arXiv Machine Learning By Mikko Honkala, Dani Korpi, Elias Raninen, Janne M. J. Huttunen

EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver

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arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.

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arXiv AI
Aug 25

Deep Learning-Based Multi-User Communication Design for Dense IoT Networks: Interference-Aware Finite-Blocklength Communication and Preliminary MIMO Extensions

The paper presents a deep‑learning‑based end‑to‑end multi‑user communication design for dense IoT networks operating under interference‑limited, finite‑blocklength conditions. It extends a 2‑user SiameseNet transceiver to support 2, 4, and 8 users, using learned redundancy to suppress interference and improve noise robustness without joint detection. Preliminary results also show potential for a 2×2 MIMO configuration with fixed‑channel CSIT and CSIR.

By Arkadeep Sinha, Shubham Paul, R. Manivasakan