Explore and Analyze the Performance Factors on Wi-Fi Sensing Starvation Problems

Wi-Fi wireless communication has become a basic service in public areas. But the quality is not stable due to the factors that are influenced by 1) a limited number of channels results in access interference, 2) various transmission ranges, carrier sensing and hidden terminal starvation problems, and 3) the barriers reduce the quality of transmissions. This work aims to explore and analyze the factors to show the level of performance effect on various transmission ranges of access points. Accordingly, this study designs several simulation cases to evaluate whether a small cell size can provide high performance co-existing with a large cell size by controlling the effect of the sensing ranges, transmission ranges, traffic types, data rates, and packet sizes. Network Simulation 3 (NS3) tool is used to implement the simulation cases and compare the results. We discussed our findings on these factors that affect the levels of starvation caused by the various signal ranges.

[1]  Matias Richart,et al.  Self management of rate, power and carrier-sense threshold for interference mitigation in IEEE 802.11 networks , 2014, 10th International Conference on Network and Service Management (CNSM) and Workshop.

[2]  Rong Zheng,et al.  Starvation Modeling and Identification in Dense 802.11 Wireless Community Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[3]  Song Chong,et al.  CSMA-Based Robust AP Throughput Guarantee Under User Distribution Uncertainty , 2015, IEEE/ACM Transactions on Networking.

[4]  Sumit Roy,et al.  Modelling Throughput and Starvation in 802.11 Wireless Networks with Multiple Flows , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[5]  Marco Gruteser,et al.  Symphony: Synchronous Two-Phase Rate and Power Control in 802.11 WLANs , 2008, IEEE/ACM Transactions on Networking.

[6]  Jean-François Hélard,et al.  Improving the capacity of future IEEE 802.11 high efficiency WLANs , 2014, 2014 21st International Conference on Telecommunications (ICT).

[7]  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.

[8]  Chong-kwon Kim,et al.  Practical application of physical energy detection to recognize starvation in 802.11 wireless networks , 2009, 2009 International Conference on Information Networking.

[9]  Liesbet Van der Perre,et al.  A Spatial Learning Algorithm for IEEE 802.11 Networks , 2009, 2009 IEEE International Conference on Communications.

[10]  Vaduvur Bharghavan,et al.  Robust rate adaptation for 802.11 wireless networks , 2006, MobiCom '06.

[11]  Eun Byol Koh,et al.  Mitigating starvation in CSMA-based wireless ad hoc networks using carrier sense threshold , 2007, 2007 15th International Conference on Software, Telecommunications and Computer Networks.

[12]  Vincent W. S. Wong,et al.  Flow Starvation Mitigation for Wireless Mesh Networks , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[13]  Srinivasan Seshan,et al.  Interference-Aware Transmission Power Control for Dense Wireless Networks , 2007 .