A Novel Collaborative Cognitive Dynamic Network Architecture

TL;DR

This paper introduces a new collaborative cognitive dynamic network architecture that leverages spectrum sharing and intelligent resource management to enhance 5G network capabilities, focusing on adaptability, efficiency, and self-organization.

Contribution

The paper proposes a novel CDNA architecture integrating cognitive features for spectrum utilization, supporting multi-hop, multi-operator cooperation, and self-organizing network management.

Findings

Demonstrates potential benefits for users and operators through a resource allocation case study.

Shows CDNA's suitability for 5G requirements like energy efficiency and scalability.

Highlights challenges and future directions for CDNA development.

Abstract

Increasing mobile data demands in current cellular networks and proliferation of advanced handheld devices have given rise to a new generation of dynamic network architectures (DNAs). In a DNA, users share their connectivities and act as access points providing Internet connections for others without additional network infrastructure cost. A large number of users and their dynamic connections make DNA highly adaptive to variations in the network and suitable for low cost ubiquitous Internet connectivity. In this article, we propose a novel collaborative cognitive dynamic network architecture (CDNA) which incorporates cognitive capabilities to exploit underutilized spectrum in a more flexible and intelligent way. The design principles of CDNA are perfectly aligned to the functionality requirements of future 5G wireless networks such as energy and spectrum efficiency, scalability, dynamic reconfigurability, support for multi-hop communications, infrastructure sharing, and multi-operator cooperation. A case study with a new resource allocation problem enabled by CDNA is conducted using matching theory with pricing to illustrate the potential benefits of CDNA for users and operators, tackle user associations for data and spectrum trading with low complexity, and enable self-organizing capabilities. Finally, possible challenges and future research directions are given.