# NOMA-based Compressive Random Access using Gaussian Spreading

## TL;DR

This paper explores integrating NOMA with compressive random access using Gaussian spreading to enhance device detection in massive machine-type communication, demonstrating improved detection accuracy through large-system analysis and simulations.

## Contribution

It introduces a novel application of power-domain NOMA to CRA with Gaussian spreading, deriving design criteria via large-system analysis to support more devices.

## Key findings

- NOMA reduces device activity detection errors.
- Design criteria for power levels are established.
- Simulation confirms improved detection performance.

## Abstract

Compressive random access (CRA) is a random access scheme that has been considered for massive machine-type communication (MTC) with non-orthogonal spreading sequences, where the notion of compressive sensing (CS) is used for low-complexity detectors by exploiting the sparse device activity. In this paper, we study the application of power-domain non-orthogonal multiple access (NOMA) to CRA in order to improve the performance of CRA (or increase the number of devices to be supported). We consider Gaussian spreading sequences and derive design criteria through a large-system analysis to determine the power levels for power-domain NOMA. From simulation results, we can confirm that the number of incorrectly detected device activity can be reduced by applying NOMA to CRA.