A Probabilistic Approach to Model SIC based RACH mechanism for Massive Machine Type Communications in Cellular Networks

In cellular Internet of Things, burst transmissions from millions of machine type communications (MTC) devices can result in channel congestion. The main bottleneck in such scenario is the inefficient random access channel (RACH) mechanism that is used to attach MTC devices to a base station (BS). To address this issue of congestion in RACH mechanism, 3GPP has proposed an extended access barring (3GPP-EAB) mechanism. However, several works indicate that the performance of the 3GPP-EAB mechanism can be further improved. In this work, a successive interference cancellation (SIC) based RACH mechanism is considered to significantly increase the RACH success rate and reduce congestion. In the proposed mechanism, the devices are allowed to transmit repeatedly for a finite number of times in a given radio frame, and thereafter, the success rate is improved by applying back-and-forth SIC at the BS. A Markov model of the proposed mechanism is presented with all transition and steady-state probabilities. Further, the probability of successful SIC for a given state of the Markov chain is derived. Through extensive numerical results, it is shown that the proposed mechanism significantly outperforms the state-of-the-art mechanisms in terms of the success rate and average access delay. Moreover, to maximize the success rate, the optimum number of devices to be entered in a radio frame is also calculated. Finally, we investigate the effect of imperfect SIC, increasing number of contending devices on the performance of the proposed mechanism.