A Neural Network based cognitive engine for IEEE 802.11 WLAN Access Point selection

Nowadays IEEE 802.11 WLANs are widely deployed; in spite of this, the issue of designing an efficient and practical Access Point selection schemes that can provide the best throughput performance in a variety of link conditions is still open. In this paper we present a Cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and a cognitive engine based on a Neural Network trained on this data drives the AP selection process. Our performance evaluation shows that the proposed scheme has very good performance in a variety of scenarios, as opposed to other algorithms previously proposed in the literature which perform well only in specific cases and cannot address the non-idealities typical under real conditions.

[1]  Fabio Panzieri,et al.  A strategy for best access point selection , 2010, 2010 IFIP Wireless Days.

[2]  Christian Igel,et al.  Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.

[3]  Klaus Wehrle,et al.  A Performance Comparison of Recent Network Simulators , 2009, 2009 IEEE International Conference on Communications.

[4]  Jyh-Cheng Chen,et al.  WLC19-4: Effective AP Selection and Load Balancing in IEEE 802.11 Wireless LANs , 2006, IEEE Globecom 2006.

[5]  Christian Igel,et al.  Improving the Rprop Learning Algorithm , 2000 .

[6]  George F. Riley,et al.  The ns-3 Network Simulator , 2010, Modeling and Tools for Network Simulation.

[7]  Nj Piscataway,et al.  Wireless LAN medium access control (MAC) and physical layer (PHY) specifications , 1996 .

[8]  Chong-kwon Kim,et al.  Traffic-aware decentralized AP selection for multi-rate in WLANs , 2010, 2010 The 12th International Conference on Advanced Communication Technology (ICACT).

[9]  Li-Hsing Yen,et al.  SNMP-Based Approach to Load Distribution in IEEE 802.11 Networks , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[10]  Yuji Oie,et al.  Decentralized access point selection architecture for wireless LANs , 2007, 2004 Symposium on Wireless Telecommunications.

[11]  Voon Chin Phua,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1999 .

[12]  Paramvir Bahl,et al.  A rate-adaptive MAC protocol for multi-Hop wireless networks , 2001, MobiCom '01.

[13]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[14]  Jyh-Cheng Chen,et al.  Effective AP Selection and Load Balancing in IEEE 802.11 Wireless LANs , 2006 .