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Articles by Gerhard Wunder
Computer Science > Computation and Language
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[2510.15380] Bilinear Compressive Security
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
[2404.16853] Expectation Entropy as a Password Strength Metric
arXiv:2404.16853 (cs) View PDF Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2404.16853 [cs.CR] (or arXiv:2404.16853v1 [cs.CR] for this version) Related DOI: https://doi.org/10.1109/CNS56114.2022.9947259 Submission history From: Khan Reaz [ view email] [v1] Mon, 18 Mar 2024 15:03:37 UTC (244 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?)...
[2401.16414] A Causal Model for Quantifying Multipartite Classical and Quantum Correlations
arXiv:2401.16414 (cs) [Submitted on 29 Jan 2024 (v1), last revised 29 Aug 2024 (this version, v4)] View PDF HTML (experimental) Comments: Subjects: Information Theory (cs.IT); Quantum Physics (quant-ph) Cite as: arXiv:2401.16414 [cs.IT] (or arXiv:2401.16414v4 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2401.16414 Related DOI: https://doi.org/10.1109/ISIT57864.2024.10619265 Submission history From: Shuchan Wang [ view email] [v1] Mon, 29 Jan 2024 18:51:02 UTC (146 KB) [v2] Mon, 18...
[2401.14056] Model CBOR Serialization for Federated Learning
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Deep Learning for Channel Coding via Neural Mutual Information Estimation
End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks.
Performance of Hierarchical Sparse Detectors for Massive MTC
Contributors Gerhard Wunder - , Free University of Berlin (Author) Ingo Roth - , Free University of Berlin (Author) Rick Fritschek - , Free University of Berlin (Author) Jens Eisert - , Free University of Berlin (Author) Abstract Recently, a new class of so-called \emph{hierarchical thresholding algorithms} was introduced to optimally exploit the sparsity structure in joint user activity and channel detection problems.
Constant-gap sum-capacity approximation of the deterministic interfering multiple access channel
Recent investigations have shown that multi-user gain can be enabled in frequency-flat time-invariant single-antenna cellular networks, for example the interfering multiple access channel (IMAC). These investigations have shown gain beyond results through treating interference as noise (TIN) techniques in the weak interference regime, rendering TIN sub-optimal for these networks.
Deterministic IMAC revisited: Constant-gap capacity in the weak interference case
Recent investigations have shown that approximations of certain channels by the linear deterministic model (LDM) are sub-optimal in some cases. Replacing the LDM by a more sophisticated deterministic model possibly results in improvements of previous results. An example is use of the lower triangular deterministic model (LTDM) to replace the LDM. Due to the integrated dependence on the fine channel gains, results can be obtained which were not previously possible.
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