# Fronthaul Quantization as Artificial Noise for Enhanced Secret Communication in C-RAN

## TL;DR

This paper proposes using the quantization noise from fronthaul compression in C-RAN as artificial noise to improve secrecy rates, optimizing joint precoding and compression strategies.

## Contribution

It introduces a novel approach to enhance physical layer security in C-RAN by controlling quantization noise as artificial noise through joint optimization.

## Key findings

- Quantization noise can be effectively used as artificial noise for secrecy.
- Joint optimization of precoding, compression, and artificial noise improves secrecy rates.
- The proposed method outperforms traditional schemes without artificial noise.

## Abstract

This work considers the downlink of a cloud radio access network (C-RAN), in which a control unit (CU) encodes confidential messages, each of which is intended for a user equipment (UE) and is to be kept secret from all the other UEs. As per the C-RAN architecture, the encoded baseband signals are quantized and compressed prior to the transfer to distributed radio units (RUs) that are connected to the CU via finite-capacity fronthaul links. This work argues that the quantization noise introduced by fronthaul quantization can be leveraged to act as "artificial" noise in order to enhance the rates achievable under secrecy constraints. To this end, it is proposed to control the statistics of the quantization noise by applying multivariate, or joint, fronthaul quantization/compression at the CU across all outgoing fronthaul links. Assuming wiretap coding, the problem of jointly optimizing the precoding and multivariate compression strategies, along with the covariance matrices of artificial noise signals generated by RUs, is formulated with the goal of maximizing the weighted sum of achievable secrecy rates while satisfying per-RU fronthaul capacity and power constraints. After showing that the artificial noise covariance matrices can be set to zero without loss of optimaliy, an iterative optimization algorithm is derived based on the concave convex procedure (CCCP), and some numerical results are provided to highlight the advantages of leveraging quantization noise as artificial noise.