Research on Spectrum Hole Distribution for Cognitive Radio Systems

In this paper, we at first studied the distribution of the interval between two consecutive packets (ITCP) for two-user networks. We then extend our interest to multi-user networks by using fitting curves to approximate the distribution of the ITCP. Next, based on the above observation, we present a new spectrum hole prediction model for the IEEE 802.11 standards based cognitive radio (CR) local areas networks. The effectiveness of the presented prediction model was thereafter validated by using NS-2 and MATLAB simulation programs. Simulation results showed that when adopting the proposed spectrum hole prediction model, CR systems can largely reduce the number of collisions with licensed users.

[1]  Seyed Alireza Zekavat,et al.  Traffic Pattern Prediction and Performance Investigation for Cognitive Radio Systems , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[2]  Hüseyin Arslan,et al.  Binary Time Series Approach to Spectrum Prediction for Cognitive Radio , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[3]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks , 2007, IEEE Transactions on Wireless Communications.

[4]  Akira Miura,et al.  A proposal for a mobile communication traffic forecasting method using time-series analysis for multi-variate data , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[5]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks , 2007, IEEE Transactions on Wireless Communications.

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

[7]  Tao Jiang,et al.  A Subcarriers Allocation Scheme for Cognitive Radio Systems Based on Multi-Carrier Modulation , 2008, IEEE Transactions on Wireless Communications.

[8]  Hao Chen,et al.  Trunked radio systems: traffic prediction based on user clusters , 2004, 1st International Symposium onWireless Communication Systems, 2004..

[9]  Kang G. Shin,et al.  Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks , 2008, IEEE Transactions on Mobile Computing.

[10]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[11]  R.W. Brodersen,et al.  Implementation issues in spectrum sensing for cognitive radios , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[12]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[13]  Haipeng Shen,et al.  Short-Term Traffic Forecasting in a Campus-Wide Wireless Network , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[15]  Mohamed-Slim Alouini,et al.  On the Energy Detection of Unknown Signals Over Fading Channels , 2007, IEEE Transactions on Communications.

[16]  P. Moungnoul,et al.  GSM Traffic Forecast by Combining Forecasting Technique , 2005, 2005 5th International Conference on Information Communications & Signal Processing.

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