Enhancing Spectrum Sharing with Real-Time Traffic Pattern Recognition using Gated Recurrent Unit

Abstract

As a result of an expanding number of connected devices and a greater demand for bandwidth, efficient radio spectrum management is more vital than ever. Spectrum sharing, especially in cognitive networks, offers a flexible technique that allows numerous users to use the same frequency bands at the same time, resulting in improved network performance.  This paper looks at how real-time traffic pattern detection utilizing Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks can enhance spectrum sharing in wireless communication systems. In this research, we look at the problems of integrating machine learning models for spectrum management, with a specific emphasis on improving LSTM and GRU networks for resource-constrained contexts. By examining traffic patterns, it is demonstrated that these models can minimize packet loss and enhance resource allocation. The findings show that, while both LSTM and GRU successfully reduce error rates, the GRU model outperforms the former because of its quicker learning speed and lower error values, making it particularly helpful in dynamic network environments. These findings underscore the rising importance of machine learning in spectrum management, paving the way for more flexible and efficient communication systems, particularly in high-density locations where dependable connection is critical.