Analysis Framework for Opportunistic Spectrum OFDMA and Its Application to the IEEE 802.22 Standard

We present an analytical model that enables the evaluation of opportunistic spectrum orthogonal frequency division multiple-access (OS-OFDMA) networks using metrics such as blocking probability or, most importantly, throughput. The core feature of the model, based on a discrete-time Markov chain, is the consideration of different channel and subchannel allocation strategies under different primary and secondary user types, traffic, and priority levels. The analytical model also assesses the impact of different spectrum sensing strategies on the throughput of OS-OFDMA network. In addition, we consider studies of cochannel interference. The analysis is applied to the IEEE 802.22 standard to evaluate the impact of the two-stage spectrum sensing strategy and the varying temporal activity of wireless microphones on the system throughput. In addition to the analytical model, we present a set of comprehensive simulation results using NS-2 related to the delay performance of the OS-OFDMA system considered. Our study suggests that OS-OFDMA with subchannel notching and channel bonding could provide almost ten times higher throughput compared with a design without these options when the activity and density of wireless microphones are very high. Furthermore, we confirm that OS-OFDMA implementation without subchannel notching, which is used in the IEEE 802.22, can support the real-time and non-real-time quality of service classes, provided that the temporal activity of wireless microphones is moderate (with sparse wireless microphone distribution, with light urban population density and short duty cycles). Finally, the two-stage spectrum sensing option improves the OS-OFDMA throughput, provided that the length of spectrum sensing at every stage is optimized using our model.

[1]  Yonghong Zeng,et al.  Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View , 2009, IEEE Transactions on Vehicular Technology.

[2]  Marwan Krunz,et al.  Coexistence Problem in IEEE 802.22 Wireless Regional Area Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[3]  Zhongding Lei,et al.  IEEE 802.22: The first cognitive radio wireless regional area network standard , 2009, IEEE Communications Magazine.

[4]  Ying Wang,et al.  Compressive wide-band spectrum sensing , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Wha Sook Jeon,et al.  An Advanced Quiet-Period Management Scheme for Cognitive Radio Systems , 2010, IEEE Transactions on Vehicular Technology.

[6]  Danijela Cabric,et al.  Performance of Joint Spectrum Sensing and MAC Algorithms for Multichannel Opportunistic Spectrum Access Ad Hoc Networks , 2009, IEEE Transactions on Mobile Computing.

[7]  Zhou Yuan,et al.  On Sidelobe Suppression for Multicarrier-Based Transmission in Dynamic Spectrum Access Networks , 2010, IEEE Transactions on Vehicular Technology.

[8]  Catherine Rosenberg,et al.  What is the right model for wireless channel interference? , 2006, IEEE Transactions on Wireless Communications.

[9]  Stefan Parkvall,et al.  LTE: the evolution of mobile broadband , 2009, IEEE Communications Magazine.

[10]  Tho Le-Ngoc,et al.  Distributed Resource Allocation for Cognitive Radio Networks With Spectrum-Sharing Constraints , 2011, IEEE Transactions on Vehicular Technology.

[11]  Janne Riihijärvi,et al.  Empirical time and frequency domain models of spectrum use , 2009, Phys. Commun..

[12]  Linda Doyle,et al.  OFDM Pulse-Shaped Waveforms for Dynamic Spectrum Access Networks , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[13]  Tõnu Trump,et al.  An energy detector for spectrum sensing in impulsive noise environment , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[14]  Danijela Cabric,et al.  Performance of Opportunistic Spectrum OFDMA Network with Users of Different Priorities and Traffic Characteristics , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[15]  Adam Wolisz,et al.  COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Frequency Hopping Communities for Efficient IEEE 802.22 Operation , 2007, IEEE Communications Magazine.

[16]  Vamsi Paruchuri,et al.  Broadcast Protocol for Energy-Constrained Networks , 2007, IEEE Transactions on Broadcasting.

[17]  Kyutae Lim,et al.  First Cognitive Radio Networking Standard for Personal/Portable Devices in TV White Spaces , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[18]  Shamik Sengupta,et al.  A Game Theoretic Framework for Distributed Self-Coexistence Among IEEE 802.22 Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

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

[20]  Piet Van Mieghem,et al.  Performance analysis of communications networks and systems , 2006 .

[21]  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..

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

[23]  Danijela Cabric,et al.  Cyclostationary-based low complexity wideband spectrum sensing using compressive sampling , 2012, 2012 IEEE International Conference on Communications (ICC).

[24]  Peng Cheng,et al.  A Distributed Algorithm for Optimal Resource Allocation in Cognitive OFDMA Systems , 2008, 2008 IEEE International Conference on Communications.

[25]  Khaled Ben Letaief,et al.  Spectrum sensing with active cognitive systems , 2010, IEEE Transactions on Wireless Communications.

[26]  Cyril Leung,et al.  Resource allocation for non-real-time services in OFDM-based cognitive radio systems , 2009, IEEE Communications Letters.

[27]  Umberto Spagnolini,et al.  Packet-wise vertical handover for unlicensed multi-standard spectrum access with cognitive radios , 2008, IEEE Transactions on Wireless Communications.

[28]  Ying-Chang Liang,et al.  Cognitive radio on TV bands: a new approach to provide wireless connectivity for rural areas , 2008, IEEE Wireless Communications.

[29]  Kang G. Shin,et al.  What and how much to gain by spectrum agility? , 2007, IEEE Journal on Selected Areas in Communications.

[30]  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.

[31]  Rick S. Blum,et al.  A statistical and physical mechanisms-based interference and noise model for array observations , 2000, IEEE Trans. Signal Process..

[32]  Dave Cavalcanti,et al.  Chapter 14 – Cognitive radio for broadband wireless access in TV bands: The IEEE 802.22 standards , 2010 .

[33]  Danijela Cabric,et al.  Throughput and Collision Analysis of Multichannel Multistage Spectrum Sensing Algorithms , 2010, IEEE Transactions on Vehicular Technology.

[34]  Brian M. Sadler,et al.  A Survey of Dynamic Spectrum Access , 2007, IEEE Signal Processing Magazine.

[35]  Tao Jiang,et al.  Extended Active Interference Cancellation for Sidelobe Suppression in Cognitive Radio OFDM Systems With Cyclic Prefix , 2010, IEEE Transactions on Vehicular Technology.

[36]  Georgios B. Giannakis,et al.  Compressed Sensing for Wideband Cognitive Radios , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[37]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Transactions on Wireless Communications.

[38]  Kwang-Cheng Chen,et al.  Asynchronous Dynamic Spectrum Access , 2012, IEEE Transactions on Vehicular Technology.

[39]  Gordon L. Stüber,et al.  Interference-Aware Radio Resource Allocation in OFDMA-Based Cognitive Radio Networks , 2011, IEEE Transactions on Vehicular Technology.

[40]  Tzi-cker Chiueh,et al.  Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[41]  Brian L. Mark,et al.  A Framework for Cognitive WiMAX With Frequency Agility , 2009, Proceedings of the IEEE.

[42]  Rajarathnam Chandramouli,et al.  Dynamic spectrum access in open spectrum wireless networks , 2006, IEEE Journal on Selected Areas in Communications.

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

[44]  Chunyan Feng,et al.  Research on WRAN system level simulation platform design , 2008, 2008 Third International Conference on Communications and Networking in China.

[45]  Mohamed Kadhem Karray,et al.  Analytical evaluation of QoS in the downlink of OFDMA wireless cellular networks serving streaming and elastic traffic , 2010, IEEE Transactions on Wireless Communications.

[46]  Zhi Ding,et al.  Optimal Sensing-Transmission Structure for Dynamic Spectrum Access , 2009, IEEE INFOCOM 2009.

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

[48]  Hyunwook Kim,et al.  An Effective MIMO–OFDM System for IEEE 802.22 WRAN Channels , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[49]  Hung-Yu Wei,et al.  Game Theoretical Resource Allocation for Inter-BS Coexistence in IEEE 802.22 , 2010, IEEE Transactions on Vehicular Technology.

[50]  G. Staple,et al.  The end of spectrum scarcity [spectrum allocation and utilization] , 2004, IEEE Spectrum.

[51]  Ling Luo,et al.  A Two-Stage Sensing Technique for Dynamic Spectrum Access , 2008, 2008 IEEE International Conference on Communications.

[52]  Zhu Han,et al.  Dynamic spectrum access in IEEE 802.22- based cognitive wireless networks: a game theoretic model for competitive spectrum bidding and pricing , 2009, IEEE Wireless Communications.

[53]  Paal E. Engelstad,et al.  Towards dynamic spectrum access in primary OFDMA systems , 2009, 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications.

[54]  Gwangzeen Ko,et al.  An efficient quiet period management scheme for cognitive radio systems , 2008, IEEE Transactions on Wireless Communications.

[55]  Kang G. Shin,et al.  Asymmetry-Aware Real-Time Distributed Joint Resource Allocation in IEEE 802.22 WRANs , 2010, 2010 Proceedings IEEE INFOCOM.

[56]  R. Chandramouli,et al.  A Coordinated Distributed Scheme for Cognitive Radio Based IEEE 802.22 Wireless Mesh Networks , 2008, ICC Workshops - 2008 IEEE International Conference on Communications Workshops.

[57]  Chunyan Miao,et al.  A game theory approach for self-coexistence analysis among IEEE 802.22 networks , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[58]  Sofie Pollin,et al.  Performance Analysis of Multichannel Medium Access Control Algorithms for Opportunistic Spectrum Access , 2009, IEEE Transactions on Vehicular Technology.

[59]  Loutfi Nuaymi,et al.  Wimax Technology for Broadband Wireless Access , 2007 .

[60]  S.-E. Elayoubi,et al.  Performance Evaluation of Admission Control and Adaptive Modulation in OFDMA WiMax Systems , 2008, IEEE/ACM Transactions on Networking.

[61]  Young-June Choi,et al.  Opportunistic Access of TV Spectrum Using Cognitive-Radio-Enabled Cellular Networks , 2011, IEEE Transactions on Vehicular Technology.

[62]  Tzi-cker Chiueh,et al.  Low-latency mobile IP handoff for infrastructure-mode wireless LANs , 2004, IEEE Journal on Selected Areas in Communications.

[63]  Hou-Shin Chen,et al.  Spectrum Sensing for TV White Space in North America , 2011, IEEE Journal on Selected Areas in Communications.

[64]  Jennifer Widom,et al.  Teletraffic modeling for personal communications services , 1997 .

[65]  Taejoong Song,et al.  A Fully Integrated UHF-Band CMOS Receiver With Multi-Resolution Spectrum Sensing (MRSS) Functionality for IEEE 802.22 Cognitive Radio Applications , 2009, IEEE Journal of Solid-State Circuits.

[66]  David G. Daut,et al.  Spectrum Sensing Using Cyclostationary Properties and Application to IEEE 802.22 WRAN , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[67]  Hüseyin Arslan,et al.  OFDM for cognitive radio: merits and challenges , 2009, IEEE Wireless Communications.

[68]  Dusit Niyato,et al.  A Novel Spectrum-Scheduling Scheme for Multichannel Cognitive Radio Network and Performance Analysis , 2011, IEEE Transactions on Vehicular Technology.

[69]  Yonina C. Eldar,et al.  From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals , 2009, IEEE Journal of Selected Topics in Signal Processing.

[70]  David G. Daut,et al.  Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN , 2007, 2007 IEEE International Conference on Communications.

[71]  Cyril Leung,et al.  A Distributed Algorithm for Resource Allocation in OFDM Cognitive Radio Systems , 2011, IEEE Transactions on Vehicular Technology.

[72]  Yonghong Zeng,et al.  Sensing-Throughput Tradeoff for Cognitive Radio Networks , 2008, IEEE Trans. Wirel. Commun..

[73]  Ha H. Nguyen,et al.  Resource Allocation for OFDMA-Based Cognitive Radio Multicast Networks With Primary User Activity Consideration , 2010, IEEE Transactions on Vehicular Technology.

[74]  Cyril Leung,et al.  Cross-Layer Resource Allocation for Mixed Services in Multiuser OFDM-Based Cognitive Radio Systems , 2009, IEEE Transactions on Vehicular Technology.

[75]  Yonghong Zeng,et al.  Opportunistic spectrum access for energy-constrained cognitive radios , 2008, IEEE Transactions on Wireless Communications.