SAFE-GF-NOMA: Social Autonomous Flocking to Enhance GF-NOMA for Massive Internet of Things Uplink Access Contention

Abstract

The swift progress of 6G cellular networks responds to the urgent demand for seamlessly integrating Internet of Things (IoT) devices on a large scale. Among various emerging technologies, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a standout, offering distinct advantages over traditional networks in accommodating extensive connectivity needs. GF-NOMA optimizes resource usage by reallocating each time-frequency slot to multiple devices with different power levels. Furthermore, it reduces the coordination burden typically associated with uplink communication by broadcasting random access channels. Nonetheless, GF-NOMA faces a significant challenge: the risk of multiple devices inadvertently selecting the same resource and power, resulting in data loss, particularly problematic during emergencies marked by uncoordinated communications. Additionally, the expanding deployment of IoT devices demands a proportional increase in resources, despite advancements in network technology. To address these challenges, this paper introduces an innovative architecture aimed at significantly boosting spatial capacity through the establishment of autonomous social interactions among IoT devices. The proposed SAFE-GF-NOMA aggregation scheme facilitates resource sharing in an independent and ad-hoc trust management environment by ensuring trustworthy sharing. The proposed Social IoT (SIoT) framework reduces uplink access by grouping devices based on trust metrics, resulting in a notable 50% reduction in collision probability, and over a 50% increase in success probability, and a threefold capacity increase compared to conventional systems. Additionally, our system achieves a substantial reduction in energy consumption, cutting it from 17 J to just 5 J per device within the cluster.

Keywords

• Clustering
• GF-NOMA
• random access
• social Internet of Things
• uncoordinated access