Performance Analysis on Massive Random-Access Based on Repetition and Post-Encoding Processing

The relentless advancement of wireless mobile communication technology, evolving from 1G to 5G and now venturing into 6G, has prompted an exploration of massive random-access (MRA) techniques to accommodate the anticipated massive communication demands. This paper delves into the domain of MRA technology, garnering increasing attention in 6G Internet of Things (IoT) networks. Based on extensive prior research on MRA, this paper analyzes the impact of repetitions and post-encoding processing on MRA performance. Two scenarios are considered: the ideal post-encoding processing scenario with the maximum processing gain, where each repetition is treated as independent; and the identical post-encoding processing scenario with the most basic processing gain, where all repetitions undergo the same processing steps. Through theoretical derivations, performance bounds for MRA with joint decoding across all actual IoT devices and all repetitions are established for both scenarios. The special scenario concerning performance bounds, where the repetition number is 1, aligns with existing results. Numerical evaluations have confirmed the accuracy of the theoretical derivations and further demonstrated that, as the number of repetitions increases, the performance disparity between the ideal post-encoding processing scenario and the identical post-encoding processing scenario widens. The performance bounds of other post-encoding processing schemes lie between the ideal and the identical post-encoding processing. These findings emphasize optimizing post-encoding processing schemes to bring actual performance closer to the ideal post-encoding processing scenario.