Srijita Maity

Work place: School of Electronics Engineering, Vellore Institute of Technology, Vellore – 632014, Tamil Nadu, India

E-mail: srijita.1206@gmail.com

Website: https://orcid.org/0009-0000-8029-0359

Research Interests:

Biography

Srijita Maity is an Undergraduate student at VIT Vellore, India. She is pursuing Electronics and Communication Engineering. She has worked on projects involving RIS-assisted D2D communication, UAV-enabled wireless systems, and channel estimation using machine learning techniques. She has interned at TATA Steel India Ltd. and worked on a research project under Samsung PRISM.Her current research focuses on various wireless communication networks, intelligent communication systems, and Machine learning.

Author Articles
Analysis of Binary Modulations Using Channel Estimation with Machine Learning

By Srijita Maity Sanjana Bhattacharjee Hemanta Kumar Sahu

DOI: https://doi.org/10.5815/ijwmt.2025.04.04, Pub. Date: 8 Aug. 2025

To ensure robust signal recovery and efficient data transmission in wireless communication systems, accurate channel estimation plays a vital role, especially under dynamic and complex conditions. Machine learning-based channel estimation is explored for binary phase shift keying (BPSK) and quadrature phase shift keying  (QPSK) modulation schemes over Rayleigh, Rician, and Gaussian fading models. In this work, a framework using Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP) is developed to predict channel coefficients and analyze the impact on bit error rate (BER), throughput, and spectral efficiency for binary modulations. A comprehensive performance comparison of BPSK and QPSK under ML-based estimation across various fading conditions is provided. The results show that CNNs are effective in tracking time-varying coefficients, while MLPs often yield lower mean squared error (MSE). The study emphasizes practical applications in low-SNR environments and supports energy-efficient designs aligned with SDG goals. Key simulation results include BER vs SNR, throughput, and spectral efficiency comparisons between BPSK and QPSK under ML estimation.

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