We study the distributed spectrum sensing in cognitive radio networks. Existing distributed consensus-based fusion algorithms only ensure equal gain combining of local measurements, whose performance may be incomparable to various centralized soft combining schemes. Motivated by this fact, we consider practical channel conditions and link failures, and develop new weighted soft measurement combining without a centralized fusion center. Following the measurement by its energy detector, each secondary user exchanges its own measurement statistics with its local one-hop neighbors, and chooses the information exchanging rate according to the measurement channel condition, e.g., the signal-to-noise ratio (SNR). We rigorously prove the convergence of the new consensus algorithm, and show all secondary users hold the same global decision statistics from the weighted soft measurement combining throughout the network. We also provide distributed optimal weight design under uncorrelated measurement channels. The convergence rate of the consensus iteration is given under the assumption that each communication link has an independent probability to fail, and the upper bound of the iteration number of the$ \epsilon$-convergence is explicitly given as a function of system parameters. Simulation results show significant improvement of the sensing performance compared to existing consensus-based approaches, and the performance of the distributed weighted design is comparable to the centralized weighted combining scheme.

In modern communication systems, power amplifiers (PAs) are important components and inherently nonlinear. The nonlinearity of the PA causes bandwidth expansion of the communication signal, often referred to as spectral regrowth, at the PA output. Conventionally, spectral regrowth is treated as a distortion, and a range of compensation and filtering techniques have been considered to mitigate its effect. In this paper, we propose to exploit spectral regrowth to enhance channel identification accuracy. Our approach is motivated by the fact that the nonlinearly amplified communication signal carries more bandwidth and allows better probing of the channel. We introduce an iterative algorithm which jointly estimates the PA characteristics and the channel impulse response. The effectiveness of the proposed algorithm is illustrated by computer simulation.

This paper investigates a type of attacks on a cognitive radio (CR) network, most active band (MAB) attack, where an attacker or a malicious CR node senses/determines the most active band within a multi-band CR network and targets this band through a denial of service (DoS) attack. We propose a countermeasure strategy, coordinated concealment strategy (CCS), to counter the MAB attack. Our results show that CCS significantly outperforms CR's inherent capability of signal/interference avoidance under a MAB attack. We also introduce power control in CCS to further improve the countermeasure performance in terms of the percentage of survival nodes.

In this paper, we study the distributed spectrum sensing in cognitive radio networks. Using weighted average consensus algorithm, we develop a weighted soft measurement combining scheme without the centralized fusion center. After the measurement by the energy detector, each secondary user (SU) exchanges their own measurement statistics with its local neighbors, and chooses the information exchanging rate according to the estimated average signal-to-noise ratio (SNR). We prove the convergence of the consensus iteration, and each SU will hold the global decision statistics from the weighted soft measurement combining throughout the network. The proposed scheme is robust with respect to temporary communication link failures. Simulation results show our method has a better performance than the existing average consensus-based approach.

In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.

This paper characterizes key issues in establishing a policy language for IEEE standards activity P1900.5 to shape radio behavior that realizes social contracts and business logic via cognitive radio policy languages (CRPL). This paper shows the current lack of consensus on the scope and needs of policy languages, tracing roots to differences among regulatory, financial, business, and technical domains. Cognitive linguistics, an emerging theory of human language acquisition and use shows why this is the case and may assist in formulating a CRPL that addresses the diverse policy needs of the regulatory and business communities as well as the narrower issues of dynamic spectrum.

The radio research community has aggressively embraced cognitive radio for dynamic radio spectrum management to enhance spectrum usage, e.g., in ISM bands and as secondary users in unused TV bands, but the needs of the mobile wireless user have not been addressed as thoroughly on the question of high quality of information (QoI) as a function of place, time, and social setting (e.g. commuting, shopping, or in need of medical assistance). This paper considers the evolution of cognitive radio architecture (CRA) in the context of motivating use cases such as public safety and sentient spaces to characterize CRA with an interdisciplinary perspective where machine perception in visual, acoustic, speech, and natural language text domains provide cues to the automatic detection of stereotypical situations, enabling radio nodes to select from among radio bands and modes more intelligently and enabling cognitive wireless networks to deliver higher QoI within social and technical constraints, made more cost effective via embedded and distributed computational intelligence.

The 17 papers in this special issue focus on the theory and applications of cognitive radio.

Today's wireless services have come a long way since the roll out of the conventional voice-centric cellular systems. The demand for wireless access in voice and high rate data multi-media applications has been increasing. New generation wireless communication systems are aimed at accommodating this demand through better resource management and improved transmission technologies. Pushing the adaptive system design further by introducing multi-dimensional awareness, sensing, and learning from its experiences to reason, plan, and decide future actions to meet user needs brings the 'Cognitive Radio' concept into the wireless world. Even though there is no consensus on the formal definition of cognitive radio as of now, the concept has evolved recently to include various meanings in several contexts. One main aspect concerns autonomously exploiting locally unused spectrum to provide new paths to spectrum access. Other aspects include interoperability across several networks; roaming across borders while staying in compliance with local regulations; adapting the system, transmission, and reception parameters without user intervention; and having the ability to understand and follow actions and choices taken by their users, learning to become more responsive over time. The interest in increasing spectrum access and improving spectrum efficiency combined with both the introduction of software-defined radios (SDR) and the realization that machine learning can be applied to radios has created intriguing new possibilities for wireless radio research. This special issue discusses the cognitive radio, SDR, and adaptive radio concepts from several directions. There are many fundamental and practical issues that need to be studied more to ensure the success of these concepts in wireless communication markets. We are delighted that this special issue addresses some of these issues with eight papers selected through a competitive peer review process. In addition to the peer-reviewed papers, one invited paper is included. First, the invited paper discusses the practical implementation of cognitive radio including passing environmental information from the radio to the cognitive engine, using this information and performing real-time control of the radio platform by the cognitive engine. This paper provides a broad view to the readers and relates other technical papers to the larger themes of cognitive radio. A central value proposition of cognitive radio is to use limited radio spectrum resources as efficiently as possible. Opportunistic spectrum usage exploits the unused part of the spectrum to increase capacity. However, this requires spectrum awareness and new techniques for efficient utilization of densely occupied spectrum. The paper entitled 'Spectrum sensing in cognitive radio networks: The cooperation-processing tradeoff' discusses spectral awareness. Local and cooperative spectrum sensing and related issues are discussed under realistic radio channel conditions with fading. Next, in the paper entitled 'Spectrum sharing through distributed coordination in dynamic spectrum access networks,' techniques for the sharing of available spectrum are discussed. Coordination of the spectrum usage to support fair access to spectrum with minimum mutual interference is discussed. Related to these two papers, the fourth paper 'Formalizing the interference temperature model' defines the interference temperature model, and derives algorithms for computing radio frequency (RF) transmission parameters that maximize both capacity and spectral efficiency for a given RF environment. Similar to radio spectrum, radiated power is also a very important resource in wireless communications, especially for wireless sensor networks. The battery power is limited in small wireless devices, so it must be used very efficiently. One important consideration in cognitive radio is system design to adaptively use power as efficiently as possible. The fifth paper 'Efficient duty cycling through prediction and sampling in wireless sensor networks' deals with this issue through an innovative medium-access-control (MAC) layer protocol for wireless sensor networks for an adaptive interface to achieve ultra-low power operation. The next two papers address SDR implementation of cognitive radio. The paper entitled 'Automatic antenna tuning unit for software-defined and cognitive radio' discusses the implementation of an automatic antenna tuning unit system (ATU). Antennas for true SDR and cognitive radio realizations must cover a variety of frequency bands with radiation and reception parameters suited to different wireless standards. This paper covers recent progress in reconfigurable antenna technology. On the other hand, the paper entitled 'DSP implementation of a bit loading algorithm for adaptive wireless multicarrier transceivers' studies fixed-point DSP hardware implementations of an adaptive bit loading algorithm that can increase the capacity of the multicarrier systems like OFDM efficiently via implementation with reasonable computational complexity. The last two papers are related to the optimization of the radio resource and transmission parameters. The paper entitled 'Cognitive engine implementation for wireless multicarrier transceivers' presents a genetic algorithm (GA) driven cognitive engine for single carrier and multicarrier systems. The last paper entitled 'Install or invoke: The optimal trade-off between performance and cost in the design of multi-standard reconfigurable radios' finds the optimal trade-off between performance and cost, via a mathematical model of the design choices represented in a graph of progressively simpler functional modules. We thank the contributors of this special issue for their excellent contributions and for working with us to publish their work. Also, we extend our great appreciation to the reviewers who dedicated their valuable time to provide constructive comments, suggestions, and corrections. Finally, we thank the Editor-in-Chief, Professor Mohsen Guizani, and the staff at Wiley for their guidance. With such support, we believe that this special issue offers a considerable and timely contribution in the area of cognitive radio, and we hope that our readers will benefit from these papers and find them interesting.

As the software radio makes its transition from research to practice, it becomes increasingly important to establish provable properties of the software radio architecture on which product developers and service providers can base technology insertion decisions. Establishing provable properties requires a mathematical perspective on the software radio architecture. This paper contributes to that perspective by critically reviewing the fundamental concept of the software radio, using mathematical models to characterize this rapidly emerging technology in the context of similar technologies like programmable digital radios. The software radio delivers dynamically defined services through programmable processing capacity that has the mathematical structure of the Turing machine. The bounded recursive functions, a subset of the total recursive functions, are shown to be the largest class of Turing-computable functions for which software radios exhibit provable stability in plug-and-play scenarios. Understanding the topological properties of the software radio architecture promotes plug-and-play applications and cost-effective reuse. Analysis of these topological properties yields a layered distributed virtual machine reference model and a set of architecture design principles for the software radio. These criteria may be useful in defining interfaces among hardware, middleware, and higher level software components that are needed for cost-effective software reuse.