Application and Analysis of Improved Multi-Channel Slotted ALOHA Algorithm in OFDMA Networks

With the rapid growth of the Internet of Things (IoT), the number of connected devices in cellular networks is expanding significantly, particularly in scenarios such as smart cities, Industry 4.0, and autonomous driving. The Random Access Channel (RACH) plays a key role in managing the initial network access for these devices, but faces significant challenges, including resource contention and channel congestion in Orthogonal Frequency Division Multiple Access (OFDMA) networks. The traditional RACH mechanism, based on the Poisson arrival process, struggles to handle the bursty traffic patterns typical in IoT applications. To address these issues, we propose a method that combines a multi-channel Slotted ALOHA mechanism with a drift approximation model to optimize RACH performance under high device loads. The proposed method includes a grouping strategy to prioritize delay-sensitive devices, an improved multi-channel Slotted ALOHA mechanism, and a discrete time drift model to capture transient system dynamics. Simulation results show that our method reduces collision probability and improves access success rates, even under high device density scenarios.