多天线认知无线电网络多信道联合频谱感知策略

当前,以单天线认知用户组成的认知无线电网络同时协作检测多个信道的分配策略得到了广泛研究,而在认知用户可以灵活选取自身天线进行空间分集接收的条件下,当联合感知多信道时,多天线认知无线电网络如何获取最优的天线分配策略仍有待进一步研究。为解决这一问题,在限制各信道最大虚警概率的前提下,以最小化所有信道漏检概率之和为目标,建立了优化模型,并提出了基于分支定界的算法和基于贪婪思想的启发式算法。前者可以获得最优策略,但复杂度较高,后者以牺牲较小检测性能为代价,明显降低了复杂度,有效实现了检测性能与复杂度的平衡,并且在保护各个信道上主用户免受认知用户干扰层面,一定程度上兼顾了公平性。      

Spectrum management in cognitive radio ad hoc networks

The problem of spectrum scarcity and inefficiency in spectrum usage will be addressed by the newly emerging cognitive radio paradigm that allows radios to opportunistically transmit in the vacant portions of the spectrum already assigned to licensed users. For this, the ability for spectrum sensing, spectrum sharing, choosing the best spectrum among the available options, and dynamically adapting transmission parameters based on the activity of the licensed spectrum owners must be integrated within cognitive radio users. Specifically in cognitive radio ad hoc networks, distributed multihop architecture, node mobility, and spatio-temporal variance in spectrum availability are some of the key distinguishing factors. In this article the important features of CRAHNs are presented, along with the design approaches and research challenges that must be addressed. Spectrum management in CRAHNs comprises spectrum sensing, sharing, decision, and mobility. In this article each of these functions are described in detail from the viewpoint of multihop infrastructureless networks requiring cooperation among users.     

Cooperative Communications for Cognitive Radio Networks

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space–time–frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.      

Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.      

OS-MAC: An Efficient MAC Protocol for Spectrum-Agile Wireless Networks

Wireless networks and devices have been rapidly gaining popularity over their wired counterparts. This popularity, in turn, has been generating an explosive and ever-increasing demand for, and hence creating a shortage of, the radio spectrum. Existing studies indicate that this foreseen spectrum shortage is not so much due to the scarcity of the radio spectrum, but due to the inefficiency of current spectrum access methods, thus leaving spectrum opportunities along both the time and the frequency dimensions that wireless devices can exploit. Fortunately, recent technological advances have made it possible to build software-defined radios (SDRs) which, unlike traditional radios, can switch from one frequency band to another at little or no cost. We propose a MAC protocol, called Opportunistic Spectrum MAC (OS-MAC), for wireless networks equipped with cognitive radios like SDRs. OS-MAC (1) adaptively and dynamically seeks and exploits opportunities in both licensed and unlicensed spectra and along both the time and the frequency dimensions; (2) accesses and shares spectrum among different unlicensed and licensed users; and (3) coordinates with other unlicensed users for better spectrum utilization. Using extensive simulation, OS-MAC is shown to be far more effective than current access protocols from both the network's and the user's perspectives. By comparing its performance with an Ideal-MAC protocol, OS-MAC is also shown to not only outperform current access protocols, but also achieve performance very close to that obtainable under the Ideal-MAC protocol.     

Cognitive Radio and Networking Research at Virginia Tech

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.     

Capacity maximization in spectrum sensing for Cognitive Radio Networks thru Outage Probability

With today's increase in the usage of wireless devices and the consequent spectrum allocation, radio spectrum is becoming scarce. In practice most of the allotted spectrum is not used for large periods of time. Cognitive radio has been proposed to exploit the presence of these unused spectrum band (called as spectrum hole). Cognitive radios perform radio environment analysis, identify the spectrum holes and operate in those holes. Several factors like fading and shadowing affects the ability of the cognitive radio to detect the primary user. The current research shows that cooperation among the cognitive users can increase the detection probability for a given probability of false alarm. We proposed the system that to have maximized the capacity in spectrum sensing for Cognitive Radio Networks thru Outage Probability for Rayleigh fading channel.     

Dynamically optimized spatiotemporal prioritization for spectrum sensing in cooperative cognitive radio

In this paper, an enhanced cooperative, statistics-driven spectrum sensing algorithm, called Dynamically Optimized Spatiotemporal Prioritization (DOSP), is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The target of the DOSP algorithm is to improve spectrum sensing efficiency and achieve better spectrum access opportunities by prioritizing channels for fine sensing. The sensing priority is determined dynamically and intelligently based on an optimal statistical fusion that jointly considers both the local statistics obtained by the individual cognitive radios as well as the long-term spatiotemporal statistics obtained by other cognitive radios in the network. As such, the individual cognitive radio peers work together to get the most out of available spectrum opportunities. Numerical results demonstrate that the proposed DOSP algorithm is capable of achieving better performance compared with recently reported cooperative spectrum sensing methods in terms of overhead and percentage of missed spectrum opportunities. Furthermore, results show that the DOSP algorithm is more robust to the environment of low cognitive radio densities than that by using other state-of-the-art cooperative spectrum sensing methods.     

Cognitive radio testing using psychometric approaches: applicability and proof of concept study

Cognitive radios promise efficient spectrum use and other performance improvements through use of machine learning to adapt the radios?? operational parameters to optimize performance; however, their flexibility complicates evaluation of cognitive radios?? performance. We propose to improve cognitive radio development and evaluation using approaches developed for efficiently measuring and testing human cognitive characteristics. Cognitive radio performance evaluation requirements and applicable psychometric approaches are described. Finally, a proof of concept application of a psychometric measurement technique to evaluate cognitive engine performance is presented for simulated channel conditions for multiple prioritizations of optimization goals.     

Cognitive radio for next-generation wireless networks: an approach to opportunistic channel selection in ieee 802.11-based wireless mesh - [accepted from open call]

?Cognitive radio? has emerged as a new design paradigm for next-generation wireless networks that aims to increase utilization of the scarce radio spectrum (both licensed and unlicensed). Learning and adaptation are two significant features of a cognitive radio transceiver. Intelligent algorithms are used to learn the surrounding environment, and the knowledge thus obtained is utilized by the transceiver to choose the frequency band (i.e., channel) of transmission as well as transmission parameters to achieve the best performance. In this article we first provide an overview of the different components to achieve adaptability in a cognitive radio transceiver and discuss the related approaches. A survey of the cognitive radio techniques used in the different wireless systems is then presented. To this end, a dynamic opportunistic channel selection scheme based on the cognitive radio concept is presented for an IEEE 802.11-based wireless mesh network.     

Capacity Analysis in Multi-Radio Multi-Channel Cognitive Radio Networks: A Small World Perspective

Cognitive radio (CR) has emerged as a promising technology to improve spectrum utilization. Capacity analysis is very useful in investigating the ultimate performance limits for wireless networks. Meanwhile, with increasing potential future applications for the CR systems, it is necessary to explore the limitations on their capacity in dynamic spectrum access environment. However, due to spectrum sharing in cognitive radio networks (CRNs), the capacity of the secondary network (SRN) is much more difficult to analyze than that of traditional wireless networks. To overcome this difficulty, in this paper we introduce a novel solution based on small world model to analyze the capacity of SRN. First, we propose a new method of shortcut creation for CRNs, which is based on connectivity ratio. Also, a new channel assignment algorithm is proposed, which jointly considers the available time and transmission time of the channels. And then, we derive the capacity of SRN based on small world model over multi-radio multi-channel (MRMC) environment. The simulation results show that our proposed scheme can obtain a higher capacity and smaller latency compared with traditional schemes in MRMC CRNs.     

Efficient Utilization of Available Channels in Dynamic Spectrum Access Networks

In case of dynamic spectrum access networks, how to efficiently utilize the dynamically available bandwidth is very important to enhance the performance of the networks. In this paper, we propose an Error Adaptive MAC protocol which adaptively changes its transmission mode according to the channel status. Using the cognitive radio technology, additional channels are assumed to be randomly available for data transmission. When the channel error rate is relatively high, those additional channels are utilized for error recovery; otherwise, the extra channels can be used to increase the throughput if the wireless medium is stable and reliable. We formulate an analytical model to study the dynamics of our adaptive MAC protocol, and using simulation, show our proposed method can significantly enhance the throughput of dynamic spectrum access networks.     

Multihop cognitive radio networks: to route or not to route

Routing is a fundamental issue to consider when dealing with multihop cognitive radio networks. We investigate in this work, the potential routing approaches that can be employed in such adaptive wireless networks. We argue that in multihop cognitive radio environments no general routing solution can be proposed, but cognitive environments can be classified into three separate categories, each requiring specific routing solutions. Basically, this classification is imposed by the activity of the users on the licensed bands that cognitive radios try to access. First, over a relatively static primary band, where primary nodes idleness largely exceeds cognitive users communication durations, static mesh routing solutions can be reused, whereas second, over dynamically available spectrum bands new specific routing solutions have to be proposed, we give some guidelines and insights about designing such solutions. Third, if cognitive radios try to access over highly active and rarely available primary bands, opportunistic forwarding without preestablished routing is to be explored.     

Graph-based criteria for spectrum-aware clustering in cognitive radio networks

Cognitive radios (CRs) can exploit vacancies in licensed frequency bands to self-organize in opportunistic spectrum networks. Such networks, henceforth referred to as cognitive radio networks (CRNs), operate over a dynamic bandwidth in both time and space. This inherently leads to the partition of the network into clusters depending on the spatial variation of the primary radio network (PRN) activity. In this article, we analytically evaluate the performance of a new class of clustering criteria designed for CRNs, which explicitly take into account the spatial variations of spectrum opportunities. We jointly represent the network topology and spectrum availability using bipartite graphs. This representation reduces the problem of spectrum-aware cluster formation to a biclique construction problem. We investigate several criteria for constructing clusters for the CRN environment, and characterize their performance under different spectrum sensing and PR activity models. In particular, we evaluate the expected cluster size and number of common idle channels within each cluster, as a function of the spectrum and topology variability. We verify our analytical results via extensive simulations.     

A cross‐layer study for application‐aware multi‐hop cognitive radio networks

Satisfying the different requirements of applications is important in multi‐hop cognitive radio networks, because the spectrum resources change dynamically and the requirements for various applications could be very different. In this paper, we propose new schemes of channel allocation and route selection for real‐time and non‐real‐time applications to tackle this challenge. Our scheme is flexible so that it can adapt to the different application requirements, and it can provide enough throughput while maintaining the packet loss rate and transmission rate requirements. First, we give the network model in a cognitive radio network environment, and show how to calculate the capacity of the route in multi‐hop cognitive radio networks. Second, we formulate optimization problems to fulfill the rate requirements of different applications for each unicast session. We also consider the primary user activities, channel availability, interface and interference constraint. We propose the corresponding routing and channel allocation schemes for different application scenarios. Third, we propose an admission control scheme to study the impact of application requirements for multiple sessions in cognitive radio networks. Finally, we implement simulations to show the performance of our schemes and compare them with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.     

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

Applications of Machine Learning to Cognitive Radio Networks

Cognitive radio offers the promise of intelligent radios that can learn from and adapt to their environment. To date, most cognitive radio research has focused on policy-based radios that are hard-coded with a list of rules on how the radio should behave in certain scenarios. Some work has been done on radios with learning engines tailored for very specific applications. This article describes a concrete model for a generic cognitive radio to utilize a learning engine. The goal is to incorporate the results of the learning engine into a predicate calculus-based reasoning engine so that radios can remember lessons learned in the past and act quickly in the future. We also investigate the differences between reasoning and learning, and the fundamentals of when a particular application requires learning, and when simple reasoning is sufficient. The basic architecture is consistent with cognitive engines seen in AI research. The focus of this article is not to propose new machine learning algorithms, but rather to formalize their application to cognitive radio and develop a framework from within which they can be useful. We describe how our generic cognitive engine can tackle problems such as capacity maximization and dynamic spectrum access.     

Channelization for dynamic multi-frequency, multi-hop wireless cellular networks

Multi-hop relaying in cellular networks can greatly increase capacity and performance by exploiting the best available links to a base station. We envision an environment in which relay networks are dynamically formed when performance on the radio access network is degraded and then dissolved when the performance improves or the radio spectrum on which the relay network is operating is reclaimed. Each relay network operates on a different frequency band. Likewise, a relay network may channelize its frequency band to offer non-interfering links among the mobile nodes within a single relay network. We propose a set of algorithms used to form such relay networks on-demand. Each algorithm provides a simple and distributed frequency assignment scheme. We also propose two enhancements to improve network throughput of resulting relay networks. We evaluate these algorithms in terms of the overhead of the relay network formation. The evaluation results show that having nodes outmost from the BS initiate route discovery first is the best approach for reducing the formation overhead. The results also show that there is a large increase in throughput when using multiple frequencies in a relay network. Further, the performance of the network using multiple frequencies based on our simple frequency assignment is very close to that of a network using optimal frequency assignment.     

Adaptive power-controlled MAC protocols for improved throughput in hardware-constrained cognitive radio networks

Cognitive radios (CRs) are emerging as a promising technology to enhance spectrum utilization through opportunistic on-demand access. Many MAC protocols for cognitive radio networks (CRNs) have been designed assuming multiple transceivers per CR user. However, in practice, such an assumption comes at the cost of extra hardware. In this paper, we address the problem of assigning channels to CR transmissions in single-hop and multi-hop CRNs, assuming one transceiver per CR. The primary goal of our design is to maximize the number of feasible concurrent CR transmissions, and conserve energy as a secondary objective, with respect to both spectrum assignment and transmission power subject to interference constraint and user rate demands. The problem is formulated under both binary-level and multi-level spectrum opportunity frameworks. Our formulation applies to any power-rate relationship. For single-hop CRNs, a centralized polynomial-time algorithm based on bipartite matching that computes the optimal channel assignment is developed. We then integrate this algorithm into distributed MAC protocols that preserve fairness. For multi-hop ad hoc CRNs, we propose a novel distributed MAC protocol (WFC-MAC) that attempts to maximize the CRN throughput, assuming single transceiver radios but with “dual-receive” capability. WFC-MAC uses a cooperative assignment that relies only on information provided by the two communicating users. The main novelty in WFC-MAC lies in requiring no active coordination with licensed users and exploiting the dual-receive capability of radios, thus alleviating various channel access problems that are common to multi-channel designs. We conduct theoretical analysis of our MAC protocols, and study their performance via simulations. The results indicate that compared with CSMA/CA variants, our protocols significantly decrease the blocking rate of CR transmissions, and hence improve network throughput.     

COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - The Throughput Potential of Cognitive Radio: A Theoretical Perspective

Cognitive radios are promising solutions to the problem of overcrowded spectrum. In this article we explore the throughput potential of cognitive communication. Different interpretations of cognitive radio that underlay, overlay, and interweave the transmissions of the cognitive user with those of licensed users are described. Considering opportunistic communication as a baseline, we investigate the throughput improvements offered by the overlay methods. Channel selection techniques for opportunistic access such as frequency hopping, frequency tracking, and frequency coding are presented. The trade-off between regulation and autonomy inherent in the design and performance of cognitive networks is examined through a simple example, which shows that the optimal amount of licensing is equal to the duty cycle of the traffic arrivals