Joint Optimization of Detection Threshold and Resource Allocation in Infrastructure-based Multi-band Cognitive Radio Networks

[EN] Consider an infrastructure-based multi-band cognitive radio network (CRN) where secondary users (SUs) opportunistically access a set of sub-carriers when sensed as idle. The carrier sensing threshold which affects the access opportunities of SUs is conventionally regarded as static and treated independently from the resource allocation in the model. In this article, we study jointly the optimization of detection threshold and resource allocation with the goal of maximizing the total downlink capacity of SUs in such CRNs. The optimization problem is formulated considering three sets of variables, i.e., detection threshold, sub-carrier assignment and power allocation, with constraints on the PUs¿ rate loss and the power budget of the CR base station. Two schemes, referred to as offline and online algorithms respectively, are proposed to solve the optimization problem. While the offline algorithm finds the global optimal solution with high complexity, the online algorithm provides a close-to-optimal solution with much lower complexity and realtime capability. The performance of the proposed schemes is evaluated by extensive simulations and compared with the conventional static threshold selection algorithm specified in the IEEE 802.22 standard.This work is supported by the EU FP7 S2EuNet project (247083), the National Nature Science Foundation of China (NSF61121001), Program for New Century Excellent Talents in University (NCET) and the Spanish Ministry of Education and Science under project (TIN2008-06739-C04-02).Shi, C.; Wang, Y.; Wang, T.; Zhang, P.; Martínez Bauset, J.; Li, FY. (2012). Joint Optimization of Detection Threshold and Resource Allocation in Infrastructure-based Multi-band Cognitive Radio Networks. EURASIP Journal on Wireless Communications and Networking. 2012(334):1-16. https://doi.org/10.1186/1687-1499-2012-334S1162012334Wang B, Liu K: Advances in cognitive radio networks: a survey. IEEE J. Sel. Top. Signal Process 2011, 5: 5-23.Akyildiz I, Lee W, Vuran M, Mohanty S: Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 2006, 50(13):2127-2159. 10.1016/j.comnet.2006.05.001Haykin S: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun 2005, 23(2):201-220.Zhao Q, Sadler B: A survey of dynamic spectrum access. IEEE Signal Process. Mag 2007, 24(3):79-89.Nguyen M, Lee H: Effective scheduling in infrastructure-based cognitive radio network. IEEE Trans. Mobile Comput 2011, 10(6):853-867.Almalfouh S, Stuber G: Interference-aware radio resource allocation in OFDMA-based cognitive radio networks. IEEE Trans. Veh. Technol 2011, 60(4):1699-1713.Kang X, Liang Y, Nallanathan A, Garg H, Zhang R: Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans. Wirel. Commun 2009, 8(2):940-950.Bansal G, Hossain M, Bhargava V: Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems. IEEE Trans. Wirel. Commun 2008, 7(11):4710-4718.Yucek T, Arslan H: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor 2009, 11: 116-130.Cordeiro C, Ghosh M, Cavalcanti D, Challapali K: Spectrum sensing for dynamic spectrum access of TV bands. In Proceedings of the 2nd Cognitive Radio Oriented Wireless Networks and Communications (CrownCom’07). (Orlando, FL, USA, 1–3 Aug 2007);Chong J, Sung D, Sung Y: Cross-layer performance analysis for CSMA/CA protocols: impact of imperfect sensing. IEEE Trans. Veh. Technol 2010, 59(3):1100-1108.Seol D, Lim H, Im G: Cooperative spectrum sensing with dynamic threshold adaptation. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM’09). Honolulu, HI, USA; 1.Liang Y, Zeng Y, Peh E, Hoang A: Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wirel. Commun 2008, 7(4):1326-1337.Kang X, Liang Y, Garg H, Zhang L: Sensing-based spectrum sharing in cognitive radio networks. IEEE Trans. Veh. Technol 2009, 58(8):4649-4654.Choi H, Jang K, Cheong Y: Adaptive sensing threshold control based on transmission power in cognitive radio systems. In Proceedings of the 3rd Cognitive Radio Oriented Wireless Networks and Communications (CrownCom’08). (Singapore, 15–17 May 2008), pp.1–6Gorcin A, Qaraqe K, Celebi H, Arslan H: An adaptive threshold method for spectrum sensing in multi-channel cognitive radio networks. In Proceedings of the IEEE International Conference on Telecommunications (ICT’10). Doha, Qatar; 4.Foukalas F, Mathiopoulos P, Karetsos G: Joint optimal power allocation and sensing threshold selection for SU’s capacity maximisation in SS CRN. Electron. Lett 2010, 46(20):1406-1407. 10.1049/el.2010.1355Jia P, Vu M, Le-Ngoc T, Hong S, Tarokh V: Capacity-and bayesian-based cognitive sensing with location side information. IEEE J. Sel. Areas Commun 2011, 29(2):276-289.Wang R, Lau V, Lv L, Chen B: Joint cross-layer scheduling and spectrum sensing for OFDMA cognitive radio systems. IEEE Trans. Wirel. Commun 2009, 8(5):2410-2416.Kang X, Garg H, Liang Y, Zhang R: Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria. IEEE Trans. Wirel. Commun 2010, 9(6):2066-2075.Quan Z, Cui S, Sayed A, Poor H: Optimal multiband joint detection for spectrum sensing in cognitive radio networks. IEEE Trans. Signal Process 2009, 57(3):1128-1140.López-Benítez M, Casadevall F: An overview of spectrum occupancy models for cognitive radio networks. In International IFIP TC 6 Workshops: PE-CRN, NC-Pro, WCNS , and SUNSET. Valencia, Spain; 13 May 2011.Pla V, Vidal J, Martinez-Bause J, Guijarro L: Modeling and characterization of spectrum white spaces for underlay cognitive radio networks. In Proceedings of IEEE International Conference on Communications (ICC’10). Cape Town, South Africa; 23.Yu W, Lui R: Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Trans. Commun 2006, 54(7):1310-1322.Boyd S, Vandenberghe L: Convex Optimization. Cambridge University Press, Cambridge; 2004.Jang J, Lee K: Transmit power adaptation for multiuser OFDM systems. IEEE J. Sel. Areas Commun 2003, 21(2):171-178. 10.1109/JSAC.2002.807348Luenberger D, Ye Y: Linear and Nonlinear Programming. Springer Verlag, Stanford; 2008.Barbarossa S, Sardellitti S, Scutari G: Joint optimization of detection thresholds and power allocation for opportunistic access in multicarrier cognitive radio networks. In Proceedings of 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP’09). Aruba, Netherlands; 13

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