Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

TL;DR

This paper surveys how AI, including machine learning and data analytics, can enhance the operation and management of next-generation wireless networks like 5G and beyond, focusing on distributed, context-aware solutions.

Contribution

It provides a comprehensive overview of AI techniques applied to future wireless networks, highlighting their role in data acquisition, network planning, and management, with a case study included.

Findings

AI techniques improve data collection and knowledge discovery.

AI enhances network planning and management efficiency.

Case study demonstrates practical AI application in wireless networks.

Abstract

The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical challenges for network planning, operation, management and troubleshooting. At the same time, generation and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. In mitigating the complexity of future network operation, new approaches of intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. These promising architectures make the adoption of artificial intelligence (AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP) techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network planning, operation and management of the next generation wireless networks. A brief case study utilizing the AI techniques for this network has also been provided.