A.: Efficient Non-linear Equalization for 1-bit Quantized Cyclic Prefix-Free Massive MIMO Systems. 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 2018 more… Full text ( DOI )