IEEE Journal on Selected Areas in Communications, Special Issue on “Cognitive Radio: Theory and Application”, vol. 26, no. 1, January 2008.

Proceedings of the IEEE, Special Issue on “Cognitive Radio - I”, vol. 97, no. 4, April 2009

Proceedings of the IEEE, Special Issue on “Cognitive Radio - II”, vol. 97, no. 5, May 2009

IEEE Journal on Selected Areas in Communications, Special Issue on “Advances in Cognitive Radio Networking and Communications - I”, vol. 29, no. 2, February 2011.

IEEE Journal on Selected Areas in Communications, Special Issue on “Advances in Cognitive Radio Networking and Communications - II”, vol. 29, no. 4, April 2011.

IEEE Transactions on Vehicular Technology, Special issue on the "Achievements and the Road Ahead: The First Decade of Cognitive Radio", vol. 59, no. 4, May 2010.

IEEE Journal of Selected Topics in Signal Processing,Special Issue on “Signal Processing in Cognitive Radio”, vol. 5, no. 1, January 2011.

IEEE Wireless Communications Magazine, Special Issue on “Cognitive Radio Networks: A Practical Perspective”, August 2012.

IEEE Vehicular Technology Magazine, Special Issue on“Applications of Cognitive Radio Networks”, June 2012.

EURASIP Journal on Wireless Communications and Networking, Special Issue on “Ten Years of Cognitive Radio: State of the Art and Perspectives”, November 2011.

EURASIP Journal on Advances in Signal Processing, Special Issue on “Dynamic Spectrum Access for Wireless Networking”, August 2009.

EURASIP Journal on Advances in Signal Processing, Special Issue on “Advanced Signal Processing for Cognitive Radio Networks”, 2010.

Elsevier Computer Communications, Special Issue on “Cognitive Radio and Dynamic Spectrum Sharing Systems”, vol. 32, no. 18, December 2009.

S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
This is a seminal paper, which for the first time discusses the basic cognitive radio functionalities from communications, signal processing, and networking point of view and introduces methods for radio scene analysis, channel state and interference-temperature estimation, and power control for cognitive radios.

J. Mitola III and G. Q. Maguire Jr., “Cognitive radio: Making software radios more personal," IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, August 1999.
This is the pioneering paper which introduces the concept of cognitive radio and discusses its relationship with software defined radio – also introduces the idea of using Radio Knowledge Representation Language (RKRL) to implement cognitive and software defined radios.

I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks (Elsevier), vol. 50, no. 13, pp. 2127-2159, September 2006.
This is an extensive survey on the architecture and protocol design issues for cognitive radio networks or dynamic spectrum access (DSA) networks, also referred to as NeXt Generation (xG) networks.

Q. Zhao and B. M. Sadler, “A survey of dynamic spectrum access,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, May 2007. 
This is one of the early tutorial papers on cognitive radio, which provides a summary of technical and regulatory challenges in dynamic spectrum access.

Bruce A. Fette, Cognitive Radio Technology (2nd Edition), Academic Press, ISBN: 978-0123745354, 2009.
This book provides a comprehensive coverage on the basic concepts, implementation details, standards, policy issues on cognitive radio technology.

B. Wang and K.J.R. Liu, “Advances in cognitive radio networks: A survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5-23, 2011.
The paper provides an updated survey on cognitive radio networks.

E. Hossain, D. Niyato, and Z. Han, Dynamic Spectrum Access and Management in Cognitive Radio Networks, Cambridge University Press, 2009.
This book provides an all-inclusive introduction to this emerging technology, outlining the fundamentals of cognitive radio-based wireless communication and networking, spectrum sharing models, and the requirements for dynamic spectrum access. This is a perfect introduction for graduate students and researchers, as well as a useful self-study guide for practitioners.

A. Goldsmith, S. A. Jafar, I. Maric, and S. Srinivasa, “Breaking spectrum gridlock with cognitive radios: An information theoretic perspective,”Proceedings of the IEEE, vol. 97, no. 5, pp. 894-914, May 2009.
This is an excellent survey on the information-theoretic capacity results, related bounds, and the degrees of freedom for different cognitive radio network design paradigms (e.g., underlay, overlay, and interweave paradigms).

N. Devroye, P. Mitran, and V. Tarokh, “Achievable rates in cognitive radio channels,”IEEE Transactions on Information Theory, vol. 52, no. 5, pp. 1813-1827, May 2006. 
This is a premier work on the information-theoretic analysis of achievable rate region for a two-sender and two-receiver cognitive radio interference channel.

A. Jovicic and P.Viswanath, “Cognitive radio: An information-theoretic perspective,” IEEE Transactions on Information Theory, vol. 55, no. 9, pp. 3945-3958, September 2009.
This is another important work, which analyzes the rate capacity of cognitive radio-based communications under coexistence conditions where a cognitive radio causes to rate degradation for the primary user communication with single-user decoder.

M. Gastpar, “On capacity under receive and spatial spectrum-sharing constraints,”IEEE Transactions on Information Theory, vol. 53, no. 2, pp. 471-487, February 2007.
This paper takes a different approach in capacity analysis for wireless multiple access and relay networks under received power constraints and geometric spectrum-sharing constraints. The capacity analysis considers cooperation, feedback and dependent sources.

S. Rini, D.Tuninetti, and N.Devroye, “Inner and outer bounds for the Gaussian cognitive interference channel and new capacity results,” IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 820-848, Feb. 2012. 
The paper presents a new set of results on the capacity of the Gaussian cognitive interference channels for several parameter regimes (or channel models).

T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp.116-130, 2009.
A very comprehensive survey on spectrum sensing – introduces the concept of multi-dimensional spectrum sensing – explains the various forms of cooperative sensing –discusses models for prediction of primary user behavior - a one stop reference for spectrum sensing.

Y.-C. Liang, Y. Zeng, E.C.Y. Peh, and A. T. Hoang, “Sensing-throughput tradeoff for cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1326-1337, April 2008. 
This paper is one of the first works that studies the problem of optimizing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected.  The sensing-throughput tradeoff is studied for energy detection and cooperative sensing is also considered.

R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 4-17, February 2008.
This paper presents one of the very first attempts to model the effects of uncertainty in noise and channel fading on signal detectioninthe context of cognitive radios – coins the term “SNR wall” - the minimum SNR of the signal below which a detector is not able to detect it reliably no matter how large the sensing duration is. The paper analyzes the tradeoff between the performance loss to the primary system and the robustness of signal detection/spectrum sensing.

Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, February 2008. 
An important work on linear decision fusion for cooperative spectrum sensing - optimal weightings for linearly combining the energies measured at the cognitive radio users such that the probability of detection is maximized with a constraint on the probability of false alarm.

G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part I: Two user networks,” and “part II: Multiuser networks,"IEEE Transactions on Wireless Communications, vol. 6, no. 6, pp. 2204-2222, June 2007.
These two papers introduce the novel concept of cooperation through relaying for spectrum sensing and thus exploit spatial diversity for performance gain in spectrum sensing.

K. B. Letaief and W. Zhang, “Cooperative communications for cognitive radio networks,” Proceedings of the IEEE, vol. 97, no. 5, pp. 878-893, May 2009. 
An important work on cooperative spectrum sensing – presents several robust cooperative spectrum sensing techniques based on the concepts of cooperative diversity and multiuser diversity.

A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs,” IEEE Communications Magazine, vol. 46, no. 4, pp. 32-39, April 2008.
This is one of the very first papers which provides an overview of the regulatory requirements (e.g., sensing periodicity and detection sensitivity), the major challenges associated with spectrum sensing (e.g., due to uncertainty in radio environment), the commonly used spectrum sensing methods in cognitive radio networks, and the performance tradeoff issues in spectrum sensing.

Y. Zeng and Y.-C. Liang, “Eigenvalue based spectrum sensing algorithms for cognitive radio,” IEEE Transactions Communications, vol. 57, no. 6, pp. 1784-1793, 2009. 
A pioneering work which uses random matrix theory to obtain the probability distributions of the test statistics and find the closed-form expressions for the probability of detection and the probability of false alarm – also proposes spectrum sensing methods which, overcome the noise uncertainty problem, and can be used without requiring the knowledge of signal, channel and noise power.

J. Unnikrishnan and V. V. Veeravalli, “Cooperative sensing for primary detection in cognitive radio," IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 18-27, February 2008.
Another significant work on decision fusion for cooperative spectrum sensing, which considers correlations among the sensing results in the individual nodes – proposes a linear-quadratic (LQ) fusion strategy and compares with counting rule which is linear combining.

S. Haykin, D. Thomson, and J. Reed, "Spectrum sensing for cognitive radio," Proceedings of the IEEE, Special Issue on “Cognitive Radio”, vol. 97, no.5, pp.849-877, May 2009. 
Provides a tutorial on the multi-taper method (MTM), which is a nonparametric method for spectrum sensing. This method provides high spectral-resolution capability, estimates the average power in each sub-band of the spectrum, and identifies the unknown directions of interfering signals.

Y. Zeng, Y.-C. Liang, A.T. Hoang, and R. Zhang, “A review on spectrum sensing for cognitive radio: Challenges and solutions,” EURASIP Journal on Advances in Signal Processing, vol. 2010, Article ID 381465, 2010.
This paper reviews various spectrum-sensing techniques with emphasis on blind sensing techniques, which do not require information about source signals and propagation channels. The paper provides theoretical analysis on test statistic distribution and threshold setting.

P. D. Sutton, K. E. Nolan, and L. E. Doyle, “Cyclostationary signatures in practical cognitive radio applications,” IEEE Journal on Selected Areas in Communications, vol. 26, no.1, pp.13-24, January 2008. 
This paper introduces a novel idea of embedding a cyclostationary signature in a signal to enable shorter sensing duration for signal detection and also to facilitate cognitive network identification.

E. Axell, G.Leus, E. G. Larsson, and H. V. Poor, “Spectrum sensing for cognitive radio: state-of-the-art and recent advances,” IEEE Signal Processing Magazine, vol.29, no.3, pp.101-116, May 2012.
This paper reviews the state-of-the-art and recent advances in spectrum sensing. Some of the recently developed methods are reviewed.

A. F. Molisch, M. Shafi, and L. J. Greenstein, “Propagation issues for cognitive radio,” Proceedings of the IEEE, Special Issue on “Cognitive Radio”, vol. 97, 787-804 (2009). 
Provides a comprehensive overview of the propagation channel characteristics and models, which will be useful for the design of spectrum sensing methods and transmission strategies for cognitive radio systems.

I. Budiarjo, H. Nikookar, and L. Ligthart, “Cognitive radio modulation techniques,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 24-34, November 2008.
This article provides an excellent exposition to the orthogonal frequency-division multiplexing (OFDM) and transform domain communications system (TDCS) modulation techniques for spectrum overlay-based cognitive radio systems.

J. Meng, W. Yin, H. Li, E. Hossain, and Z. Han, “Collaborative spectrum sensing from sparse observations in cognitive radio networks,” IEEE Journal on Selected Topics on Communications, Special Issue on “Advances in Cognitive Radio Networking and Communications”, vol. 29, no. 2, pp. 327-337, February 2011. 
This work is an important contribution towards designing low-overhead methods for wide-band spectrum sensing. In a cognitive radio network with large number of cooperating users and frequency channels, to significantly reduce the overhead of cooperative spectrum sensing to detect spectrum holes, this paper proposes a matrix completion approach along with a novel joint sparsity algorithm.

A. Ghasemi and E. S.  Sousa,  “Interference aggregation in spectrum-sensing cognitive wireless networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 41-56, February 2008.
This paper is a significant contribution towards modeling the distribution of aggregate interference at a primary receiver due to the secondary transmitters in terms of system parameters of a spectrum sensing-based cognitive radio network.

A. Rabbachin, Tony Q. S. Quek, H. Shin, and M. Z. Win,  “Cognitive network interference,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 2, February 2011. 
This is an important contribution to statistical modeling of aggregate interference caused to a primary user in a cognitive radio network. The theory of truncated stable distributions is used for the modeling. The effect of power control on the cognitive network interference is also considered.

M. Wellens and P. Mahonen, “Lessons learned from an extensive spectrum occupancy measurement campaign and a stochastic duty cycle model,” Mobile Networks and Applications (Springer), 2009.
This is one of the very few papers in the literature, which presents a spectrum measurement campaign in detail and the lessons learned in this campaign. Also, the paper presents a stochastic model based on modified beta distribution for the duty cycle of occupancy in a sub-band.

D. A. Roberson, C. S. Hood, J. L. LoCicero, and J. T. MacDonald, “Spectral occupancy and interference studies in support of cognitive radio technology deployment,” in Proc. of First IEEE Workshop on Networking Technologies for Software Defined Radio Networks, pp. 26-35, September 2006. 
This is one of the very early studies on spectrum occupancy and interference in cognitive radio systems, and the paper also discusses the opportunities, challenges, and communication limits in cognitive radio technology.

G. L. Stuber, S. M. Almalfouh, and D. Sale, “Interference analysis of TV-band white space,” Proceedings of the IEEE, vol. 97, no. 4, pp. 741-754, April 2009.
The paper reports a comprehensive experimental study on interference analysis in the IEEE 802.22-based WRAN system.

B. Canberk, I. F. Akyildiz, and S. Oktug,“Primary user activity modeling using first-difference filter clustering and correlation in cognitive radio networks," IEEE/ACM Transactions on Networking, vol. 19, no. 1, pp. 170-183, February 2011. 
This work addresses the potential drawback of Poisson modeling for primary user activity and presents a new model based on the first-difference filter clustering and temporal correlation statistics.  

C. Ghosh, S. Pagadarai, D. Agrawal, and A. M. Wyglinski, “A framework for statistical wireless spectrum occupancy modeling,” IEEE Transactions on Wireless Communications, vol. 9, no. 1, pp. 38-44, January 2010.
This paper proposes a statistical model for spectrum occupancy in time and frequency domain, and the key model parameters are determined from actual measurements.

D. Datla, A. M. Wyglinski, and G. J. Minden, “A spectrum surveying framework for dynamic spectrum access networks,” IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4158-4158, April 2009. 
This is an important work in the area providing a framework for spectrum measurement and offline processing of measured data. The framework has been evaluated by using on real-world spectrum measurement data.

Q. Zhao, L. Tong, A. Swami, and Y. Chen, “Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework,” IEEE Journal on Selected Areas in Communications, vol. 25, no. 3, pp. 589-600, April 2007.
A pioneering work on decentralized cognitive MAC design – provides a decision-theoretic approach which integrates the design of spectrum access with spectrum sensing at the physical layer and traffic statistics determined by the application layer of the primary network – considers a single-user scenario.

A. Ghasemi and E. S. Sousa, “Fundamental limits of spectrum-sharing in fading environments,” IEEE Transactions on Wireless Communications, vol. 6, no. 2, pp. 649-658, February 2007. 
This is the pioneering work on spectrum sharing that evaluates the secondary channel capacities under the average and peak interference power constraints at the primary receiver in different fading environments. It is shown that with the same interference power limit, channel capacity in fading environments exceeds that of the AWGN channel. Impacts of correlated fading and multiple primary receivers on the channel capacity are also studied. Asymptotic capacities under different fading distributions are derived.

H. Kim and K. G. Shin, “Efficient discovery of Spectrum opportunities with MAC-layer sensing in cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. 7, no. 5, pp. 533-545, May 2008.
This is one of the early papers, which studies the problem of maximizing the discovery of spectrum opportunities with MAC-layer sensing by adapting sensing periods assuming an ON/OFF alternating channel activity pattern for primary users. The parameters for the probability distributions of ON/OFF periods are estimated by using Maximum Likelihood (ML) estimators.

R. Zhang and Y.-C. Liang, “Exploiting multi-antennas for opportunistic spectrum sharing in cognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 88-102, February 2008. 
One of the very few works on spectrum sharing in multiple-input and multiple-output (MIMO)-based cognitive radio systems – proposes convex optimization-based methods to maximize the cognitive radio’s transmission rate under the transmit power constraint and a set of interference power constraints for any arbitrary number of primary and secondary, transmit and receive, antennas.

J. Jia, Q. Zhang, and X. Shen, “HC-MAC: A hardware-constrained cognitive MAC for efficient spectrum management,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 106-117, January 2008.
This paper presents a hardware-constrained multichannel cognitive MAC protocol under spectrum overlay. Although it considers a single-user scenario and an independent channel usage model, this is an important contribution towards practical MAC design for cognitive radio networks.

Y. Xing, C. N. Mathur, M. A. Haleem, R. Chandramouli, and K. P. Subbalakshmi, “Dynamic spectrum access with QoS and interference temperature constraints,” IEEE Transactions on Mobile Computing, vol. 6, no. 4, pp. 423-433, April 2007. 
This is a premier work on modeling and optimization of the spectrum sharing problem in a cognitive radio network considering the interference temperature constraint for primary users and the quality-of-service (QoS) requirements for secondary users. A social-optimization formulation and a game-theoretic formulation are considered, respectively, for centralized and distributed implementation of spectrum sharing.

Y. Xing, R. Chandramouli, S. Mangold, and S. Sankar N, “Dynamic spectrum access in open spectrum wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 3, pp. 626-637, March 2006.
This is one of the early works on MAC design for spectrum sharing. This paper presents a learning-based MAC protocol for spectrum-sharing wireless networks such that weighted time-fairness can be achieved among the different networks sharing the spectrum.

X. Kang, Y.-C. Liang, A. Nallanathan, H. K. Garg, and R. Zhang, “Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity,” IEEE Transactions on Wireless Communications, vol. 8, no. 2, pp. 940-950, February 2009. 
A significant work on capacity analysis of a cognitive radio systemunder different power allocation strategies at the cognitive radios such that the interference power experienced by the primary receiver is limited - considers various combination of peak/average transmit and interference power constraints and studies the power allocation strategies to achieve the ergodic, delay-limited, and outage capacities for different fading channel models such as Rayleigh, Nakagami, and log-normal shadowing.

Y. Chen, Q. Zhao, and A. Swami, “Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors,” IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 2053-2071, May 2008.
A premier work on cross-layer (PHY-MAC) design - demonstrates how sensing errors at the PHY layer affects MAC design and how incorporating MAC layer information into physical layer leads to a cognitive spectrum sensor whose performance improves over time by learning from accumulating observations.

L. Zhang, Y.-C. Liang, and Y. Xin, “Joint beamforming and power allocation for multiple access channels in cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 26, no.1, pp. 38-51, January 2008. 
A premier work on single-input multiple-output multiple access channel (SIMO-MAC) for cognitive spectrum sharing where multiple single-antenna secondary users communicate simultaneously to a multi-antenna secondary base station in the presence of multiple single-antenna primary receivers - provides insightful results on the joint beamforming and power allocation design under the transmit power constraint at each secondary transmitter and the interference power constraint at each primary receiver for two distinct objectives: sum-rate maximization and SINR balancing.

G. Bansal, M. J. Hossain, and V. K. Bhargava, “Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems,” IEEE Transactions on Wireless Communications, 7(11): 4710-4718, November 2008.
One premier work on OFDM-based cognitive radio system which investigates the optimal power loading problem to maximize the transmission data rate while maintaining the interference caused to the primary users within a given limit.

L. B. Le and E. Hossain, “Resource allocation for spectrum underlay in cognitive wireless networks," IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 5306-5315, December 2008. 
This is one of the early papers on rate, power and admission control for cognitive radios using code-division multiple access (CDMA) in spectrum underlay scenarios – considers fairness among cognitive radios.

H. Jiang, L. Lai, R. Fan, and H. V. Poor, “Optimal selection of channel sensing order in cognitive radio,” IEEE Transactions on Wireless Communications, vol. 8, no. 1, pp. 297-303, January 2009.
Another important work on multichannel cognitive MAC protocol in a single-user scenario - provides a dynamic programming-based solution for optimal channel sensing order for a given number of time slots in the MAC layer considering adaptive modulation at the physical layer. Both the independent and correlated channel occupancy models are considered.

L. Lai, H. El Gamal, H. Jiang and H. Vincent Poor, “Cognitive Medium Access: Exploration, Exploitation and Competition,” IEEE Transactions on Mobile Computing, vol. 10, no. 2, pp. 239-253, February 2011. 
This paper particularly develops the cognitive medium access with the capability of cognitive radio user to explore, exploit, and compete for the radio resource. The game theory is used to analyze the strategies of cognitive radio users to maximize the total throughput. Also, low complexity protocol is introduced based on the theoretical results

D. I. Kim, L. B. Le, and E. Hossain, "Joint rate and power allocation for cognitive radios in dynamic spectrum access environment," IEEE Transactions on Wireless Communications, vol. 7, no. 12 - part 2, pp. 5517-5527, December 2008
A significant work on joint rate, power and admission control for cognitive radios in spectrum underlay scenarios – considers a realistic scenario where power allocations for the secondary transmitters are performed based on the average (rather than instantaneous) channel gain estimates while satisfying the target interference constraint violation probability for primary receivers.

R. Zhang, Y.-C. Liang and S. Cui, “Dynamic resource allocation in cognitive radio networks,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 102-114, May 2010. 
An important survey on dynamic resource allocation schemes for cognitive radio systems with the interference temperature based spectrum-sharing model. Many new and challenging problems regarding the design of CR systems are formulated and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks.

L. Zhang, Y.-C. Liang, Y. Xin, and H. V. Poor, “Robust cognitive beamforming with partial channel state information,” IEEE Transactions on Wireless Communications, vol. 8, no. 8, pp. 4143-4153, August 2009.
This is the first paper addressing a robust beamforming design problem for cognitive radios.

Z. Hasan, G. Bansal, E. Hossain, and V. K. Bhargava, “Energy-efficient power allocation in OFDM- based cognitive radio systems: A risk-return model,” IEEE Transactions on Wireless Communications, 8(12): 6078-6088, December 2009. 
A premier work on energy-efficient power allocation to maximize the expected transmission rate for OFDM-based cognitive radio systems – takes into account the reliability of the available sub-bands (which depends on sensing error and primary user activity), sub-band power constraints, and total allowed interference limit to the adjacent primary user bands.

M. G. Khoshkholgh, K. Navaie, and H. Yanikomeroglu, “Access strategies for spectrum sharing in fading environment: Overlay, underlay and mixed,” IEEE Transactions on Mobile Computing, vol. 9, no. 12, pp. 1780-1793, December 2010.
This is another pioneering work on the analysis of the ergodic capacity of secondary user system for different spectrum sharing strategies (underlay, overlay, and a hybrid strategy) with one primary user and one secondary user. The analysis reveals that the maximum capacity in a spectrum underlay system can be achieved with reduced signaling complexity where the channel state information between the secondary transmitter and the primary receiver may not be required for power allocation.

N.Nie and C. Comaniciu, “Adaptive channel allocation spectrum etiquette for cognitive radio networks,” in Proc. of First IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN’05), pp. 269-278, November 2005.
This is a premier work on a learning-based distributed and adaptive channel selection method for cognitive radios  - proposes a game theoretic framework to analyze the behaviour of selfish cognitive radios.

R. W. Thomas, D. H. Friend, L. A. Dasilva, and A. B. Mackenzie, “Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives,” IEEE Communications Magazine, vol. 44, no. 12, pp. 51-57, December 2006. 
This is one of the early papers describing the motivations, architecture, functionality, and design, and implementation of cognitive networks applicable to both wired and wireless networks. A cognitive process to learn from past decisions and use this learning to influence future behavior is the foundation for such networks.

C. Clancy, J. Hecker, E. Stuntebeck, and T. O’Shea, “Applications of machine learning to cognitive radio networks,” IEEE Wireless Communications, vol. 14, no. 4, pp. 47-52, 2007. 
This paper pioneers the use of machine learning techniques in cognitive radio, in contrast to the traditional methods which rely on the policy-based and hard-coded approaches – presents a concrete model of a generic cognitive radio with learning engine.

M. Maskery, V. Krishnamurthy, and Q. Zhao, “Decentralized dynamic spectrum access for cognitive radios: Cooperative design of a non-cooperative game,” IEEE Transactions on Communications, vol. 57, no. 2, pp. 459-469, February 2009. 
This is a pioneering work and one among the very early papers on multi-user distributed dynamic spectrum access in a spectrum overlay scenario based on game-theoretic learning. In particular, a carrier-sense multiple access (CSMA) scenario is considered and a game theoretic correlated equilibrium is achieved.

A. He, K. K. Bae, T. Newman, J. Gaeddert, K. Kim, R. Menon, L. Morales-Tirado, J. Neel, Y. Zhao, J. Reed, and W. Tranter, “A survey of artificial intelligence for cognitive radios,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1578-1592, May 2010.
This is the first comprehensive survey paper on the use of artificial intelligence for cognitive radio networks - reviews different implementations of cognitive radio which are designed based on intelligent algorithms including artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs) – also discusses the issues related to the selection of intelligent algorithms (e.g., responsiveness, complexity, security, robustness, and stability).

A. G.-Serrano and L. Giupponi, “Distributed Q-learning for aggregated interference control in cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1823-1834, May 2010. 
Deriving the idea of artificial intelligence, this paper considers the problem of aggregated interference management due to multiple cognitive radios. The IEEE 802.22 standard is modeled as the multiagent system where agents are the secondary base-stations supporting and controlling the data transmission of secondary users. The real-time multiagent reinforcement learning (i.e., decentralized Q-learning algorithm) is introduced to manage the aggregated interference. Both complete and partial information cases are evaluated in which the optimal and suboptimal (but good enough) solutions are obtained, respectively. The computational and memory requirements for implementations of these reinforcement algorithms are evaluated.

A. Anandkumar, N.Michael, A. K. Tang, and A. Swami, “Distributed algorithms for learning and cognitive medium access with logarithmic regret,” IEEE JSAC on Advances in Cognitive Radio Networking and Communications, vol. 29, no. 4, pp. 781-745, April 2011.
This is a significant work on distributed learning of channel availability statistics and channel access in cognitive radio network which presents distributed channel access policies based on the results on classical multi-armed bandit problem. For these policies, the bounds on the regrets are obtained.

M. van der Schaar and F. Fu,  “Spectrum access games and strategic learning in cognitive radio networks for delay-critical applications,” Proceedings of the IEEE, vol. 97, no. 4, pp. 720-740, April 2009. 
This is the first paper considering the cognitive radio network for delay-critical applications using game theory and learning algorithm. Previously, the cognitive radio was believed to be useful only for non-real-time applications (e.g., best effort). However, by using the advanced techniques in optimization and artificial intelligence, this paper proves the feasibility of deploying real-time applications (e.g., multimedia) in the cognitive radio network. This paper presents centralized and decentralized spectrum market models based on the stochastic game framework.

V. Tumuluru, P. Wang, and D. Niyato, “A neural network based spectrum prediction scheme for cognitive radio,” in Proc. of IEEE International Conference on Communications (ICC'10), May 2010, pp. 1-5.
This paper adopts the neural network to predict the availability of the spectrum for cognitive radio users. With the multilayer perceptron (MLP) neural network where the training data is assumed to be available, the accuracy of the spectrum prediction by the secondary user can be improved significantly. Therefore, the secondary user can efficiently access the available spectrum without collision with the transmission by the primary users. Z. Han, R. Zheng, and 

H. Vincent Poor,  “Repeated auctions with Bayesian nonparametric learning for spectrum access in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 3, pp. 890-900, March 2011. 
This is the first paper adopting the concept of Bayesian nonparametric learning algorithm to optimize the repeated spectrum auction in cognitive radio network. The repeated spectrum auction is used which considers the monitoring and entry costs of the secondary users to bid for the radio resource from the primary users. Since the knowledge of the secondary users to other users is limited due to the distributed environment, the secondary user learns from experience using Bayesian nonparametric belief update scheme and adapts the bidding strategies accordingly. Based on the belief, the secondary user decides to join the auction or not. In addition, the bidding strategy is proved to be optimal.

K. W. Choi and E. Hossain, “Opportunistic access to spectrum holes between packet bursts: A learning-based approach,” IEEE Transactions on Wireless Communications, vol. 10, no. 8, pp. 2497-2509, August 2011.
This work presents a novel learning-based approach for dynamic spectrum access by secondary users in a spectrum overlay scenario where the probability of collision with the primary users needs to be bounded. This approach significantly outperforms the traditional listen-before-talk approach.

M. Bkassiny, Y. Li, and S. K. Jayaweera, “A survey on machine-learning techniques in cognitive radios,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1136-1159, Third Quarter 2013. L. Gavrilovska, 
V.Atanasovski, I.Macaluso, and L. A.DaSilva, “Learning and reasoning in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 1761-1777, Fourth Quarter 2013. 
These two papers provide a comprehensive survey for the learning and reasoning techniques from Artificial Intelligence (AI) applied to cognitive radio networks.

I. F. Akyildiz, W.-Y. Lee, and K. R. Chowdhury, “CRAHNs: Cognitive radio ad hoc networks,” Ad Hoc Networks (Elsevier), vol. 7, no. 5, pp. 810-836, July 2009.
Provides a comprehensive survey on multi-hop and ad hoc cognitive wireless networks.

H. Su and X. Zhang, “Cross-layer based opportunistic MAC protocols for QoS provisioning over cognitive radio wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 118-129, January 2008. 
This paper presents an opportunistic channel access protocol that integrates spectrum sensing at the physical (PHY) layer and packet scheduling at the MAC layer considering MAC layer queueing dynamics.

R. Urgaonkar and M. J. Neely, “Opportunistic scheduling with reliability guarantees in cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. 8, no. 6, pp. 766-777, June 2009.
This is a significant work on cross-layer modeling and analysis of single-hop cognitive radio networks. Using stochastic Lyapunov optimization, this paper develops a cross-layer (transport and radio link layers) model for flow control and scheduling of data packets at the secondary transmitters subject to maximum collision constraints for primary users.

Y. T. Hou, Y. Shi, and H. D. Sherali, “Spectrum sharing for multi-hop networking with cognitive radios,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 146-155, January 2008. 
This is a premier work on modeling and analysis of joint routing, subband division and scheduling in multi-hop cognitive radio networks.

M. M. Rashid, M. J. Hossain, E. Hossain, and V. K. Bhargava, “Opportunistic spectrum scheduling for multiuser cognitive radio: A queueing analysis,” IEEE Transactions on Wireless Communications, 8(10): 5259-5269, October 2009.
An important work on cross-layer (PHY-MAC) performance analysis for cognitive radio users in an infrastructure-based dynamic spectrum access environment - considers primary users' activity, CR users' channel activity, bursty traffic arrival pattern at the CR user ends, and correlated channel fading.

F. Akyildiz, Won-Yeol Lee, M. C. Vuran, and S. Mohanty, “A survey on spectrum management in cognitive radio networks,” IEEE Communications Magazine, vol. 46, no. 4, pp. 40-48, April 2008.
This survey paper outlines the research issues and challenges related to spectrum mobility in the context of spectrum management process for cognitive radio networks.

A. De Domenico, E. C.Strinati, and M. Di Benedetto, “A survey on MAC strategies for cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol.14, no.1, pp. 21-44, First Quarter 2012. 
The paper provides a comprehensive survey on the MAC protocols for cognitive radio networks. It identifies the fundamental role of the MAC layer, its functionalities, and provides a classification.

Won-Yeol Lee and I. F. Akyildiz, “Spectrum-aware mobility management in cognitive radio cellular networks,” IEEE Transactions on Mobile Computing, vol.11, no.4, pp.529-542, April 2012. 
The paper presents a mobility management framework to support diverse mobility events in CR networks. This framework consists of spectrum mobility management, user mobility management, and intercell resource allocation.

L.-C. Wang, C.-W. Wang, and K.-T.Feng, “A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks,” IEEE Wireless Communications, vol. 18, no. 6, pp. 18-26, December 2011.
This is the first paper that comprehensively models the effects of spectrum handoff and spectrum management methods on the call-level QoS performance of secondary users.

J. Huang, R. Berry, and M. Honig, “Auction-based spectrum sharing,” Mobile Networks and Applications, vol. 11, no. 3, pp. 405-418.
A seminal work in this area, this paper adopts the auction theory from economics to analyze the spectrum sharing in a cognitive radio network with spectrum underlay. Two auction mechanisms are proposed, i.e., cognitive radio is charged for received signal to interference plus noise ratio (SINR) and charged for transmit power. Both auction mechanisms have attractive property that they maximize the social welfare.

D. Niyato and E. Hossain, “Competitive pricing for spectrum sharing in cognitive radio networks: Dynamic game, inefficiency of Nash equilibrium, and collusion,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 192-202, January 2008. 
One of the very early papers in the area of cognitive network economics and pricing, this paper is a significant contribution towards designing market mechanisms for efficient spectrum allocation and sharing methods. A competitive pricing scheme based on a noncooperative game among multiple primary users is proposed in this paper.

Z. Ji and K. J. R. Liu, “Dynamic spectrum sharing: A game theoretical overview,” IEEE Communications Magazine, vol. 45, no. 5, May 2007.
This paper is an excellent tutorial on the use of game theoretic-models to model the behavior of cognitive radio users with self-interest. With game theory, the dynamic spectrum access can achieve flexibility, efficiency, and fairness. The overview of the game formulation is given which opens the new research direction for cognitive radio network.

F. Wang, M. Krunz, and S. Cui, “Price-based spectrum management in cognitive radio networks,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 1, pp. 74-87, February 2008. 
This is another important work on pricing in cognitive radio networks, which presents a joint power/channel allocation scheme based on a distributed pricing approach. The spectrum allocation is modeled as a noncooperative game, and a price-based iterative water-filling algorithm was introduced to reach the Nash equilibrium solution.

D. Niyato and E. Hossain, “Competitive spectrum sharing in cognitive radio networks: A dynamic game approach,” IEEE Transactions on Wireless Communications, vol. 7, no. 7, pp. 2651-2660, July 2008.
This paper is an important contribution towards modeling spectrum trading since it considers the dynamic behavior of strategy adaptation of primary users. Through a dynamic game model, it analyzes the convergence and optimality of strategy adaptations.

D. Niyato, E. Hossain, and Z. Han, “Dynamics of multiple-seller and multiple-buyer spectrum trading in cognitive radio networks: A game theoretic modeling approach,” IEEE Transactions on Mobile Computing, vol. 8, no. 8, August 2009, pp. 1009-1022. 
This is the first paper in the literature that models the most general spectrum- trading scenario in a cognitive radio with multiple spectrum sellers and buyers. An evolutionary game model is used for the spectrum selection of secondary users when choosing the primary user (or service providers) to buy spectrum from. A hierarchical game model was presented to obtain the equilibrium solution for the primary users in selecting the spectrum price to maximize their profits given the performance degradation of the primary users due to the sharing available spectrum with the secondary users.

Z. Ji and K. J. R.  Liu, “Multi-stage pricing game for collusion-resistant dynamic spectrum allocation," IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 182-191, January 2008.
The collusive behavior of users could be a significant threat to efficient dynamic spectrum allocation in a distributed cognitive radio network. This paper presents a systematic approach to avoid collusion in cognitive radio. The spectrum allocation with multiple selfish legacy spectrum holders and unlicensed users is modeled as a multi-stage dynamic game, and a pricing-based distributed collusion-resistant spectrum allocation approach is used to optimize overall spectrum efficiency.

R. Chen, J.-M. Park, and J. H. Reed, “Defense against primary user emulation attacks in cognitive radio networks,” IEEE Journal on Selected Areas in Communications, vol. 26, no. 1, pp. 25-37, January 2008.
In cognitive radio, it is important to distinguish between the signals from primary and secondary users. Especially, in a hostile environment, the attacker may use a modified air interface of a cognitive radio to imitate the signal of primary users making secondary user unable to access the spectrum. This paper systematically shows that the aforementioned attack can result in severe interference and significantly reduce spectrum utilization. To solve the problem, it proposes a transmitter verification scheme, namely LocDef (localization-based defense) scheme, which is able to identify whether the signal is from primary user or not by using an estimated location of a transmitter (i.e., based on non-interactive localization technique) and characteristics of the signal itself.

R. Chen, J.-M. Park, Y. T. Hou, and J. H. Reed, “Toward secure distributed spectrum sensing in cognitive radio networks,” IEEE Communications Magazine, vol. 46, no. 4, pp. 50-55, April 2008. 
This is a pioneering work on mitigating the security threats such as the incumbent emulation and spectrum sensing data falsification threats in cognitive radio networks, which can degrade the performances of distributed spectrum sensing. In particular, an attacker (e.g., malicious secondary user) tries to gain the priority over other secondary users by transmitting signals emulating that of primary user (i.e., incumbent emulation) or reporting the false spectrum sensing (i.e., spectrum sensing data falsification). In this paper, the defending methods against these attacks are introduced. For an incumbent emulation attack, the signal verification method is proposed. For a spectrum sensing data falsification attack, a robust data fusion method is proposed.

T. C. Clancy and N. Goergen, “Security in cognitive radio networks: Threats and mitigation,” in Proc. of 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), pp. 1-8, May 2008.
Provides a comprehensive investigation on the security in cognitive radio networks. The cognitive radio devices could wrongly learn from environment and be taught by malicious activities. This paper illustrates how various attacks (e.g., sensory manipulation attacks against policy radios, belief manipulation attacks against learning radios, and self-propagating behavior leading to cognitive radio viruses) can utilize this approach. To address the problem, the common sense policy can be included in wireless devices to avoid learning the manipulated environment by the attacker.

A. G. Fragkiadakis, E. Z. Tragos, and I. G.Askoxylakis, “A survey on security threats and detection techniques in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol.15, no.1, pp. 428-445, First Quarter 2013. 
The paper provides a comprehensive survey of security threats and the corresponding detection techniques. The focus is on the cognitive capability and reconfigurability. Threats related to the cognitive capability include attacks that mimic primary transmitters, and transmission of false observations related to spectrum sensing. Reconfiguration can be exploited by attackers through the use of malicious codes installed in cognitive radios.

H. Li and Z. Han, “Catch me if you can: An abnormality detection approach for collaborative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 9, no.11, pp. 3554-3565, November 2010.
This work considers the problem of malicious secondary users deliberately sending false reports to gain access of the spectrum. It proposes an abnormality-detection approach which does not rely on the knowledge of attacker's strategy, and hence suitable for a practical cognitive radio system. This approach is based on the analysis of report history of all secondary users. This history is organized in a high-dimensional space so that the abnormalities can be possibly detected.

H. Li and Z. Han, “Dogfight in spectrum: Combating primary user emulation attacks in cognitive radio systems part II: Unknown channel statistics,” IEEE Transactions on Wireless Communications, vol. 9, no. 11, pp. 3566-3577, November 2010. 
This is another important work on primary user emulation attack, which develops a defense strategy against this attack considering the uncertainties in the channel statistics. In particular, the adversarial multi-armed bandit algorithm is modified to obtain the optimal defense strategy using the experience of spectrum access.

Z. Gao, H. Zhu, S. Li, S. Du, and X. Li, “Security and privacy of collaborative spectrum sensing in cognitive radio networks,” IEEE Wireless Communications, vol.19, no.6, pp.106-112, December 2012.
The paper introduces a new kind of privacy vulnerability in cognitive radio. This is related to attacks in collaborative sensing, which are expected to compromise secondary users’ location privacy by correlating their sensing reports and their physical locations.

A. Attar, H. Tang, A. V. Vasilakos, F. R. Yu, and V. C. M. Leung, “A survey of security challenges in cognitive radio networks: Solutions and future research directions,” Proceedings of the IEEE, vol. 100, no. 12, pp. 3172-3186, Dec. 2012. 
The paper presents a comprehensive list of major known security threats within a cognitive radio framework. It classifies the attacks based on the type of attacker, namely, exogenous (external) attackers, intruding malicious nodes, and greedy cognitive radios.

T. Chen, H. Zhang, G. M. Maggio, and I. Chlamtac, “CogMesh: A cluster-based cognitive radio network,” in Proc. of 2nd IEEE International Symposium on Dynamic Spectrum Access Networks(DySPAN’07), pp. 168-178, April 2007.
This paper shows how the principles of cognitive radio can be also used in multi-hop wireless mesh networks - proposes a cluster-based dynamic channel allocation framework for cognitive wireless mesh networks taking the issues of interference and coexistence with primary users into account.

J. Wang, M. Ghosh, and K. Challapali, “Emerging cognitive radio applications: A survey,” IEEE Communications Magazine, vol. 49, no. 3, pp. 74-81, March 2011. 
Provides comprehensive survey on the emerging cognitive radio applications (e.g., smart grid communications, public safety, and broadband cellular to medical applications). This paper provides a high-level detail on how to adopt cognitive radio into such applications, related challenges and some solution approaches. Also, it presents some standardization processes related to adopting cognitive radio in various applications.

G. Gür, S. Bayhan, and F. Alagöz, “Cognitive femtocell networks: An overlay architecture for localized dynamic spectrum access,” IEEE Wireless Communications, vol. 17, no. 4, pp. 62-70, Aug. 2010.
An important work on the application of cognitive radio concepts in the evolving femtocell networks - proposes a femtocell-based cognitive radio architecture for enabling multitiered opportunistic access in next-generation hierarchical cellular wireless networks.

G. Gür and F. Alagöz, “Green wireless communications via cognitive dimension: An overview,” IEEE Network, vol. 25, no. 2, pp. 50-56, March-April 2011. 
An interesting work on applications of cognitive radio concepts in the context of green communications. Two approaches are discussed, i.e., applying cognitive radio to achieve energy efficiency and developing energy efficiency for cognitive radio systems. The paper introduces the different techniques in achieving energy efficiency in wireless systems and how to apply those techniques in cognitive radio.

R. Bagheri, A. Mirzaei, S. Chehrazi, M. E. Heidari, M. Lee, M. Mikhemar, W. Tang, and A. A.  Abidi, “An 800-MHz–6-GHz software-defined wireless receiver in 90-nm CMOS,” IEEE Journal of Solid-State Circuits, vol. 41, no. 12, pp. 2860-2876, December 2006. 272
This paper introduces the design of a software-defined radio from a low-power ADC perspective. The design is based on the windowed integration sampler and clock-programmable discrete-time analog filters. By using the low-noise amplifier (LNA) and a wide tuning-range synthesizer, the wideband RF front-end can operate on any frequency between 800MHz and 6GHz. The wideband LNA can achieve a good performance (e.g., 18-20dB of maximum gain). The performance evaluation is performed on the GSM and 802.11g standards.

J. Mitola III, “Software radio architecture: A mathematical perspective,” IEEE Journal on Selected Areas in Communications, vol. 17, no. 4, pp. 514-538, April 1999. 
This is the very first paper on cognitive radio which provides the details of a multiband/multimode and a plug-and-play programmable software radio architecture – also presents a mathematical model to analyze software radio based on the Turing machine.

A. A. Abidi, “The path to the software-defined radio receiver,” IEEE Journal of Solid-State Circuits, vol. 42, no. 5, pp. 954-966, May 2007.
This is the first paper surveying the design methodologies for constructing flexible software-defined radio receiver for cognitive radio – discusses the design for a digital front-end with a wideband low noise amplifier and a tunable mixer. The proposed receiver can work in any band from 800MHz to 6GHz with any bandwidth to provide flexibility for cognitive radio applications.

D. Cabric, I. D. O'Donnell, M. S.-W. Chen,and R. W. Brodersen, “Spectrum sharing radios,” IEEE Circuits and Systems Magazine, vol. 6, no. 2, pp. 30-45, 2006. 
This is an early paper on design and implementation of an UWB cognitive radio for high data rate communications with very low transmission power. The design of ultra-low power baseband, impulse-UWB transceiver front-end, and digital backend implementation are discussed. In addition, the paper reports a test-bed implementation based on the Berkeley Emulation Engine 2 (BEE2).

S. M. Mishra, D. Cabric, D. Chang, D. Willkomm, B. van Schewick, S. Wolisz, and B. W. Brodersen, “A real time cognitive radio testbed for physical and link layer experiments,” in Proc. of First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), pp. 562-567, 8-11 November 2005.
This is the first paper introducing a real-time cognitive radio test-bed for physical and link layer experiments. This test-bed is based on the Berkeley Emulation Engine 2 (BEE2), which is a multi-FPGA emulation engine. Using BEE2, the test-bed can connect to 18 radio front-ends, and these front-ends can be set to be the primary and secondary users in various test scenarios. In addition, by using FPGA, the test-bed can simultaneously operate multiple radios, suitable for complex experiments and performance studies of high-speed low latency links.

K. Hong, S. Sengupta, and R.Chandramouli, “SpiderRadio: A cognitive radio implementation using IEEE 802.11 components,” IEEE Transactions on Mobile Computing, vol. 12, no. 11, pp. 2105-2118, November 2013. 
This paper presents the implementation of a cognitive radio MAC using IEEE 802.11-based off-the-shelf components. The fundamental tradeoff between complexity and network performance is studied. For researchers interested in implementation of a cognitive radio system, this paper would be useful.

C. Cordeiro, K. Challapali, D. Birru, and N. S. Shankar, “IEEE 802.22: The first worldwide wireless standard based on cognitive radios,” in Proc. of First IEEE International Symposium on Dynamic Spectrum Access Networks(DySPAN’05), pp. 328-337, 8-11 November 2005.
This is the first paper that discusses about the IEEE 802.22 standard for wireless regional area networks (WRANs). This paper provides both technical and business perspectives of the standard. An overview of 802.22 architecture (e.g., topology, entities, and connections), its requirements (e.g., service capacity, service coverage, physical and MAC layer details), applications, and coexistence issues (e.g., antenna, TV and wireless microphone sensing and protection) are discussed.

C. Stevenson, G. Chouinard, Z. Lei, W. Hu, S. Shellhammer, and W. Caldwell, “IEEE 802.22: The first cognitive radio wireless regional area network standard,” IEEE Communications Magazine, vol. 47, no. 1, pp.130-138, January 2009. 
This is the first paper providing a comprehensive discussion on the IEEE 802.22 standard for wireless regional area networks (WRANs) and compares with 802.16e.

F. Granelli, P. Pawelczak, R. Venkatesha Prasad, K.P. Subbalakshmi, R. Chandramouli, J.A. Hoffmeyer, and S. Berger, “Standardization and research in cognitive and dynamic spectrum access networks: IEEE SCC 41 efforts and open issues,” IEEE Communications Magazine, January 2010.
This is the first paper presenting a comprehensive review of the standardization activities in cognitive radio and dynamic spectrum access. The paper focuses on the IEEE P1900 and IEEE SCC (standards coordinating committee) 41 for the dynamic spectrum access networks. The relationship of IEEE SCC 41 with other standard entities including IEEE 802.22 and some open issues (e.g,. regulation and testing, system design and networking, and security) are also discussed.

S. Filin, H. Harada, H. Murakami, and K.Ishizu, “International standardization of cognitive radio systems,” IEEE Communications Magazine, vol.49, no.3, pp.82-89, March 2011. 
This paper reviews major standardization efforts on cognitive radio networks including those from International Telecommunication Union, IEEE, European Telecommunications Standards Institute, and European Association for Standardizing Information and Communication Systems.

M.  Murroni et al., “IEEE 1900.6 spectrum sensing interfaces and data structures for dynamic spectrum access and other advanced radio communication systems standard: Technical aspects and future outlook,” IEEE Communications Magazine, vol. 49, no. 12, pp. 118 - 127, December 2011.
This is an extensive review paper on the IEEE 1900.6 standard focusing on the technical details of spectrum sensing interfaces and data structure for dynamic spectrum access. The relationship of the IEEE 1900.6 standard with other standardization efforts (e.g., CogNeA ECMA 392, ETSI DTR/RRS-01003/02004, and 3GPP-LTE) is also discussed.