# Secrecy Analysis and Learning-based Optimization of Cooperative NOMA SWIPT Systems

## TL;DR

This paper investigates the security of cooperative NOMA systems with energy harvesting, deriving intercept probabilities and using deep learning to optimize power allocation, demonstrating improved robustness over traditional methods.

## Contribution

It introduces a deep learning-based approach for optimizing power allocation in secure cooperative NOMA SWIPT systems, considering eavesdropper threats and energy harvesting constraints.

## Key findings

- Deep learning optimization outperforms iterative search in power allocation.
- Analytical expression for intercept probability in cooperative NOMA with energy harvesting.
- Enhanced security and efficiency in NOMA systems through proposed methods.

## Abstract

Non-orthogonal multiple access (NOMA) is considered to be one of the best candidates for future networks due to its ability to serve multiple users using the same resource block. Although early studies have focused on transmission reliability and energy efficiency, recent works are considering cooperation among the nodes. The cooperative NOMA techniques allow the user with a better channel (near user) to act as a relay between the source and the user experiencing poor channel (far user). This paper considers the link security aspect of energy harvesting cooperative NOMA users. In particular, the near user applies the decode-and-forward (DF) protocol for relaying the message of the source node to the far user in the presence of an eavesdropper. Moreover, we consider that all the devices use power-splitting architecture for energy harvesting and information decoding. We derive the analytical expression of intercept probability. Next, we employ deep learning based optimization to find the optimal power allocation factor. The results show the robustness and superiority of deep learning optimization over conventional iterative search algorithm.