Minimizing NOMA Resource Usage for Cellular IoT Networks through Distributed Source Coding

Many research endeavors to optimize the performance of NOMA (non-orthogonal multiple access) have focused on mobile broadband applications, where transmissions typically occur in the downlink with full-buffered traffic and the primary performance metric is the sum date rates. Machine-type communications, however, are distinctly different, where transmissions typically occur in the uplink and traffic load from individual machines is low with short payloads. To optimize NOMA performance for resource-constrained cellular IoT (Internet of Things) networks, in this paper we investigate a problem to minimize total radio resource used by all IoT devices for sending the collected data to the base station. By employing distributed source coding (DSC) between the two nodes in a NOMA pair, we first solve the resource optimization problem of a two-node pair through joint data and power allocation. With the calculated resource usage of two nodes in OMA and NOMA, we then construct a weighted graph and investigate maximum weight matching for pairing IoT devices in the network with minimum total resource usage. We compare different pairing algorithms for solving the problem while striking a good balance between complexity and optimality. Simulation results show that DSC can achieve significant performance gain for NOMA resource usage compared to the baseline approaches in cellular IoT networks.