Big Data Methods for Ultra‐dense Network Deployment

This chapter outlines big data methods for ultra‐dense network (UDN) deployment from the perspective of two data science approaches: identify spatial‐temporal traffic and service challenge patterns to aid UDN targeted deployment; and identify social community patterns to assist ultra‐dense peer‐to‐peer and device‐to‐device networking. It reviews appropriate research on heterogeneous big data analytics to optimize the deployment of wireless networks. The chapter examines how social media data can quantify traffic patterns across various temporal and spatial scales. The methodological aspects of this work includes structured data analytics, such as clustering and social community detection. The chapter examines how social media data can improve our understanding of sentiment toward different service topics, and reinforce our understanding of poor quality‐of‐experience (QoE) reasons and locations for targeted improvement. As the data are often text based and not numerical, the methodological aspects of this work includes unstructured data analytics, such as natural language processing (NLP) and surveys.