Information Freshness of Massive Heterogeneous IoT Systems With Double-Layer NOMA-ALOHA

Abstract

This work focuses on the uplink transmission of a massive IoT update system to a common access point. Firstly, using non-orthogonal multiple access (NOMA), a NOMA-ALOHA-based double-layer (DL) primary-secondary random access (RA) scheme is proposed, where the users are divided into two groups based on user priorities, i.e., primary users and secondary users. Secondly, to enhance the capacity of RA and overcome the non-linear hardware limitations of the transceiver, with the successive interference cancellation (SIC) a DL primary-secondary signal-to-interference-plus-noise ratio (SINR) level allocation scheme is proposed. Based on the conventional single-layer SINR level allocation, the SINR scale factor of the secondary system is derived, by which the secondary SINR levels are inserted into the primary ones. Thirdly, a required generation and random preemption (RG-preemption) policy is proposed. In this policy, at the beginning of each update period, with the requirement by access point, all users simultaneously generate and randomly transmit their status updates. During the remainder of the update delivery period, new random generation preempts the current update until the update delivery is successful or the current update period ends. Finally, using phase-type distribution, the work presents the derivations regarding absorbing Markov chain, one-step transition matrix, and the AoI of the secondary user.