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Power Control and Resource Allocation for QoS-Constrained Wireless Networks
Developments such as machine-to-machine communications and multimedia services are placing growing demands on high-speed reliable transmissions and limited wireless spectrum resources. Although multiple-input multiple-output (MIMO) systems have shown the ability to provide reliable transmissions in fading channels, it is not practical for single-antenna devices to support MIMO system due to cost and hardware limitations. Cooperative communication allows single-antenna devices to share their spectrum resources and form a virtual MIMO system where their quality of service (QoS) may be improved via cooperation. Most cooperative communication solutions are based on fixed spectrum access schemes and thus cannot further improve spectrum efficiency. In order to support more users in the existing spectrum, we consider dynamic spectrum access schemes and cognitive radio techniques in this dissertation.
Our work includes the modelling, characterization and optimization of QoS-constrained cooperative networks and cognitive radio networks. QoS constraints such as delay and data rate are modelled. To solve power control and channel resource allocation problems, dynamic power control, matching theory and multi-armed bandit algorithms are employed in our investigations. In this dissertation, we first consider a cluster-based cooperative wireless network utilizing a centralized cooperation model. The dynamic power control and optimization problem is analyzed in this scenario. We then consider a cooperative cognitive radio network utilizing an opportunistic spectrum access model. Distributed spectrum access algorithms are proposed to help secondary users utilize vacant channels of primary users in order to optimize the total utility of the network. Finally, a noncooperative cognitive radio network utilizing the opportunistic spectrum access model is analyzed. In this model, primary users do not communicate with secondary users. Therefore, secondary users are required to find vacant channels on which to transmit. Multi-armed bandit algorithms are proposed to help secondary users predict the availability of licensed channels.
In summary, in this dissertation we consider both cooperative communication networks and cognitive radio networks with QoS constraints. Efficient power control and channel resource allocation schemes have been proposed for optimization problems in different scenarios.Cambridge Overseas Trust; China Scholarship Counci
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
Dynamic Spectrum Allocation and Sharing in Cognitive Cooperative Networks
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
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
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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