586 research outputs found
Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential enabling functionality for cognitive radio
networks to detect spectrum holes and opportunistically use the under-utilized
frequency bands without causing harmful interference to legacy networks. This
paper introduces a novel wideband spectrum sensing technique, called multiband
joint detection, which jointly detects the signal energy levels over multiple
frequency bands rather than consider one band at a time. The proposed strategy
is efficient in improving the dynamic spectrum utilization and reducing
interference to the primary users. The spectrum sensing problem is formulated
as a class of optimization problems in interference limited cognitive radio
networks. By exploiting the hidden convexity in the seemingly non-convex
problem formulations, optimal solutions for multiband joint detection are
obtained under practical conditions. Simulation results show that the proposed
spectrum sensing schemes can considerably improve the system performance. This
paper establishes important principles for the design of wideband spectrum
sensing algorithms in cognitive radio networks
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
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 PUsÂż 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.; MartĂnez 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.LĂłpez-BenĂtez 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
Spatial-Spectral Joint Detection for Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential functionality that enables cognitive radios
to detect spectral holes and opportunistically use under-utilized frequency
bands without causing harmful interference to primary networks. Since
individual cognitive radios might not be able to reliably detect weak primary
signals due to channel fading/shadowing, this paper proposes a cooperative
wideband spectrum sensing scheme, referred to as spatial-spectral joint
detection, which is based on a linear combination of the local statistics from
spatially distributed multiple cognitive radios. The cooperative sensing
problem is formulated into an optimization problem, for which suboptimal but
efficient solutions can be obtained through mathematical transformation under
practical conditions.Comment: To appear in the Proceedings of the 2008 IEEE International
Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, March
30-April 4, 200
Peak to average power ratio based spatial spectrum sensing for cognitive radio systems
The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval
Analytical Studies of Fragmented-Spectrum Multi-Level OFDM-CDMA Technique in Cognitive Radio Networks
In this paper, we present a multi-user resource allocation framework using
fragmented-spectrum synchronous OFDM-CDMA modulation over a frequency-selective
fading channel. In particular, given pre-existing communications in the
spectrum where the system is operating, a channel sensing and estimation method
is used to obtain information of subcarrier availability. Given this
information, some real-valued multi-level orthogonal codes, which are
orthogonal codes with values of , are provided
for emerging new users, i.e., cognitive radio users. Additionally, we have
obtained a closed form expression for bit error rate of cognitive radio
receivers in terms of detection probability of primary users, CR users' sensing
time and CR users' signal to noise ratio. Moreover, simulation results obtained
in this paper indicate the precision with which the analytical results have
been obtained in modeling the aforementioned system.Comment: 6 pages and 3 figure
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