IEEE Standards Supporting Cognitive Radio and Networks, Dynamic Spectrum Access, and Coexistence
TL;DR: A review of standardization activities for cognitive radio technologies and comments on prospects and issues for future standardization are provided.
Abstract: Cognitive radio techniques are being applied to many different communications systems. They hold promise for increasing utilization of radio frequencies that are underutilized today, allowing for improved commercial data services, and allowing for new emergency and military communications services. For example, these techniques are being considered by the U.S. FCC for communications services in unlicensed VHF and UHF TV bands. Although traditionally these techniques are closely associated with software-defined radios, many standards such as WiFi (IEEE 802.11), Zigbee (IEEE 802.15.4), and WiMAX (IEEE 802.16) already include some degree of CR technology today. Further advances are occurring rapidly. IEEE 802.22 will be the first cognitive radio-based international standard with tangible frequency bands for its operation. Standardization is at the core of the current and future success of cognitive radio. Industry stakeholders are participating in international standards activities governing the use of cognitive radio techniques for dynamic spectrum access and coexistence, next-generation radio and spectrum management, and interoperability in infrastructure-less wireless networks. This article provides a review of standardization activities for cognitive radio technologies and comments on prospects and issues for future standardization.
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TL;DR: A comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies is provided.
Abstract: Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.
336 citations
Cites background from "IEEE Standards Supporting Cognitive..."
...For instance, demand response control [9] adjusts the operation of electrical appliances in homes and businesses to reduce demands on the power grid....
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TL;DR: The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.
Abstract: Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.
287 citations
Cites background from "IEEE Standards Supporting Cognitive..."
...• Learning engine and reasoning engine will always update the decision making based on the previous action taken and is responsible [9] for wields experience and accumulates all previous history in the knowledge-base for prospect reference that is a key aspect of cognition cycle....
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...A reasoning CR is an improvement of the traditional CR with no interference that can automatically determine legal limits on secondary spectrum operation and is referred as an ‘Expert system’ [8,9]....
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TL;DR: This paper surveys the existing research work on cognitive networks, as well as related and enabling techniques and technologies, and provides a summary of artificial intelligence techniques that are potentially suitable for the development of cognitive networks and map them to the corresponding states of the cognition loop.
191 citations
TL;DR: The present document aims to provide a comprehensive and self-contained description of this research topic area, mainly focusing on communication protocols, spectrum decision issues, and future research directions according to the vision of the authors.
Abstract: Currently, the radio spectrum is statically allocated and divided between licensed and unlicensed frequencies. Due to this inflexible policy, some frequency bands are growing in scarcity, while large portions of the entire radio spectrum remain unused independently of time and location. Cognitive Radio is a recent network paradigm that aims a more flexible and efficient usage of the radio spectrum. Basically, it allows wireless devices to opportunistically access portions of the entire radio spectrum without causing any harmful interference to licensed users. The present document surveys the literature on Cognitive Radio. It aims to provide a comprehensive and self-contained description of this research topic area, mainly focusing on communication protocols, spectrum decision issues, and future research directions. It is a tutorial in nature and consequently does not require any previous knowledge about Cognitive Radio. Readers are only required to have some general background on wireless data networks. Emphasis is put on Cognitive Radio genesis, issues that must be addressed, related technologies, standardization efforts, the state of the art, and future research directions according to the vision of the authors.
167 citations
Cites background or methods from "IEEE Standards Supporting Cognitive..."
...[41] state that the type of the information to be accessed, QoS and security requirements for data streams should be considered....
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...In fact, coexistence has been considered for many years within IEEE standards, initially through tedious manual coordination and frequency planning, and CR techniques can be used to facilitate coexistence [41]....
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TL;DR: This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks, and classify the algorithms by presenting a thematic taxonomy of the current channel assignments algorithms in Cognitive radio networks.
Abstract: The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.
161 citations
Cites background from "IEEE Standards Supporting Cognitive..."
...The activities of such standards with respect to cognitive radio technology are critically reviewed in [30], which further highlights the issues for future standardization....
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References
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TL;DR: With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs and transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them.
Abstract: Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a radio knowledge representation language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio provides an ideal platform for the realization of cognitive radio.
9,238 citations
"IEEE Standards Supporting Cognitive..." refers background in this paper
..." [5] The definition was developed in the context of a software-defined radio (SDR), where the radio could easily be reconfigured to operate on different frequencies with different protocols by software reprogramming....
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TL;DR: This article discusses recent standardization efforts related to cognitive radio focusing on the work of IEEE Standards Coordinating Committee 41, formerly known as IEEE 1900, and some important tasks to be performed by the CR standardization community.
Abstract: This article discusses recent standardization efforts related to cognitive radio focusing on the work of IEEE Standards Coordinating Committee 41, formerly known as IEEE 1900. Some important tasks to be performed by the CR standardization community also are presented. These tasks will expedite the introduction of CR devices to the market while promoting a fair use of scarce radio resources. Some avenues for using the currently available standards for rapid deployment of CR devices, such as ISO standards, also are discussed.
166 citations
"IEEE Standards Supporting Cognitive..." refers background in this paper
...Although SCC 41 and IEEE 802.22 are the primary cognitive standards efforts today, many completed IEEE 802 standards already include CR/DSA-like capabilities or related building blocks....
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...These applications of CR/DSA techniques still can be viewed as coexistence....
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...Eventually people realized many of these techniques could be automated, and a second generation of standards resulted, including capabilities such as DFS and PC. Today CR/DSA standards are being developed that address issues such as coexistence....
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...CR/DSA techniques can permit additional (secondary) use of spectrum while protecting primary users....
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...In some regards, the application of CR/DSA techniques can be thought of as an evolution of coexistence techniques....
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TL;DR: This article describes recent advances in cognitive communications, which combines the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones.
Abstract: This article describes recent advances in cognitive communications. We combine the concepts of signal processing, communications, pattern classification, and machine learning to make dynamic use of the spectrum, such that the emanated signals do not interfere with the existing ones. Unlike other programs such as neXt Generation communications of the Defense Advanced Research Projects Agency, where radio scene analysis is performed to find the spectrum holes or the white space, we make use of the white, as well as the gray space for non- interfering signal transmission. We examine the possibility of employing machine perception and autonomous machine learning technologies to the autonomous design and analysis of air interfaces. The underlying premise is that a learning module will facilitate adaptation in the standard classification process so that the presence of new types of waveforms can be detected, features that best facilitate classification of the previously and newly identified signals can be determined, and waveforms can be generated by using the basis-set orthogonal to the ones present in the environment. Incremental learning and prediction allows knowledge enhancement as more snapshots of data are processed, resulting in improved decisions. Some of the contributions of this project include technological advances in signal detection, feature identification, signal classification, sub-space tracking, adaptive waveform design, machine learning, and prediction.
66 citations
"IEEE Standards Supporting Cognitive..." refers background in this paper
...They hold promise for increasing utilization of radio frequencies that are underutilized today, allowing for improved commercial data services, and allowing for new emergency and military communications services [1]....
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