Data Sensing and Offloading in Edge Computing Networks: TDMA or NOMA?

With the development of Internet-of-Things (IoT), we witness explosive growth in number devices with sensing, computing, and communication capabilities, along a large amount raw data generated at network edge. Mobile (multi-access) edge computing (MEC), acquiring processing (like base station (BS)) via wireless links, has emerged as promising technique for real-time applications. In this paper, consider scenario that multiple sense then offload to an server/BS, offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division access (TDMA) non-orthogonal (NOMA) multiuser computation offloading. Particularly, take sequence TDMA-based transmission/offloading into account. The NP-hard non-convex. A set low-complexity algorithms designed decomposition approach exploration valuable insights problems. They either optimal or can achieve close-to-optimal performance shown simulation. comprehensive simulation results show sequence-optimized TDMA scheme achieves better than NOMA scheme, while is under assumptions time-sharing strategy identical sensing capability devices.