Cognitive network

About: Cognitive network is a research topic. Over the lifetime, 4213 publications have been published within this topic receiving 107093 citations.

Reactive relay selection in underlay cognitive networks with fixed gain relays

Abstract: In a communication system with multiple cooperative relays, selecting the best relay utilizes the available spectrum more efficiently. However, selective relaying poses a different problem in underlay cognitive networks compared to the traditional cooperative networks due to interference thresholds to the primary users. In most cases, a best relay is the one which provides the maximum end-to-end signal to noise ratio (SNR). This approach needs plenty of instantaneous channel state information (CSI). The CSI burden could be reduced by partial relay selection. In this paper, a partial relay selection scheme is presented and analyzed for an underlay cognitive network with fixed gain relays operating in the vicinity of a primary user. The system model is adopted in a way that each node needs minimal CSI to perform its task. The best relay is chosen on the basis of maximum source to relay link SNR which then forwards the message to the destination. We derive closed form expressions for the received SNR distributions, system outage, probability of bit error and average channel capacity of the system. The derived results are confirmed through simulations.

Distributed Compressive Wideband Spectrum Sensing in Cooperative Multi-Hop Cognitive Networks

Abstract: This paper develops a distributed compressed spectrum sensing approach for cooperative wideband multi-hop cognitive radio (CR) networks where both primary users and CR users are active during the sensing stage. Due to the multi-hop nature, each CR needs to sense its individual spectral map that consists of both common spectral components from primary users and individualized spectral innovations arising from emissions of other CRs or interference in its local one-hop region. These CR-dependent spectral innovation components complicate the task of user cooperation for primary user detection. To cope with these difficulties, we adopt the compressed sensing approach at local CRs to attain high-resolution signal recovery at lower-than-Nyquist sampling rates. Each CR alternatively estimates the spectral occupancy of primary and CR users, and exchanges proper information with neighboring CRs to reach global fusion and consensus on the estimated primary user spectrum. The spectral orthogonality between primary users and CR users is exploited to improve the spectral estimation accuracy. Using only one-hop local communications, the proposed distributed algorithm converges fast to the globally optimal solution at low communication and computation load scalable to the network size.

A learner based on neural network for cognitive radio

Abstract: Intelligence is a very important characteristic for cognitive radios (CR). Design of cognitive engine and application of artificial intelligence (AI) techniques are key to the implementation of this characteristic. Machine learning is one of the disciples in AI. This paper will propose a scheme of cognitive engine design, and use a learning algorithm based on neural network (NN) to implement a learner in the cognitive engine. A multilayer perceptron (MLP) neural network model will be introduced to ensure the convergence of the network, and problems on stop condition and overfitting will also be discussed. Finally, performance of the algorithm will be analyzed by simulations.

Cognitive optical networks: Need, requirements and architecture

Abstract: This paper is proposing cognitive optical networks (COGNITION) as a new paradigm to address the increasing complexity of optical networks and provide an efficient autonomic as well as user-controlled network. Cognitive optical networks aim to introduce cognition on multiple planes (e.g. data plane, control plane, management plane, service plane, application layer) in order to perceive current network conditions, and then plan, decide and act on those conditions. The network can learn from these adaptations and use them to make future decisions, all while taking into account end-to-end goals. The ultimate goal of COGNITION is first to enhance optical network infrastructure by providing cognition on devices, systems and layers. The COGNITION architecture and its processes will be discussed throughout the paper.

Accomplishments of the DARPA SURAN Program

Abstract: The approach and primary achievements of the Survivable, Adaptive Networks (SURAN) Program are summarized. The purpose of the program is to research and develop network technology capable of supporting communication between computers and their users on the modern battlefield. Accomplishments include a comprehensive set of adaptive, robust, and secure network algorithms, an automated laboratory testbed, and an experimental, low-cost, packet radio network that integrates and demonstrates SURAN technology. Open issues for survivable network research are briefly discussed. >