https://www.cs.montana.edu/courses/csci566/Ref/CR-Survey.pdf

NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey

Cognitive radio networks will provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. However, CR networks impose challenges due to the fluctuating nature of the available spectrum, as well as the diverse QoS requirements of various applications. Spectrum management functions can address these challenges for the realization of this new network paradigm. To provide a better understanding of CR networks, this article presents recent developments and open research issues in spectrum management in CR networks. More specifically, the discussion is focused on the development of CR networks that require no modification of existing networks. First, a brief overview of cognitive radio and the CR network architecture is provided. Then four main challenges of spectrum management are discussed: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility.

/pdf/a-survey-on-spectrum-management-in-cognitive-radio-networks-p8ekergqia.pdf

A survey on spectrum management in cognitive radio networks

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.

/pdf/advances-in-cognitive-radio-networks-a-survey-3gp2nvo27m.pdf

Advances in cognitive radio networks: A survey

Cognitive radio (CR) is the enabling technology for supporting dynamic spectrum access: the policy that addresses the spectrum scarcity problem that is encountered in many countries. Thus, CR is widely regarded as one of the most promising technologies for future wireless communications. To make radios and wireless networks truly cognitive, however, is by no means a simple task, and it requires collaborative effort from various research communities, including communications theory, networking engineering, signal processing, game theory, software-hardware joint design, and reconfigurable antenna and radio-frequency design. In this paper, we provide a systematic overview on CR networking and communications by looking at the key functions of the physical (PHY), medium access control (MAC), and network layers involved in a CR design and how these layers are crossly related. In particular, for the PHY layer, we will address signal processing techniques for spectrum sensing, cooperative spectrum sensing, and transceiver design for cognitive spectrum access. For the MAC layer, we review sensing scheduling schemes, sensing-access tradeoff design, spectrum-aware access MAC, and CR MAC protocols. In the network layer, cognitive radio network (CRN) tomography, spectrum-aware routing, and quality-of-service (QoS) control will be addressed. Emerging CRNs that are actively developed by various standardization committees and spectrum-sharing economics will also be reviewed. Finally, we point out several open questions and challenges that are related to the CRN design.

Cognitive radio networking and communications: an overview

Dynamic spectrum access stands as a promising and spectrum-efficient communication approach for resource-constrained multihop wireless sensor networks due to their event-driven communication nature, which generally yields bursty traffic depending on the event characteristics. In addition, opportunistic spectrum access may also help realize the deployment of multiple overlaid sensor networks, and eliminate collision and excessive contention delay incurred by dense node deployment. Incorporating cognitive radio capability in sensor networks yields a new sensor networking paradigm (i.e., cognitive radio sensor networks). In this article the main design principles, potential advantages, application areas, and network architectures of CRSNs are introduced. The existing communication protocols and algorithms devised for cognitive radio networks and WSNs are discussed along with the open research avenues for the realization of CRSNs.

/pdf/cognitive-radio-sensor-networks-51zpbxlxld.pdf

Cognitive radio sensor networks

In a cognitive radio (CR) network, MAC-layer configuration involves determining a common set of channels to facilitate communication among participating nodes. Further, the availability of multiple channels and frequent channel switches add to the complexity of route selection. Knowledge of the global network topology can be used to solve the above-described problems. In this paper, we propose a distributed algorithm for gathering global network topology information for a CR network. We outline approaches that utilize the gathered topology information for MAC-layer configuration and efficient routing of packets. In addition, situation awareness is achieved by sharing the physical location information among the nodes in the network. The proposed algorithm determines the global network topology in O(N2 ) timeslots, where N is the maximum number of nodes deployed. With 80 available channels for communication, the algorithm terminates within 0.8 second

/pdf/control-channel-based-mac-layer-configuration-routing-and-53lb9dpzoz.pdf

Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks

https://www.utdallas.edu/~neerajm/publications/journals/discovery.pdf

Time-efficient distributed layer-2 auto-configuration for cognitive radio networks

On neighbor discovery in cognitive radio networks

Cognitive radios CR have the capability to dynamically adapt to local spectrum availability. In a network comprised of CR-enabled devices, layerauto-configuration involves determining a common set of channels also referred to as the global channel set to facilitate communication among participating nodes. This is a unique challenge as nodes in the CR network are unaware of who their neighbors are, and also, the channels on which they can communicate among themselves. In this paper, we propose a time-efficient distributed algorithm for layerautoconfiguration for a CR network. Our algorithm finds the global channel set in timeslots, where is the maximum number of nodes deployed, is the maximum number of available channels for communication and is the diameter of the network. Assuming all nodes are aware of and , we present both diameter-aware and diameter-unaware versions of the algorithm. For highly sparse networks like linear chain topology where , with ! and #"! , the diameteraware configuration protocol terminates within "%$'&)(+*-,/.0$ and the diameter-unaware version terminates within $'& ('*-,/.0$ .

Time-Efficient Layer-2 Auto-Configuration for Cognitive Radios.

Cognitive Radios (CR) [4] enable flexible and improved radio spectrum utilization by allowing a group of CR nodes to utilize unused channels without interference with the channels' owners [1]. A CR node has a set of available (wireless) channels and this set varies with time, location and activities of the primary (licensed) users. Determining a node's neighborhood and common channels for communication is non-trivial. We propose several efficient distributed algorithms for node discovery and configuration (NDC) for various CR node models. Krishnamurthy et al. [3] proposed a solution for NDC without a control channel. In their model, each node has a single transceiver. If N is the maximum number of nodes and M is the maximum number of channels, their algorithm uses 2NM timeslots for NDC. Other works assume a common control channel, or every node is equipped with a separate radio interface for each channel or an identical frequency distribution at each node. Please see [2] for a thorough discussion of related work. Here, we extend the algorithms of [3] to other CR node models.

Srinivasan Krishnamurthy

Papers

Control channel based MAC-layer configuration, routing and situation awareness for cognitive radio networks

Time-efficient distributed layer-2 auto-configuration for cognitive radio networks

On neighbor discovery in cognitive radio networks

Time-Efficient Layer-2 Auto-Configuration for Cognitive Radios.

Brief announcement : Synchronous distributed algorithms for node discovery and configuration in multi-channel cognitive radio networks