DNN-Based Non-Linear Precoding for Bandwidth, Latency, and Data Rate Optimization in 6G Networks

As the demand for higher bandwidth, increased peak data rates, and reduced latency continues to escalate, traditional communication systems encounter limitations that necessitate innovative approaches. The research identifies the existing gaps in current 6G network architectures, where conventional precoding methods struggle to fully exploit the potential of non-linear signal processing. This research aims to bridge this gap by introducing a novel DNN-based precoding technique that harnesses the power of artificial intelligence for optimized signal transmission. In the pursuit of advancing communication technologies, this research proposes a groundbreaking solution leveraging Deep Neural Network (DNN) based Non-Linear Precoding to address critical challenges in 6G networks. The study involves the development and training of a sophisticated DNN model capable of learning complex non-linear precoding patterns. This model is integrated into the 6G network architecture to dynamically adapt and optimize signal precoding based on real-time network conditions. Results from extensive simulations and experiments demonstrate a substantial improvement in key performance metrics. The proposed DNN-based non-linear precoding exhibits superior efficiency compared to traditional methods, showcasing a remarkable increase in bandwidth, peak data rate, and a substantial reduction in latency.