activity
20242026
collaborators

17 papers

cs.IT2026

Distribution of -th Maximum Order Statistics of Independent, & Non-Identical SNR random variables in fading and its applications in

Srinivas Sagar, Athira Subhash, Sheetal Kalyani

This paper employs extreme value theory to establish the asymptotic distribution of the -th maximum order statistics of signal to noise ratio (SNR) for a fading channel…

cs.CL2026

Breaking the Script Barrier: Enabling Automatic Alignment for PoS-based ASR Error Analysis in Non-Latin Scripts

Prasenjit K Mudi, Dahlia Devapriya, Sheetal Kalyani

Automatic Speech Recognition (ASR) systems are commonly evaluated using aggregate metrics such as Word Error Rate (WER), which do not capture the linguistic structure of errors. Fi…

cs.LG2026

BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences

Nitin Priyadarshini Shankar, Vaibhav Singh, Sheetal Kalyani +1

Traditional cellular traffic forecasting models are optimized for minimizing symmetric errors, leaving them indifferent to shifting operational priorities. To bridge this gap, we i…

cs.IT2026

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…

quant-ph2026

Maximizing Qubit Throughput under Buffer Decoherence and Variability in Generation

Padma Priyanka, Avhishek Chatterjee, Sheetal Kalyani

Quantum communication networks require transmission of high-fidelity, uncoded qubits for applications such as entanglement distribution and quantum key distribution. However, curre…

cs.LG2026

Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference

Nitin Priyadarshini Shankar, Soham Lahiri, Sheetal Kalyani +1

Federated Learning (FL) preserves privacy by distributing training across devices. However, using DNNs is computationally intensive at the low-powered edge during inference. Edge d…