Cognitive Medium Access: Constraining Interference Based on Experimental Models

In this paper we design a cognitive radio that can coexist with multiple parallel WLAN channels while abiding by an interference constraint. The interaction between both systems is characterized by measurement and coexistence is enhanced by predicting the WLAN's behavior based on a continuous-time Markov chain model. Cognitive medium access (CMA) is derived from this model by recasting the problem as one of constrained Markov decision processes. Solutions are obtained by linear programming. Furthermore, we show that optimal CMA admits structured solutions, simplifying practical implementations. Preliminary results for the partially observable case are presented. The performance of the proposed schemes is evaluated for a typical WLAN coexistence setup and shows a significant performance improvement.

[1]  Brian M. Sadler,et al.  Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy , 2006, ArXiv.

[2]  Brian M. Sadler,et al.  Measurement-Based Models for Cognitive Medium Access in WLAN Bands , 2007 .

[3]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[4]  Brian M. Sadler,et al.  Opportunistic Spectrum Access via Periodic Channel Sensing , 2008, IEEE Transactions on Signal Processing.

[5]  Lang Tong,et al.  A Measurement-Based Model for Dynamic Spectrum Access in WLAN Channels , 2006, MILCOM 2006 - 2006 IEEE Military Communications conference.

[6]  Brian M. Sadler,et al.  COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space , 2007, IEEE Communications Magazine.

[7]  Paula Fikkert,et al.  Specification of the Bluetooth System , 2003 .

[8]  Q. Zhao,et al.  Decentralized cognitive mac for dynamic spectrum access , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  Nada Golmie,et al.  Bluetooth and WLAN coexistence: challenges and solutions , 2003, IEEE Wireless Communications.

[10]  Brian M. Sadler,et al.  Dynamic Spectrum Access in the Time Domain : Modeling and Exploiting Whitespace , 2007 .

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

[12]  Nada Golmie,et al.  Interference Evaluation of Bluetooth and IEEE 802.11b Systems , 2003, Wirel. Networks.

[13]  Ramesh R. Rao,et al.  Coexistence mechanisms for interference mitigation in the 2.4-GHz ISM band , 2003, IEEE Trans. Wirel. Commun..

[14]  A. M. Abdullah,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1997 .

[15]  Brian M. Sadler,et al.  Optimal Dynamic Spectrum Access via Periodic Channel Sensing , 2007, 2007 IEEE Wireless Communications and Networking Conference.

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

[17]  E. Altman Constrained Markov Decision Processes , 1999 .

[18]  J. Wendelberger Adventures in Stochastic Processes , 1993 .

[19]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[20]  Ananthram Swami,et al.  Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework , 2007, IEEE Journal on Selected Areas in Communications.

[21]  R. Tandra,et al.  Fundamental limits on detection in low SNR under noise uncertainty , 2005, 2005 International Conference on Wireless Networks, Communications and Mobile Computing.

[22]  Brian M. Sadler,et al.  Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis , 2006, TAPAS '06.