LSTM Network for Hotspot Prediction in Traffic Density of Cellular Network

Abstract

This paper implements long short-term memory (LSTM) network to predict hotspot parameters in traffic density of cellular networks. The traffic density depends on numerous factors like time, location, number of mobile users connected and so on. It exhibits spatial and temporal relationships. However, only certain regions have higher data rates, known as hotspots. A hotspot is defined as a circular region with a particular centre and radius where the traffic density is the highest compared to other regions at a given timestamp. Forecasting traffic density is very important, especially in urban areas. Prediction of hotspots using LSTM would result in better resource allocation, beam forming, hand overs and so on. We propose two methods, namely log likelihood ratio (LLR) method and cumulative distribution function (CDF) method to compute the hotspot parameters. On comparing the performances of the two methods, it can be concluded that the CDF method is more efficient and less computationally complex than the LLR method.