5 papers
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…
Learning Robust Representations for Communications over Noisy Channels
Sudharsan Senthil, Shubham Paul, Nambi Seshadri +1
We explore the use of FCNNs (Fully Connected Neural Networks) for designing end-to-end communication systems without taking any inspiration from existing classical communications m…