Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

The emergence of next generation networks (NextG), including 5G and beyond, is reshaping the technological landscape of cellular and mobile networks. These networks are sufficiently scaled to interconnect billions of users and devices. Researchers in academia and industry are focusing on technological advancements to achieve high-speed transmission, cell planning, and latency reduction to facilitate emerging applications such as virtual reality, the metaverse, smart cities, smart health, and autonomous vehicles. NextG continuously improves its network functionality to support these applications. Multiple input multiple output (MIMO) technology offers spectral efficiency, dependability, and overall performance in conjunction with NextG. This article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial attacks. The technique aims to create long-lasting and secure NextG networks using this extended approach. The viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this research. Moreover, the proposed model demonstrates high performance in terms of reliability and accuracy, with a 20% reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.