# Fast Uplink Grant for NOMA: a Federated Learning based Approach

## TL;DR

This paper introduces a federated learning-based resource allocation approach for NOMA in 5G, improving uplink grant efficiency and QoS management for Machine Type Devices through three novel techniques.

## Contribution

It presents three new resource allocation frameworks for NOMA-based systems, including a federated learning traffic model estimation and QoS-aware scheduling.

## Key findings

- Federated learning enhances traffic estimation for NOMA.
- Proposed techniques outperform existing methods in simulations.
- QoS differentiation improves service for different device modes.

## Abstract

Recently, non-orthogonal multiple access (NOMA) technique have emerged and is being considered as a building block of 5G systems and beyond. In this paper, we focus on the resource allocation for NOMA-based systems and we investigate how Machine Type Devices (MTDs) can be arranged into clusters. Specifically , we propose three allocation technique to enable the integration of massive NOMA-based MTD in the 5G. Firstly, we propose a low-complexity schema where the BS assigns an MTD to a cluster based on its CSI and transmit power in order to ensure that the SIC can be performed in the uplink as well as the downlink. In the second framework, we propose a federated-learning based traffic model estimation in order to extend the capacity of the system. In fact, the BS take into account the traffic model of the MTDs in order to use time multiplexing in addition to the power multiplexing to separate MTDs. After the BS allocation, we propose a learning algorithm to allow contending MTDs synchronize their transmissions. Finally, we focus on the Quality of Service (QoS) for NOMA based devices and we propose in the third framework a QoS-aware technique, where MTDs are served differently based on their mode, i.e. regular or alarm modes. Simulation results show that the proposed techniques outperform existing technique in the literature.