Cognitive network

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

Dynamic Packet Size Optimization and Channel Selection for Cognitive Radio Sensor Networks

Abstract: Dynamic spectrum access has gained traction in wireless sensor networks (WSNs) because of the scarcity in spectrum caused by the proliferation of wireless devices and services, and it provides spectrum efficient communication for the WSNs. However, the communication between nodes in a cognitive radio sensor network (CRSN) is affected by the transmission power, fading, and interference with licensed users, and these factors hinder the data transmission between the energy constrained cognitive radio sensor nodes. Therefore, there is a need for an adaptive energy-efficient optimization scheme which takes into account the varying environment conditions. Since packet length plays a pivotal role in determining the performance of the network, packet size adaptation that is aware of the channel characteristics may bring about performance improvement. Furthermore, existing packet size optimization or channel selection schemes devised for WSNs and CR networks are not appropriate for the CRSN framework. In this paper, we devise a dynamic packet size optimization and channel selection scheme (DyPSOCS) for CRSNs. We employ a constrained Markov decision process (CMDP) to solve the optimization problem with quality of service (QoS) constraints. Simulation results show improvement in QoS performance as well as energy efficiency when compared to other schemes.

Intelligent Secure Communication for Cognitive Networks With Multiple Primary Transmit Power

Abstract: In this paper, we study an intelligent secure communication scheme for cognitive networks with multiple primary transmit power, where a secondary Alice transmits its secrecy data to a secondary Bob threatened by a secondary attacker. The secondary nodes limit their transmit power among multiple levels, in order to maintain the quality of service of the primary networks. The attacker can work in an eavesdropping, spoofing, jamming or silent mode, which can be viewed as the action in the traditional Q-learning algorithm. On the other hand, the system can adaptively choose the transmit power level among multiple ones to suppress the intelligent attacker, which can be viewed as the status of Q-learning algorithm. Accordingly, we firstly formulate this secure communication problem as a static secure communication game with Nash equilibrium (NE) between the main links and attacker, and then employ the Q-learning algorithm to select the transmit power level. Simulation results are finally demonstrated to verify that the intelligent attacker can be effectively suppressed by the proposed studies in this paper.

Interference Reducing Networks

Abstract: When cognitive radios operate in a network, each link's adaptations impact the decisions of other cognitive radios which spawns an interactive decision processes. The existence of these interactive processes could potentially limit the deployment of cognitive radios as it is difficult to guarantee that the resulting behavior will avoid a tragedy of the commons, much less provide optimal performance. This paper proposes a novel design framework that ensures that cognitive radio interactions are beneficial and reduce sum network interference with each adaptation. Five different approaches to implementing algorithms that satisfy this framework are presented ? two of which rely on collaboration and three which permit autonomous adaptations.

Distributed Spectrum Sensing in Cognitive Radio Networks with Fairness Consideration: Efficiency of Correlated Equilibrium

Abstract: Cooperative spectrum sensing improves the reliability of detection. However, if the secondary users are selfish, they may not collaborate for sensing. In order to address this problem, Medium Access Control (MAC) protocols can be designed to enforce cooperation among secondary users for spectrum sensing. In this paper, we investigate this problem using game theoretical framework. We introduce the concept of correlated equilibrium for the cooperative spectrum sensing game among non-cooperative secondary users and formulate the optimization problem for the case where secondary users have heterogeneous traffic dynamics. We show that the correlated equilibrium improves the system utility, as compared to the mixed strategy Nash equilibrium. While maximizing system payoff is important, fairness is also equally important in systems with dissimilar users. In order to address fairness issue, we propose a new fair social welfare correlated equilibrium, which maximizes the system utility and ensures that the less well-off users do not starve. We employ a no-regret learning algorithm for distributed implementation of the correlated equilibrium. Finally, we propose a neighbourhood based learning algorithm and show that it achieves better performance than the no-regret algorithm.

Cognitive optical network testbed: EU project CHRON [Invited]

Abstract: The aim of cognition in optical networks is to introduce intelligence into the control plane that allows for autonomous end-to-end performance optimization and minimization of required human intervention, particularly targeted at heterogeneous network scenarios. A cognitive network observes, learns, and makes informed decisions based on its current status and knowledge about past decisions and their results. To test the operation of cognitive algorithms in real time, we created the first operational testbed of a cognitive optical network based on the Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) architecture. In this experiment, an intelligent control plane, enabled by a cognitive decision system (CDS), was successfully combined with a flexible data plane. The testbed was used to test and validate different scenarios, demonstrating benefits obtained by network cognition, particularly lightpath establishment and a teardown scenario, improved failure restoration time, cognitive virtual topology reconfiguration, and a modulation format change.