4 papers · 1 filter
Deep Learning-Based Multi-User Communication Design for Dense IoT Networks: Interference-Aware Finite-Blocklength Communication and Preliminary MIMO Extensions
Arkadeep Sinha, Shubham Paul, R. Manivasakan
Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiver complexity. This work intro…
Selective Interference Suppression of Siamese-Net in Heterogeneous Interference Channels
Arkadeep Sinha, Shubham Paul, R. Manivasakan +2
We study an end-to-end learnt short-block codes for a -user real Gaussian interference channel with heterogeneous pairwise interference strengths, while keeping single-user deco…
Neural Equalisers for Highly Compressed Faster-than-Nyquist Signalling: Design, Performance, Complexity and Robustness
Shubham Paul, Sheetal Kalyani, Nambi Sheshadri +1
Faster-than-Nyquist (FTN) signalling has emerged as a compelling technique for enhancing spectral efficiency in bandwidth-constrained communication systems. By intentionally introd…
Learning Robust Representations for Communications over Interference-limited Channels
Shubham Paul, Sudharsan Senthil, Preethi Seshadri +2
In the context of cellular networks, users located at the periphery of cells are particularly vulnerable to substantial interference from neighbouring cells, which can be represent…