A Study on Non-Orthogonal Multiple Access for Data-Centric Machine-to-Machine Wireless Networks

摘要

In many IoT (Internet-of-Things) applications, IoT devices (machines) are deployed to collectively gather data required by the target application, and the data gathered by individual machines is often correlated. Leveraging the fact that it is the quality of the data that matters to the application rather than the link qualities of individual machines, data-centric machine-to-machine (M2M) communications prioritize machines based on the importance of data they carry to optimize system design. In this paper, we investigate the use of non-orthogonal multiple access (NOMA) technique for data-centric M2M wireless networks. In particular, we focus on the problem of how machines should be paired for resource sharing during transmission scheduling. We have found that the conventional NOMA scheduling strategy to maximize the sum data rates of co-scheduled users can result in worse performance than OMA (orthogonal multiple access) for data-centric communications. To address the problem, we propose a scheduling strategy to minimize the waiting time among co-scheduled users by taking into consideration the amount of data to transmit and achievable data rates of NOMA users. Evaluation results show that such a scheduling strategy performs much better than sum-rate maximization scheduling for data-centric M2M networks.