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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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KMHCR: A Key-Controlled Signal-Domain Transformation for 5G IoT Security

Journal of Signal Processing Systems. 2026. Vol. 98. Article 31.
Ronglin Z., Wei L., Jiahong C., Yuxiang C., Zulong D., Zhongming F., Jigang W., Naixue X., Xiaoyan C., Avdoshin S. M.

To address the need for lightweight and low-latency protection in massive resource-constrained 5G Internet of Things (IoT) systems, this paper proposes Key-Controlled Modulation Hopping and Constellation Rotation (KMHCR). KMHCR is designed as a physical-layer confidentiality-enhancement mechanism that avoids bit-wise full-payload encryption in the protection pipeline. It uses a shared key derived from channel-reciprocity secret key generation to drive a stateless, counter-based pseudo-random function, which determines a 5G-compliant modulation scheme and a quantized constellation rotation angle for each packet without requiring fragile per-packet state synchronization. For legitimate users, these operations introduce only lightweight signal-domain processing, whereas an unauthorized receiver must jointly infer the modulation type and the phase rotation from the observed signal. To evaluate the robustness of the proposed mechanism against intelligent attacks, we construct a 3GPP-compliant 5G adversarial dataset covering six modulation types, multiple signal-to-noise ratio regimes, and realistic channel impairments. Experimental results with multiple state-of-the-art deep learning-based AMC models show that KMHCR can substantially reduce the recoverable throughput of unauthorized receivers under the considered adversarial setting. These results support the use of KMHCR as a lightweight complementary protection mechanism for 5G IoT communications.

Research target: Computer Science
Language: English
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Keywords: Deep learningPhysical layer security5G IoTAutomatic modulation classificationConstellation rotation5G adversarial dataset
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