Cooperative Caching Towards Efficient Spectrum Utilization in Cognitive-IoT Networks

TL;DR

This paper introduces a DRL-based cooperative caching method for CIoT networks that improves spectrum sharing efficiency by reducing latency and increasing cache hit rates through collaboration with primary users.

Contribution

It proposes a novel DRL framework for joint caching and spectrum access, enhancing spectrum utilization and network performance in CIoT networks.

Findings

Outperforms traditional methods in reducing latency

Increases cache hit rates for CIoT and PUs

Enhances overall network throughput

Abstract

In cognitive Internet of Things (CIoT) networks, efficient spectrum sharing is essential to address increasing wireless demands. This paper presents a novel deep reinforcement learning (DRL)-based approach for joint cooperative caching and spectrum access coordination in CIoT networks, enabling the CIoT agents to collaborate with primary users (PUs) by caching PU content and serving their requests, fostering mutual benefits. The proposed DRL framework jointly optimizes caching policy and spectrum access under challenging conditions. Unlike traditional cognitive radio (CR) methods, where CIoT agents vacate the spectrum for PUs, or relaying techniques, which merely support spectrum sharing, caching brings data closer to the edge, reducing latency by minimizing retrieval distance. Simulations demonstrate that our approach outperforms others in lowering latency, increasing CIoT and PU cache hit rates, and enhancing network throughput. This approach redefines spectrum sharing, offering a fresh perspective on CIoT network design and illustrating the potential of DRL-guided caching to highlight the benefits of collaboration over dynamic spectrum access scenarios, elevating CIoT performance under constrained resources.