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Dr. rer. nat. Igor Bjelakovic

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Most of the papers listed below and some that are not submitted yet can be found at arXiv

Preprints

M. Frey, I. Bjelakovic and S. Stanczak (2020). Towards Secure Over-The-Air Computation. Submitted to Problems of Information Transmission. Preprint available at arXiv:2001.03174

I. Bjelakovic and R. Siegmund-Schultze (2015). Quantum Stein's lemma revisited, inequalities for quantum entropies, and a concavity theorem of Lieb. Preprint available at arXiv:quant-ph/0307170

Book Chapters

I. Bjelakovic and H. Boche and J. Sommerfeld (2013). Capacity Results for Arbitrarily Varying Wiretap Channels. In: Aydinian H., Cicalese F., Deppe C. (eds) Information Theory, Combinatorics, and Search Theory. Lecture Notes in Computer Science, vol 7777. Springer, Berlin, Heidelberg

I. Bjelakovic and H. Boche and G. Janen and J. Notzel (2013). Arbitrarily Varying and Compound Classical-Quantum Channels and a Note on Quantum Zero-Error Capacities. In: Aydinian H., Cicalese F., Deppe C. (eds) Information Theory, Combinatorics, and Search Theory. Lecture Notes in Computer Science, vol. 7777. Springer, Berlin, Heidelberg

Journal Publications

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M. Frey, I. Bjelakovic and S. Stanczak (2021). Over-The-Air Computation in Correlated Channels. IEEE Transactions on Signal Processing

R. Ahlswede and I. Bjelakovic and H. Boche and J. Nötzel (2013). Quantum Capacity under adversarial quantum noise: arbitrarily varying quantum channels. Communications in Mathematical Physics, Volume 317, Issue 1, pp 103–156, https://doi.org/10.1007/s00220-012-1613-x

I. Bjelakovic and H. Boche and J. Sommerfeld (2013). Secrecy results for compound wiretap channels. Problems of Information Transmission, Volume 49, Issue 1, pp 73–98, https://doi.org/10.1134/S0032946013010079

I. Bjelakovic and H. Boche and G. Janssen (2013). Universal quantum state merging. Journal of Mathematical Physics, Vol. 54, 032204, https://doi.org/10.1063/1.4795243

M. Wiese and H. Boche and I. Bjelakovic and V. Jungnickel (2011). The Compound Multiple Access Channel with Partially Cooperating Encoders. IEEE Trans. Inf. Theory, Vol. 57. No. 5, pp 3045-3066, Special Issue on Interference Networks

R.F. Wyrembelsky and I. Bjelakovic and T.J. Oechtering and H. Boche (2010). Capacity of Bidirectional Broadcast Channels under Channel Uncertainty. IEEE Trans. Commun. vol. 58, no. 10, pp 2984-2994

I. Bjelakovic and H. Boche (2009). Classical Capacities of Compound and Averaged Quantum Channels. IEEE Trans. Inf. Theory 55, No. 7, pp 3360-3374

I. Bjelakovic and H. Boche and J. Nötzel (2009). Entanglement transmission and generation under channel uncertainty: Universal quantum channel coding. Communications in Mathematical Physics, Volume 292, Issue 1, pp 55–97, https://doi.org/10.1007/s00220-009-0887-0

T. J. Oechtering and C. Schnurr and I. Bjelakovic and H. Boche (2008). Broadcast Capacity Region of Two-Phase Bidirectional Relaying. IEEE Trans. Inf. Theory, Vol. 54, No. 1, pp 454-458

I. Bjelakovic and H. Boche (2008). Ergodic Classical-Quantum Channels: Structure and Coding Theorems. IEEE Trans. Inf. Theory, Vol. 54, No. 2, pp 723-742

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Conference, Symposium, and Workshop Papers

Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
Citation key BjeAller2019
Author I. Bjelakovic and M. Frey and S. Stanczak
Year 2019
Journal 57th Annual Allerton Conference on Communication, Control, and Computing, 24-27 Sept. 2019 in Urbana, IL, USA, arXiv:1907.03777
Month Sept.
Editor IEEE
Abstract In this work, we consider the problem of distributed approximation of functions over multiple-access channels with additive noise. In contrast to previous works, we take fast fading into account and give explicit probability bounds for the approximation error allowing us to derive bounds on the number of channel uses that are needed to approximate a function up to a given approximation accuracy. Neither the fading nor the noise process is limited to Gaussian distributions. Instead, we consider sub-gaussian random variables which include Gaussian as well as many other distributions of practical relevance. The results are motivated by and have immediate applications to a) computing predictors in models for distributed machine learning and b) the max-consensus problem in ultra-dense networks.
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Contact

Dr. rer. nat. Igor Bjelakovic
Network and Information Theory NetIT - Faculty IV
Building HFT 400a
Einsteinufer 25
10587 Berlin
+49(0)30 314-28465