Publication

Abstract : This study attempts to address the growing demand for efficient spectrum use in ultra-dense networks (UDNs) as a result of the exponential growth in data traffic and wire- less communication devices. Traditional spectrum management techniques are unable to manage the increasing number of connected devices, which leads to congestion, interference, and inadequate resource allocation. This study provides a comprehensive examination of how to optimise spectrum usage in ultra-dense wireless communication networks using cutting-edge technologies such as cognitive radio (CR), dynamic spectrum access, and machine learning. The proposed approach ensures that underutilised spectrum bands are efficiently used by utilising cognitive radio to provide adaptive allocation and spectrum sensing. Two machine learning techniques that are used in tandem to predict traffic patterns and improve resource allocation in real-time, lowering interference and boosting throughput, are reinforcement learning and neural networks. The results of this study are anticipated to contribute to the development of dependable wireless communication systems that can meet the requirements of future 5G/6G technologies, Internet of Things (IoT) networks, and smart cities. Future studies will look into the combination of blockchain technology and quantum computing to enhance spectrum optimisation in ultra-dense networks.