The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
N.Prashanth Kumar N.Prashanth Kumar, A. A. A Akshitha, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
Radar signal processing has become a fundamental technology for autonomous vehicles because of its ability to provide reliable object detection and tracking under diverse environmental conditions, including rain, fog, snow, and low-light scenarios. Conventional radar systems often face challenges such as clutter, noise, multipath interference, and limited target resolution, which can affect the accuracy of perception. This paper presents an advanced radar signal processing framework for autonomous vehicles that integrates adaptive preprocessing, target detection, clutter suppression, feature extraction, and object classification to improve perception performance. The proposed approach employs digital signal processing techniques combined with deep learning-based classification to accurately identify vehicles, pedestrians, cyclists, and other road obstacles from radar data. Multi-target tracking algorithms are incorporated to estimate object position, velocity, and trajectory in real time, enabling safe navigation and collision avoidance. Experimental evaluation demonstrates that the proposed framework achieves higher detection accuracy, improved target localization, and robust performance under challenging weather and traffic conditions while maintaining low computational complexity suitable for real-time deployment. The proposed radar signal processing system enhances the reliability, safety, and efficiency of autonomous driving by providing accurate environmental perception for intelligent decision-making.
Kuruba Theja Kuruba Theja, Dharavath Sunil Dharavath Sunil, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing by enabling intelligent monitoring and automation of industrial equipment. However, unexpected machine failures continue to cause production downtime, increased maintenance costs, and reduced operational efficiency. Predictive maintenance has emerged as an effective strategy to address these challenges by forecasting equipment failures before they occur. This paper proposes an Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology. The proposed framework continuously collects machine parameters such as vibration, temperature, pressure, current consumption, and acoustic signals through connected sensors. The collected data is analyzed using machine learning algorithms to identify anomalies, estimate remaining useful life (RUL), and generate maintenance recommendations. A digital twin model provides a virtual representation of industrial assets, enabling real-time simulation and performance evaluation. Experimental results demonstrate significant improvements in fault detection accuracy, equipment availability, and maintenance efficiency while reducing downtime and operational expenses. The proposed system contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations