Characterization of sparse WLAN data traffic in opportunistic indoor environments as a prior for coexistence scenarios of modern wireless technologies

CR enabled radio environment provides the seamless operating framework to cope with the requirements of next generation wireless systems like higher data rates, massive machine time communication and interference free coexistence scenarios of wireless technologies. Traffic characterization studies assist to optimize coexistence framework by providing necessary information about the usage patterns of wireless services in observed radio bands. Prior knowledge about the observed sparse wireless local area network (WLAN) data traffic is a key for opportunistic radio resource allocation and utilization in the given coexistence scenarios of WLAN either with cellular systems (including 5G-NR/4G Long Term Evolution (LTE)) or with low power wireless systems (including Bluetooth and ZigBee). Parametric methodology is adapted to model the sparse WLAN data traffic observed in 2.4 GHz Industrial, Scientific and Medical (ISM) band as spectral and temporal activity. The multivariate Gaussian mixture model (MGMM) is proposed in both scenarios where either dependency is considered between the observed WLAN data traffic or assuming that the observed traffic is independent and identically distributed (i.i.d). It is to be validated that in order to have an efficient characterization of sparse WLAN data traffic, the dependency (correlation) between neighbored frequency subbands must be considered. By considering the dependency either between neighbored frequency subbands or neighbored time domain signals actually help to characterize the sparse WLAN data traffic in more realistic way which could not be possible by assuming them i.i.d. Such statistics to characterize sparse data traffic really help as user activity for efficient allocation and utilization of radio resources in CR enabled coexistence scenarios of wireless technologies where WLAN acitivity is considered nominal either as secondary user or classify it grey spectrum as primary user. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

Automated summary

The CR-enabled radio environment provides a seamless operating framework to meet the requirements of next-generation wireless systems. Traffic characterization studies help optimize the coexistence framework, and the multivariate Gaussian mixture model (MGMM) is proposed in both scenarios for sparse WLAN data traffic.