Massive Wireless Random Access With Successive Decoding: Delay Analysis and Optimization

In Internet of Things, wireless access networks are required to support a large number of user equipments (UEs) in real time. With UEs' frequently arrival and departure, plenty of packet collisions can occur. Successive decoding thus becomes a promising technique to support the massive connectivity as it is capable of recovering packets from mixed received signals. In this paper, we propose an adaptive framing with successive decoding (AFSD) frame structure to deal with the fluidity of UEs. In the AFSD structure, the frame length is adaptively adjusted based on the present UE number, and successive decoding is adopted to alleviate the packet loss caused by packet collision. To quantify its performance, we provide exact as well as asymptotic results for the average delay. The analytical results are validated by simulations, and further extended to practical systems with estimated UE number, which shows that knowledge of active UE number is useful for delay reduction. Optimizations on UE transmission probability and frame length are also presented. In particular, the numerical studies reveal that by using optimized parameters, the average delay is reduced significantly for a wide range of arrival rates, which validates the effectiveness of the AFSD structure.