Data-Driven Low-complexity Detection in Grant-Free NOMA for IoT

This paper proposes a low-complexity data-driven multi-user detector for grant-free non-orthogonal multiple access (GF-NOMA), which has gained significant interest in Internet of Things (IoT). IoT traffic is predominantly sporadic, where devices become active whenever they have data to transmit. The conventional grant-access procedure for requesting a transmission slot every time results in significant signaling overhead and latency. In power domain GF-NOMA, multiple devices can be preallocated the same channel resource, but different power levels. Whenever a device has data, it starts transmission directly using the allocated power level without any grant request. While this significantly reduces the signaling overhead, the access point has to perform the complex task of identifying the active devices and decoding their data. Conventional receivers for power domain NOMA fail in such GF scenarios and the typical solution is to limit transmissions to be packet-synchronized and add carefully chosen pilots in every packet to facilitate activity detection. However, in fairly static IoT networks with low-complexity devices and small packet sizes, this represents a significant overhead and reduces efficiency. In this work we solve the GF-NOMA detection problem without these constraints, by analyzing the boundaries of the received constellation points in power domain GF-NOMA for all activation combinations at once. A low-complexity decision tree-based receiver is proposed, which performs as well as the maximum likelihood-based benchmark receiver, and better than traditional data-driven detectors for GF-NOMA. Comprehensive simulation results demonstrate the performance of the proposed detector in terms of its detection efficiency and parameter learning with minimal training data.