18,824 research outputs found

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

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    [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

    Dynamic Spectrum Allocation and Sharing in Cognitive Cooperative Networks

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    The dramatic increase of service quality and channel capacity in wireless networks is severely limited by the scarcity of energy and bandwidth, which are the two fundamental resources for communications. New communications and networking paradigms such as cooperative communication and cognitive radio networks emerged in recent years that can intelligently and efficiently utilize these scarce resources. With the development of these new techniques, how to design efficient spectrum allocation and sharing schemes becomes very important, due to the challenges brought by the new techniques. In this dissertation we have investigated several critical issues in spectrum allocation and sharing and address these challenges. Due to limited network resources in a multiuser radio environment, a particular user may try to exploit the resources for self-enrichment, which in turn may prompt other users to behave the same way. In addition, cognitive users are able to make intelligent decisions on spectrum usage and communication parameters based on the sensed spectrum dynamics and other users' decisions. Thus, it is important to analyze the intelligent behavior and complicated interactions of cognitive users via game-theoretic approaches. Moreover, the radio environment is highly dynamic, subject to shadowing/fading, user mobility in space/frequency domains, traffic variations, and etc. Such dynamics brings a lot of overhead when users try to optimize system performance through information exchange in real-time. Hence, statistical modeling of spectrum variations becomes essential in order to achieve near-optimal solutions on average. In this dissertation, we first study a stochastic modeling approach for dynamic spectrum access. Since the radio spectrum environment is highly dynamic, we model the traffic variations in dynamic spectrum access using continuous-time Markov chains that characterizes future traffic patterns, and optimize access probabilities to reduce performance degradation due to co-channel interference. Second, we propose an evolutionary game framework for cooperative spectrum sensing with selfish users, and develop the optimal collaboration strategy that has better performance than fully cooperating strategy. Further, we study user cooperation enforcement for cooperative networks with selfish users. We model the optimal relay selection and power control problem as a Stackelberg game, and consider the joint benefits of source nodes as buyers and relay nodes as sellers. The proposed scheme achieves the same performance compared to traditional centralized optimization while reducing the signaling overhead. Finally, we investigate possible attacks on cooperative spectrum sensing under the evolutionary sensing game framework, and analyze their damage both theoretically and by simulations

    Spectrum sharing models in cognitive radio networks

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    Spectrum scarcity demands thinking new ways to manage the distribution of radio frequency bands so that its use is more effective. The emerging technology that can enable this paradigm shift is the cognitive radio. Different models for organizing and managing cognitive radios have emerged, all with specific strategic purposes. In this article we review the allocation spectrum patterns of cognitive radio networks and analyse which are the common basis of each model.We expose the vulnerabilities and open challenges that still threaten the adoption and exploitation of cognitive radios for open civil networks.L'escassetat de demandes d'espectre fan pensar en noves formes de gestionar la distribució de les bandes de freqüència de ràdio perquè el seu ús sigui més efectiu. La tecnologia emergent que pot permetre aquest canvi de paradigma és la ràdio cognitiva. Han sorgit diferents models d'organització i gestió de les ràdios cognitives, tots amb determinats fins estratègics. En aquest article es revisen els patrons d'assignació de l'espectre de les xarxes de ràdio cognitiva i s'analitzen quals són la base comuna de cada model. S'exposen les vulnerabilitats i els desafiaments oberts que segueixen amenaçant l'adopció i l'explotació de les ràdios cognitives per obrir les xarxes civils.La escasez de demandas de espectro hacen pensar en nuevas formas de gestionar la distribución de las bandas de frecuencia de radio para que su uso sea más efectivo. La tecnología emergente que puede permitir este cambio de paradigma es la radio cognitiva. Han surgido diferentes modelos de organización y gestión de las radios cognitivas, todos con determinados fines estratégicos. En este artículo se revisan los patrones de asignación del espectro de las redes de radio cognitiva y se analizan cuales son la base común de cada modelo. Se exponen las vulnerabilidades y los desafíos abiertos que siguen amenazando la adopción y la explotación de las radios cognitivas para abrir las redes civiles

    Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

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    Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201
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