Online Estimation and Adaptation for Random Access With Successive Interference Cancellation

In slotted random access systems, when multiple users transmit packets simultaneously, owing to the successive interference cancellation (SIC) technique, the access point (AP) is able to decode them through SIC-enabled resolution procedures (SRPs), which may occupy multiple consequent slots. While such an SRP could potentially improve the system throughput, how to fully exploit it in practical systems is still questionable when SIC capability is limited. Moreover, the number of active users contending for the channel varies over time which complicates the random access algorithm design. In order to full exploit the potential of such limited SIC capability and maximize the system throughput, a novel online estimation based on Bayesian approach is introduced to estimate the number of active users in real-time and controls each user's transmission accordingly. It is shown that the throughput of the proposed algorithm can reach up to 0.693 packets/slot under practical assumptions, which is the first result achieving the throughput limit proved by Yu–Giannakis. It is further shown that the system throughput of 0.594 packets/slot (85.7
$\%$
of the throughput limit) is achievable even when the SIC capability is restricted by two. It is also shown that the proposed online estimation and adaptation framework can be extended to further exploit collision information and the imperfect SIC.