Interview Prof. Stanczak "Nachgefragt zu Open RAN", 25.1.2022 
Tuesday, 25. January 2022
- © https://www.basecamp.digital/event/nachgefrag
Thema: Nachgefragt! #6 Ausgabe von „Nachgefragt!“ Open RAN – was steckt hinter der neuen Mobilfunktechnologie?
Prof. Slawomir Stanczak, Head of Wireless Communications and Networks Department beim Fraunhofer Heinrich-Hertz-Institut und Professor für Netzwerkinformationstheorie an der TU Berlin
Marina Grigorian, Repräsentantin Berlin, Telefónica Deutschland (Moderation)
Datum/ Uhrzeit: 25. Januar 2022, 8:30 Uhr – 9:00 Uhr
Adresse: BASECAMP, Mittelstraße 51 -53, 10117 Berlin
Ansprechpartner: Marina Grigorian
Best Workshop Paper Award in IEEE 5GWF’21
- IEEE 5GWF’21 Best Workshop Paper Award Certificate
- © IEEE 5GWF’21
Metin Vural successfully completed his PhD on December 14, 2021.
Tuesday, 14. December 2021
- Prof. Dr.- Ing. Slawomir Stanczak & Dr.- Ing. Metin Vural
- © privat
Scientific defense of Mr. Metin Vural on Tuesday, December 14, 2021 at 5:00 p.m. (CET)
to obtain the academic degree of „Doktor der Ingenieurwissenschaften“ (Dr.-Ing.) .
The title of the doctoral thesis is: „Efficient Pareto Frontier Algorithms for Computing Structured Signal Representations“
The doctoral examination board consists of the following members:
Chair: Prof. Dr.-Ing. Jörg Raisch
Evaluators: Prof. Dr.-Ing. Slawomir Stanczak
Prof. Dr.-Ing. Aydin Sezgin
Prof. Dr. Dr. Aleksandr Aravkin
ℓp-norm minimization plays a significant role in a variety of disciplines. It is not only important for the signal recovery in compressed sensing but also beneficial for finding meaningful signal representations as for the sparse and anti-sparse coding related applications. Therefore, minimizing ℓp-norms in an efficient manner sparked interest in a variety of works. This thesis is concerned with the noise-constrained ℓp-norm minimization for 1 ≤ p ≤ ∞. Although there are various optimization problem formulations that may be used to minimize an ℓp-norm, constraining the noise can offer a more meaningful optimization problem definition since when there is a known noise tolerance in an application, one can simply canalise it into the optimization problem and formulate exactly what to solve. Thus, it is often easier to set the noise tolerance from the optimization perspective. Despite this, there is a lack of computationally efficient algorithms in the literature for the noise-constrained ℓp-norm minimization problem because its feasible area can be complicated. Different optimization problem formulations can provide equivalent solutions and some of them might be easier to solve than the others. Therefore, it might be tempting to solve a computationally efficient problem in order to have the solution to another one. In this thesis, we solved constrained ℓp-norm regularization to reach the solution of the noise-constrained ℓp-norm problem. We introduce optimality tracing based ℓp-norm minimization approaches with simple root finding iterations for 1 ≤ p ≤ ∞. The optimality trade-off between both objectives, the ℓp-norm and a loss function that measures the data misfit, is formulated as a nonlinear equation root finding problem. We present and employ several simple, derivative-free and cost-efficient nonlinear equation root finding methods to trace this optimality over a Pareto frontier. Some of these root finding methods do not require differentiable loss functions and are applicable for both convex and nonconvex data misfits and extend such problems to a broader class of applications. We also introduce a warm-start strategy of taking linear least-squares solution with the one that has minimum ℓ2-norm which is named method of frames (MOF) as an input to require less iterations. This warm-start may provide flexible and meaningful starting point initialization for many applications where MOF already exists and can be improved with a better understanding of finitedimensional geometry, e.g. n-widths. The impact of the overcomplete matrix on the convergence rate of some of the presented approaches is demonstrated for matrices fulfilling the Uniform Uncertainty Principle and Uncertainty Principle. These properties were formerly introduced o analyze the performance of random matrices for ℓ1 and ℓ∞-norm related applications espectively. In the last part of the thesis, i.e. in Chapter 7, ℓp-norm minimization related applications are probed with using several loss functions such as least-squares, Huber and a nonconvex penalty Student’s t. ℓ1-norm is minimized with a typical compressed sensing example. Also, a generic test benchmark is utilized for the comparison of the nonlinear quation root finders for ℓ1-norm minimization. A new communication scheme is introduced by minimizing ℓ∞-norm. Outlier detection problem is studied with the minimized ℓ∞-norm, and a prior is offered for the minimized ℓ∞-norm with its performance on peak-to-average power ratio (PAPR). Noise-constrained nuclear norm is minimized as well for the Euclidean distance matrix completion problem with the application of wireless sensor network localization.
Accepted paper for presentation at the IEEE ICC'21 Workshop WS-15: 'Pulse-Shaped OTFS over Doubly-Dispersive Channels: One-Tap vs. Full LMMSE Equalizers'
Wednesday, 24. March 2021
are glad to announce that the paper 'Pulse-Shaped OTFS over
Doubly-Dispersive Channels: One-Tap vs. Full LMMSE Equalizers' has
been accepted for publication and presentation at the IEEE
International Conference on Communications (IEEE ICC'21). The paper
will presented at the WS-15: 1st Workshop on
Orthogonal Time Frequency Space Modulation (OTFS) for 6G and Future
Authors: Andreas Pfadler, Peter Jung, Tom
Szollmann and Slawomir Stanczak
Title: Pulse-Shaped OTFS over Doubly-Dispersive Channels: One-Tap vs. Full LMMSE Equalizers
Abstract: Orthogonal time frequency and space (OTFS) is a modulation technique combining Gabor structure with additional time-frequency spreading. It promises significant improvements of the wireless transmission in terms of robustness and efficiency for high mobility users. However, it requires sufficiently accurate channel state information (CSI) and an appropriate equalizer. In particular, full CSI is often assumed to mitigate self-interference. This is particularly challenging in highly dynamic vehicular scenarios where the channel is truly doubly-dispersive. Self-interference is caused by the channel cross-talk coefficients coming from pulse and grid mismatch of the OTFS system with the channel scattering function. The estimation of the channel cross-term coefficients is a tedious task which is not always feasible.
Focusing on practically feasible channel main diagonal estimation and equalization techniques, we propose a tuned one-tap minimum mean square error equalizer (MMSE-EQ). We consider an additional variance parameter including the power of the channel estimation error and self-interference. We determine it by minimizing an error metric between the transmitted and received pilot and guard symbols by using gradient decent with reasonable initial guess. In addition, we numerically compare the proposed one-tap MMSE-EQ with full linear MMSE-EQ with ideal CSI, and orthogonal frequency-division multiplexing (OFDM) in terms of uncoded performance. Our results indicate that, with our proposed equalizer, OTFS significantly outperforms OFDM with low-complexity one-tap equalization over doubly-dispersive channels.
WS-15: 1st Workshop on Orthogonal Time Frequency Space Modulation (OTFS) for 6G and Future High-mobility Communications 
When: Friday, 18 June 2021 (virtual conference)
Prof. Dr.-Ing. has been invited to serve as TPC member at the ITW 2021 in Kanazawa, Japan // Oct 17-21, 2021 
Monday, 22. March 2021
Prof. Dr.-Ing. Slawomir Stanczak has been invited to serve as Technical Program Committee member at the ITW 2021, which he gladly accepted. The 2021 IEEE Information Theory Workshop (ITW2021) will be held in October 17-21 at Kanazawa Bunka Hall, Kanazawa, Japan.
Detailed information regarding the conference and paper submission deadlines can be found here .
Accepted manuscript for publication in the IEEE Signal Processing Letters: 'Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks'
Wednesday, 03. March 2021
We are glad to announce that the manuscript 'Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks' has been accepted for publication in the IEEE Signal Processing Letters.
Authors: Johannes Dommel, Zoran Utkovski, Osvaldo
Simeone and Slawomir Stanczak
Title: Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks
Abstract: A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in the uplink using grant-free random access to multiple edge nodes (ENs). Each EN is connected to a central processor (CP) via a finite-capacity fronthaul link. In contrast to conventional information-agnostic protocols based on separate source-channel (SSC) coding, where each device uses a separate codebook, this paper considers an information-centric approach based on joint source-channel (JSC) coding via a non-orthogonal generalization of type-based multiple access (TBMA). By leveraging the semantics of the observed signals, all sensors measuring the same event share the same codebook (with non-orthogonal codewords), and all such sensors making the same local estimate of the event transmit the same codeword. The F-RAN architecture directly detects the events values without first performing individual decoding for each device. Cloud and edge detection schemes based on Bayesian message passing are designed and trade-offs between cloud and edge processing are assessed.
New joint project TRURL between TU Berlin NetIT, NCO Torun, TITECH and Keio University starting in June 2021 
Monday, 01. March 2021
Distributed learning has been identified as a crucial enabler for future AI tools in heterogeneous environments. However, current work on distributed AI is based on standard optimization tools that, for example, consider the research on distributed algorithms as a separate topic from wireless communication. As a consequence, the resulting systems scale unfavorably with the number of communication partners involved and have major deficits in terms of security, privacy and robustness against partial failures in the wireless network.
Therefore we initiated a new joint project entitled "TRUstworthy distRibuted Learning" (TRURL) in cooperation with the Nicolaus Copernicus University (NCO) in Toruń, the Keio University and the Tokyo Institute of Technology (TITECH), which will start in June 2021.
The main objective of this project is to devise AI algorithms that:
(1) are truly nonhierarchical (no single point of failure) and asynchronous;
(2) provide confidence intervals for the estimates;
(3) detect attacks and eavesdroppers in the network;
(4) are privacy preserving;
(5) solve real-world problems (and, in particular, problems related to environmental modelling and network cyber security); and
(6) use practical communication protocols tailored to the optimization methods being proposed.
The research work will be divided among the partners as follows: TU Berlin will develop methods of Over-the-Air computation and Federated Learning schemes making use of them as well as defenses against eavesdroppers. NCO Torun will investigate how privacy can be preserved when carrying out these schemes. TITECH will focus on defenses agains active attacks with a focus on DDoS (Distributed Denial of Service). Keio University will work on robustness against partial communication failures and reliability of the proposed schemes.
Concert Japan EU (TRURL): http://www.concert-japan.eu/ 
New project "Computation for Communication: Bridging the Digital and Analog Worlds" between TU Berlin and University of Melbourne 
Monday, 01. March 2021
Within the project "Computation for Communication: Bridging the Digital and Analog Worlds", researchers from Technische Universität Berlin (TUB) and University of Melbourne (UoM) will work on a fundamental problem that comes with the increasing ubiquity of wireless communications: While the available bandwidth remains limited, the number of devices that share it is ever increasing.
The TUB researchers have focused mostly on analog approaches to make communication in massively-sized wireless networks hugely more efficient by tailoring the communication schemes to the applications they are used for, while the UoM researchers have approached the problem with digitally coded communication schemes. The support of the BUA-UoM Seed Fund means that the two groups have an opportunity to combine their complimentary expertise to jointly develop unifying approaches that can combine the advantages of the digital and analog worlds as well as foster a longer-term academic cooperation.
BUA/UoM Partnership: https://www.berlin-university-alliance.de/en/commitments/international/melbourne/index.html 
Upcoming talk with Prof. Dr.-Ing. Slawomir Stanczak at NVIDIA GTC'21: Key challenges and technology drivers for open 5G/6G networks // April 12-16, 2021 
Sunday, 28. February 2021
We are glad to announce that Prof. Dr.-Ing. Slawomir Stanczak will give a talk at the NVIDIA GTC 2021 (April 12 -16).
Title: Key challenges and technology drivers for open 5G/6G networks
NVIDIA GTC 2021  will take place online April 12 -16. Registration will be free. We encourage you to invite your colleagues to register and attend.
Invited talk of Prof. Dr.-Ing. Slawomir Stanczak at 5G Masters: OpenRAN, technische Evolution oder Revolution? // February 26, 2021
Wednesday, 24. February 2021
Prof. Dr.-Ing. Slawomir Stanczak has been invited to give a talk on the topic "OpenRAN, technische Evolution oder Revolution?". The event took place on the 26th February 2021 and was supported by Huawei and GvW Graf von Westphalen.
Visit the event website for more details:
Speaker-info: https://5gmasters.de/team-member/prof-dr-ing-habil-slawomir-stanczak/ 
Accepted paper for presentation at IEEE VTC2021-Spring: 'AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios' 
Wednesday, 03. February 2021
The paper 'AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios' has been accepted for presentation and publication at the IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). The conference will be held virtually from 25-28 April 2021.
Authors: Daniel Fabian Külzer, Martin Kasparick, Alexandros Palaios, Raja Sattiraju, Oscar Dario Ramos-Cantor, Dennis Wieruch, Hugues Tchouankem, Fabian Göttsch, Philipp Geuer, Jens Schwardmann, Gerhard Fettweis, Hans Dieter Schotten and Slawomir Stanczak
Title: AI4Mobile: Use Cases and Challenges of AI-based QoS Prediction for High-Mobility Scenarios
Abstract: The integration of functions into future
communication systems that predict crucial Quality of Service (QoS)
parameters is expected to enable many new or enhanced use cases, for
example, in vehicular networks and Industry 4.0. Especially with high
user mobility, QoS prediction is required in an End-to-End (E2E)
fashion to guarantee uninterrupted connectivity and provisioning of
real-time applications. In this paper, we present a concise list of
mobility use cases, both from automotive and industrial production
domains, that benefit from Artificial Intelligence-based QoS
prediction. These applications are investigated in the publicly-funded
research project AI4Mobile by a representative consortium of industry
and academia. Based on a literature review, we identify the main
challenges in realizing predictive QoS at
high mobility, and we propose research directions to enable the envisioned E2E solutions.
Conference: IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), in Helsinki, Finland, 25-28 April 2021 (virtual conference)
Website: https://events.vtsociety.org/vtc2021-spring/ 
Accepted paper for presentation at IEEE VTC2021-Spring: 'Terminal-Side Data Rate Prediction For High-Mobility Users' 
Monday, 01. February 2021
The paper 'Terminal-Side Data Rate Prediction For High-Mobility Users' has been accepted for presentation and publication at the IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). The conference will be held virtually in April 2021.
Authors: Daniel Schäufele, Martin Kasparick, Jens Schwardmann, Johannes Morgenroth, and Slawomir Stanczak
Title: Terminal-Side Data Rate Prediction For High-Mobility Users
Conference: IEEE 93rd Vehicular Technology
Conference (VTC2021-Spring), Helsinki, Finland,
April 2021 (virtual conference)
Website: https://events.vtsociety.org/vtc2021-spring/ 
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