3D Transition Matrix Solution for a Path Dependency Problem of Markov Chains-Based Prediction in Cellular Networks

Abstract

Handover (HO) management is one of the critical challenges in current and future mobile communication systems due to new technologies being deployed at a network level, such as small and femtocells. Because of the smaller sizes of cells, users are expected to perform more frequent HOs, which can increase signaling costs and also decrease user's performance, if a HO is performed poorly. In order to address this issue, predictive HO techniques, such as Markov chains (MC), have been introduced in the literature due to their simplicity and generality. This technique, however, experiences a path dependency problem, specially when a user performs a HO to the same cell, also known as a re-visit. In this paper, the path dependency problem of this kind of predictors is tackled by introducing a new 3D transition matrix, which has an additional dimension representing the orders of HOs, instead of a conventional 2D one. Results show that the proposed algorithm outperforms the classical MC based predictors both in terms of accuracy and HO cost when re-visits are considered.

IMPRESS: Indoor Mobility Prediction Framework for Pre-Emptive Indoor-Outdoor Handover for mmWave Networks

“…As part of their work in [10], the authors used the users' mobility history to input a transition probability matrix, which was used to uncover the most frequently visited base stations. To reduce the HO delay in 4G X2 HO, [11] proposes a machine learning model for managing the mobility the part of the HO process to improve prediction of future HOs. In order to solve the path dependency problems arising from classical Markov chains, which occur when users access the same cell repeatedly, the authors introduced a 3D transition matrix.…”

Indoor Mobility Prediction for mmWave Communications using Markov Chain

“…The authors in [5], expanded their work in [4] by using user's mobility history as an input to a transition probability matrix, to discover the most frequently visited base station. In [6], a machine learning based mobility management scheme for 4G X2 HO process is proposed to predict future HOs in order to reduce the HO delay. The authors introduced the concept of 3D transition matrix to address the path dependency problem of classical Markov chain, which occurs when users perform the HOs to the same cell.…”