Cognitive network

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

Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking

Abstract: This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complementary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engine's performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engine's operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications.

On physical layer security for cognitive radio networks

Abstract: In this article we explore the security issues on physical layer for cognitive radio networks. First we give an overview on several existing security attacks to the physical layer in cognitive radio networks. We then discuss the related countermeasures on how to defend against these attacks. We further investigate one of the most important physical layer security parameters, the secrecy capacity of a cognitive radio network, and study the outage probability of secrecy capacity of a primary user from a theoretical point of view. Furthermore, we present performance results for secrecy capacity and outage probability between a node and its neighbors. Our work summarizes the current advances of the physical layer security and brings insights on physical layer security analysis in cognitive radio networks.

Machine Learning for Cognitive Network Management

Abstract: Over the last decade, a significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing networks. Autonomic networking calls for automated decisions for management actions. This can be realized through a set of pre-defined network management policies engineered from human expert knowledge. However, engineering sufficiently accurate knowledge considering the high complexity of today's networking environment is a difficult task. This has been a particularly limiting factor in the practical deployment of autonomic systems. ML is a powerful technique for extracting knowledge from data. However, there has been little evidence of its application in realizing practical management solutions for autonomic networks. Recent advances in network softwarization and programmability through SDN and NFV, the proliferation of new sources of data, and the availability of lowcost and seemingly infinite storage and compute resource from the cloud are paving the way for the adoption of ML to realize cognitive network management in support of autonomic networking. This article is intended to stimulate thought and foster discussion on how to defeat the bottlenecks that are limiting the wide deployment of autonomic systems, and the role that ML can play in this regard.

Joint power control and beamforming for cognitive radio networks

Abstract: We consider a secondary usage of spectrum scenario where a secondary network coexists and/or shares the radio spectrum with a primary network to which the spectrum is licensed. The secondary network uses cognitive radio technology to make opportunistic access of the unused and/or underutilized portions of the spectrum. For such a system, we study the problem of joint power control and beamforming with the objective of minimizing the total transmitted power of the cognitive network such that the received interferences at primary users remain below a threshold level as well as the secondary users who are admitted in the system are guaranteed with their signal to interference plus noise ratio (SINR) requirements. Two iterative algorithms, one based on weighted least square (WLS) approach and the other based on admission control for secondary users, are provided to meet the strict interference protection requirement of the primary users. A numerical study is performed to show the convergence behavior of the iterative algorithms.

Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions

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.