COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space

Dynamic spectrum access is a promising approach to alleviate the spectrum scarcity that wireless communications face today. In short, it aims at reusing sparsely occupied frequency bands while causing no (or insignificant) interference to the actual licensees. This article focuses on applying this concept in the time domain by exploiting idle periods between bursty transmissions of multi-access communication channels and addresses WLAN as an example of practical importance. A statistical model based on empirical data is presented, and it is shown how to use this model for deriving access strategies. The coexistence of Bluetooth and WLAN is considered as a concrete example

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

[2]  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.

[3]  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..

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

[5]  Panagiotis Papadimitratos,et al.  A bandwidth sharing approach to improve licensed spectrum utilization , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

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

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

[8]  G. F. Gott,et al.  Development of the Laycock-Gott occupancy model , 1997 .

[9]  Friedrich Jondral,et al.  Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency , 2004, IEEE Communications Magazine.

[10]  Anant Sahai,et al.  Fundamental tradeoffs in robust spectrum sensing for opportunistic frequency reuse , 2006 .

[11]  Peter Buchholz,et al.  A novel approach for fitting probability distributions to real trace data with the EM algorithm , 2005, 2005 International Conference on Dependable Systems and Networks (DSN'05).

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

[13]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .