Analysis of mobility impact on interference in cognitive radio networks

Abstract Cognitive radio (CR) technology seems to be a promising candidate for solving the radio frequency (RF) spectrum occupancy problem. CRs strive to utilize the white holes in the RF spectrum in an opportunistic manner. Because interference is an inherent and a very critical design parameter for all sorts of wireless communication systems, many of the recently emerging wireless technologies prefer smaller size coverage with reduced transmit power in order to decrease interference. Prominent examples of short-range communication systems trying to achieve low interference power levels are CR relays in CR networks and femtocells in next generation wireless networks (NGWNs). It is clear that a comprehensive interference model including mobility is essential especially in elaborating the performance of such short-range communication scenarios. Therefore, in this study, a physical layer interference model in a mobile radio communication environment is investigated by taking into account all of the basic propagation mechanisms such as large- and small-scale fading under a generic single primary user (PU) and single secondary user (SU) scenario. Both one-dimensional (1D) and two-dimensional (2D) random walk models are incorporated into the physical layer signal model. The analysis and corresponding numerical results are given along with the relevant discussions.

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

[2]  Holger Claussen,et al.  An overview of the femtocell concept , 2008, Bell Labs Technical Journal.

[3]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[4]  A. O. Walker British Fruit Growing , 1905, Nature.

[5]  B.L. Cragin Prediction of Seasonal Trends in Cellular Dropped Call Probability , 2006, 2006 IEEE International Conference on Electro/Information Technology.

[6]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[7]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[8]  Tommy Svensson,et al.  Towards Systems Beyond 3G Based on Adaptive OFDMA Transmission , 2007, Proceedings of the IEEE.

[9]  Mark R. Bell,et al.  Statistics of the scattering cross-section of a small number of random scatterers , 1995 .

[10]  J. C. Kluyver,et al.  A local probability problem , 1905 .

[11]  David Choi,et al.  Dealing with Loud Neighbors: The Benefits and Tradeoffs of Adaptive Femtocell Access , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[12]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[13]  Georgios B. Giannakis,et al.  A two-dimensional channel simulation model for shadowing processes , 2003, IEEE Trans. Veh. Technol..

[14]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

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

[16]  V. Tarokh,et al.  Cognitive radio networks , 2008, IEEE Signal Processing Magazine.

[17]  Ismail Güvenç,et al.  A hybrid frequency assignment for femtocells and coverage area analysis for co-channel operation , 2008, IEEE Communications Letters.

[18]  Geoffrey Ye Li,et al.  Cognitive radio networking and communications: an overview , 2011, IEEE Transactions on Vehicular Technology.

[19]  Shaowei Wang Cognitive radio networks , 2009, IEEE Vehicular Technology Magazine.

[20]  Elvino S. Sousa,et al.  Cognitive uplink interference management in 4G cellular femtocells , 2010, 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[21]  Rayleigh The Problem of the Random Walk , 1905, Nature.

[22]  David J. Skellern,et al.  An Integrated Propagation-Mobility Interference Model for Microcell Network Coverage Prediction , 1997, Wirel. Pers. Commun..

[23]  Hirofumi Suzwi,et al.  A Statistical Model for Urban Radio Propagation , 1977 .

[24]  H. Suzuki,et al.  A Statistical Model for Urban Radio Propogation , 1977, IEEE Trans. Commun..

[25]  Gordon L. Stüber Principles of mobile communication , 1996 .

[26]  Hüseyin Arslan,et al.  Impact of Mobility on the Behavior of Interference in Cellular Wireless Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[27]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[28]  M. Gudmundson Analysis of handover algorithms (microcellular radio) , 1991, [1991 Proceedings] 41st IEEE Vehicular Technology Conference.

[29]  Junshan Zhang,et al.  MIMO-Pipe Modeling and Scheduling for Efficient Interference Management in Multihop MIMO Networks , 2010, IEEE Transactions on Vehicular Technology.

[30]  Takuro Sato,et al.  Cognitive interference management in 3G femtocells , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[31]  A. Sheikh,et al.  Investigations into cochannel interference in microcellular mobile radio systems , 1992 .

[32]  Erchin Serpedin,et al.  On the Evolution of Interference in Time for Cellular Mobile Radio Networks , 2011, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN).

[33]  Hüseyin Arslan,et al.  Exploiting location awareness toward improved wireless system design in cognitive radio , 2008, IEEE Communications Magazine.

[34]  Hüseyin Arslan,et al.  Real-Time Measurements for Adaptive and Cognitive Radio Systems , 2009, EURASIP J. Wirel. Commun. Netw..

[35]  Mahmoud Naghshineh,et al.  Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey , 2000, IEEE Communications Surveys & Tutorials.