# Iris: Deep Reinforcement Learning Driven Shared Spectrum Access Architecture for Indoor Neutral-Host Small Cells

## TL;DR

This paper introduces Iris, a deep reinforcement learning-based shared spectrum access system for indoor small cells, enabling efficient, incentive-compatible spectrum sharing among multiple operators within a neutral-host infrastructure.

## Contribution

The paper presents a novel deep RL-driven dynamic pricing architecture for shared spectrum access in indoor small-cell networks, with practical system design and experimental validation.

## Key findings

- Iris's dynamic pricing outperforms alternative methods in efficiency.
- The prototype demonstrates feasible deployment in real indoor environments.
- Experimental results show improved spectrum utilization and operator incentives.

## Abstract

We consider indoor mobile access, a vital use case for current and future mobile networks. For this key use case, we outline a vision that combines a neutral-host based shared small-cell infrastructure with a common pool of spectrum for dynamic sharing as a way forward to proliferate indoor small-cell deployments and open up the mobile operator ecosystem. Towards this vision, we focus on the challenges pertaining to managing access to shared spectrum (e.g., 3.5GHz US CBRS spectrum). We propose Iris, a practical shared spectrum access architecture for indoor neutral-host small-cells. At the core of Iris is a deep reinforcement learning based dynamic pricing mechanism that efficiently mediates access to shared spectrum for diverse operators in a way that provides incentives for operators and the neutral-host alike. We then present the Iris system architecture that embeds this dynamic pricing mechanism alongside cloud-RAN and RAN slicing design principles in a practical neutral-host design tailored for the indoor small-cell environment. Using a prototype implementation of the Iris system, we present extensive experimental evaluation results that not only offer insight into the Iris dynamic pricing process and its superiority over alternative approaches but also demonstrate its deployment feasibility.