Dottorato in TECNOLOGIE DELL'INFORMAZIONE E DELLE COMUNICAZIONI (ICT)

Titolo della tesi: Cross-Layer Adaptive Random Access Protocols for Massive IoT Systems

As 5G and beyond networks evolve, enabling scalable and energy efficient connectivity for massive numbers of devices has become a fundamental challenge. In massive Machine-Type Communication (mMTC) scenarios, where billions of low power devices generate sporadic traffic, conventional coordinated scheduling becomes impractical. Random access, empowered by Successive Interference Cancellation (SIC)-enabled receivers, offers a promising solution by allowing multiple devices to transmit simultaneously without coordination. However, maintaining reliability, spectral efficiency, and information freshness under dynamic load conditions remains a critical open problem. This research develops a unified analytical and asymptotic framework to characterize the performance of SIC-enabled random access systems. The framework captures the interaction between the Medium Access Control (MAC) layer and the Physical (PHY) layer, quantifying how the joint adaptation of transmission probability and target Signal-to-Noise-plus-Interference Ratio (SNIR) influences key performance metrics such as spectral efficiency, energy consumption, access delay, and Age of Information (AoI). The analysis demonstrates that traditional random access schemes, such as Slotted ALOHA (SA) and Carrier-Sense Multiple Access (CSMA), operate sub-optimally under dense conditions, as they fail to exploit the Multi-Packet Reception (MPR) capability offered by SIC. Building on these foundations, the study introduces adaptive access control strategies that link optimal transmission probability and decoding threshold to the instantaneous network load. These adaptive scalings enable stable and efficient operation across both contention limited and interference limited regimes. Contrary to conventional design intuition, the analysis reveals that operating in the low SNIR regime can achieve higher spectral efficiency when combined with interference cancellation, effectively enabling grant-free operation in dense networks. Power control is also shown to play a critical role: the Equal Average Received Power (EARP) scheme minimizes energy consumption, while the Maximum transmission Power (MaxP) approach improves decoding performance through power diversity. To extend these results to realistic, non-saturated conditions, the thesis proposes the Multi-packet reception Adaptive Slotted Access (MASA) algorithm, which jointly adapts the transmission probability and decoding threshold as functions of the instantaneous backlog. Analytical modeling and extensive simulations demonstrate that MASA achieves high reliability, low latency, and improved timeliness, outperforming state-of-the-art adaptive schemes such as Age-Independent Random Access (AIRA)-SIC. The study also identifies a distinct energy-AoI trade-off curve with a clear operating knee, representing the optimal balance between freshness and energy efficiency in massive access networks. The findings of this research provide critical insights for the design of next generation grant-free access protocols. By establishing a theoretical and algorithmic foundation for cross layer adaptation, this work advances the scalability, timeliness, and energy efficiency of random access in dense Internet of Things (IoT) deployments. These results contribute to the evolution of 6G communication systems, paving the way toward more intelligent, resilient, and energy aware network architectures for the IoT.