Analysis of Inversely Proportional Carrier Sense Threshold and Transmission Power Setting Based on Received Power for IEEE 802.11ax

In this study, we conducted an analysis of the system performance of a wireless local area network in which access points (APs) dynamically adjust the carrier sense threshold (CST) based on the individual average received power to determine the optimal CST. Adjustment of the CST is a promising approach to improve spatial reuse and proposed for IEEE 802.11ax standard. Here, assuming to adopt the inversely proportional setting of the CST and transmission power, we can make the carrier sensing relationship symmetric, restraining throughput starvation. This paper analytically derives the density of successful transmissions (DST) on the basis of stochastic geometry. The DST is a system performance metric which expresses the number of APs whose transmission is successful based on signal-to-interference-plus-noise power ratio. We show that both results of the analytically derived DST and Monte Carlo simulation have the same trend. From the perspective of the derived DST, the optimal parameter setting is also discussed.

[1]  Masahiro Morikura,et al.  Analysis of inversely proportional carrier sense threshold and transmission power setting , 2017, 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[2]  Nitin H. Vaidya,et al.  On physical carrier sensing in wireless ad hoc networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[3]  Masahiro Morikura,et al.  Inversely Proportional Transmission Power and Carrier Sense Threshold Setting for WLANs: Experimental Evaluation of Partial Settings , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[4]  Konstantina Papagiannaki,et al.  Interference Mitigation Through Power Control in High Density 802.11 WLANs , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[5]  Ekram Hossain,et al.  A Modified Hard Core Point Process for Analysis of Random CSMA Wireless Networks in General Fading Environments , 2013, IEEE Transactions on Communications.

[6]  Daniel Camps-Mur,et al.  Evaluation of dynamic sensitivity control algorithm for IEEE 802.11ax , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[7]  Ekram Hossain,et al.  On Stochastic Geometry Modeling of Cellular Uplink Transmission With Truncated Channel Inversion Power Control , 2014, IEEE Transactions on Wireless Communications.

[8]  François Baccelli,et al.  Stochastic Geometry and Wireless Networks, Volume 1: Theory , 2009, Found. Trends Netw..

[9]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[10]  Riku Jantti,et al.  Interference control in cognitive wireless networks by tuning the carrier sensing threshold , 2013 .

[11]  Takayuki Nishio,et al.  Attenuators enable inversely proportional transmission power and carrier sense threshold setting in WLANs , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[12]  Jeffrey G. Andrews,et al.  Modeling and Analyzing the Coexistence of Wi-Fi and LTE in Unlicensed Spectrum , 2015, IEEE Transactions on Wireless Communications.

[13]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

[14]  François Baccelli,et al.  Stochastic geometry and wireless networks , 2009 .

[15]  Daniel Camps-Mur,et al.  Dynamic sensitivity control of access points for IEEE 802.11ax , 2016, 2016 IEEE International Conference on Communications (ICC).