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20242026
most citedAn Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel

1 citations · 1 across the 9 of their papers we have counts for

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9 papers

eess.SP2026

Budget-Aware Federated Dual-Side Channel Estimation for Hybrid mmWave Massive MIMO

Jiawei Chen, Ruining Fan, Mouli Chakraborty +2

This work studies communication-constrained federated dual-side channel state information (CSI) estimation in hybrid millimeter-wave (mmWave) massive multiple input multiple output…

eess.SP2026

Secure and Robust Beamforming Design for STAR-RIS-aided MU-MIMO ISAC Systems

Rakesh Ranjan, Anshu Mukherjee, Manjesh K. Hanawal +2

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) offer a transformative approach for integrated sensing and communication (ISAC) systems, par…

eess.SP2026

Comparative Analysis of Differential and Collision Entropy for Finite-Regime QKD in Hybrid Quantum Noisy Channels

Mouli Chakraborty, Subhash Chandra, Avishek Nag +3

In this work, a comparative study between three fundamental entropic measures, differential entropy, quantum Renyi entropy, and quantum collision entropy for a hybrid quantum chann…

eess.SP2026

Grover's Search-Inspired Quantum Reinforcement Learning for Massive MIMO User Scheduling

Ruining Fan, Xingyu Huang, Mouli Chakraborty +2

The efficient user scheduling policy in the massive Multiple Input Multiple Output (mMIMO) system remains a significant challenge in the field of 5G and Beyond 5G (B5G) due to its…

eess.SP2025

Quantum Deep Learning for Massive MIMO User Scheduling

Xingyu Huang, Ruining Fan, Mouli Chakraborty +2

We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addr…

eess.SP2025

On the Achievable Rate of Satellite Quantum Communication Channel using Deep Autoencoder Gaussian Mixture Model

Mouli Chakraborty, Subhash Chandra, Avishek Nag +1

We present a comparative study of the Gaussian mixture model (GMM) and the Deep Autoencoder Gaussian Mixture Model (DAGMM) for estimating satellite quantum channel capacity, consid…

eess.SP2024

A Hybrid Noise Approach to Modelling of Free-Space Satellite Quantum Communication Channel for Continuous-Variable QKD

Mouli Chakraborty, Anshu Mukherjee, Ioannis Krikidis +2

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative sate…

eess.SP2024

Hybrid Quantum Noise Approximation and Pattern Analysis on Parameterized Component Distributions

Mouli Chakraborty, Anshu Mukherjee, Ioannis Krikidis +2

Noise is a vital factor in determining the accuracy of processing the information of the quantum channel. One must consider classical noise effects associated with quantum noise so…

eess.SP2024★ 1 cited

An Unsupervised Machine Learning to Optimize Hybrid Quantum Noise Clusters for Gaussian Quantum Channel

Mouli Chakraborty, Anshu Mukherjee, Ioannis Krikidis +2

This work focuses on optimizing the hybrid quantum noise model to improve the capacity of Gaussian quantum channels using Machine Learning (ML) generated clusters. The work specifi…