# NOMA Aided Narrowband IoT for Machine Type Communications with User Clustering

## TL;DR

This paper proposes a NOMA-based user clustering scheme for NB-IoT to enhance connectivity and throughput for massive MTC devices, addressing limitations of traditional OMA methods.

## Contribution

It introduces a power domain NOMA scheme with user clustering for NB-IoT and develops an efficient heuristic algorithm for resource allocation and clustering.

## Key findings

- The proposed NOMA scheme improves network throughput.
- The heuristic algorithm effectively optimizes resource allocation.
- Simulation results confirm the scheme's efficiency.

## Abstract

To support Machine Type Communications (MTC) in next generation mobile networks, NarrowBand-IoT (NB-IoT) has been released by the Third Generation Partnership Project (3GPP) as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing Orthogonal Multiple Access (OMA) scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT, Non-Orthogonal Multiple Access (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, we propose a power domain NOMA scheme with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, we formulate an optimization problem to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. We prove the NP-hardness of the proposed optimization problem. We further design an efficient heuristic algorithm to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices. Furthermore, we prove that the reduced optimization problem of power control is a convex optimization task. Simulation results are presented to demonstrate the efficiency of the proposed scheme.