Intelligent RACH Access Techniques to Support M2M Traffic in Cellular Networks

This paper provides a thorough investigation into the use of Q-learning as a means of supporting machine-to-machine (M2M) traffic over cellular networks through the random access channel (RACH). A new back-off scheme is proposed for RACH access, which provides separate frames for M2M and conventional cellular (H2H) retransmissions, and is capable of dynamically adapting the frame size in order to maximize channel throughput. Analytical models are developed to examine the interaction of H2H and M2M traffic on the RACH channel, and to evaluate the throughput performance of both slotted ALOHA and Q-learning-based access schemes. It is shown that Q-learning can be effectively applied for M2M traffic, significantly increasing the throughput capability of the channel with respect to conventional slotted ALOHA access. Dynamic adaptation of the back-off frames is shown to offer further improvements relative to a fixed-frame scheme.