A 3-D Markov Chain Model for a Multi-Dimensional Indoor Environment

Abstract

In this paper, a pico-cellular airport traffic model is proposed which supports Engset distributed fresh call arrival process and General distributed handoff process with Dynamic Channel Allocation (DCA). The proposed model enables load balancing using DCA and uses a three-dimensional Markov chain to compute traffic congestion and call congestion for any kind of traffic streams, including Pure Chance Type-I (PCT-I) or Pure Chance Type-II (PCT-II). The application of the proposed model is illustrated in assessing indoor mobility to evaluate QoS parameters. The proposed airport traffic model is fairly general in the sense that it is not restricted by number of users, user mobility or range of offered load, and can be reduced to predict congestion for Poisson distributed fresh call arrival processes and General distributed handoff processes.

Spatial Spectrum and Energy Efficiency of Random Cellular Networks

“…Both numerical and simulation results have shown that the FSMC channel model provides an accurate model for the real channels. Based on a three-dimensional Markov chain model, a pico-cellular airport traffic model was proposed in [12] which models Engset distributed fresh call arrival process and the general distributed handoff process with dynamic channel allocation. The energy efficiency of fixed-rate transmissions from Markov sources over Rayleigh fading channels was investigated in [13].…”

Self‐organising comprehensive handover strategy for multi‐tier LTE‐Advanced heterogeneous networks

“…A newly proposed three‐dimensional (3D) Markov chain model for indoor applications and LTE femtocell are also discussed in [14, 15], respectively, which are required to define priority in the HO algorithm. On the other hand, different mobility patterns and dynamic network conditions might cause challenging situation for MUs, despite femtocells' capability of providing services in shadowed areas by cell coverage enhancements.…”

“…Seamless handover is planned and simulated in [13] by reactively multicasting the data to both the source and target cells after the handover is actually initiated, which results in a reduction in the downlink service interruption time, as well as avoiding the packet loss compared to the standard 3GPP, with only limited extra requirements. Newly proposed 3-D Markov Chain model for indoor applications and LTE femtocell are also discussed in [14], [15] respectively, which are required to define priority in the handover algorithm. On the other hand, different mobility patterns and dynamic network conditions might cause challenging situation for mobile users, despite femtocells capability of providing services in shadowed areas by cell coverage enhancements.…”