A Hybrid Intelligence-Based Cognitive Engine

This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GA) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space.

Radio spectrum is the most valuable resource in wireless communication. The cognitive radio and cognitive based networking are transforming the static spectrum allocation based communication systems in to dynamic spectrum allocation. Cognitive radios are intelligent devices with ability to sense environmental conditions and can change its parameters according to the requirements to get the optimized performance at the individual nodes or at network level. This paper covers the basics and origin of software defined radio, cognitive radio, cognitive radio network, cognitive cycle, performance metrics and the concept of cross layer design. The performance metrics explain the node and network level performance measurements. This paper also covers the different network paradigms.

Journal of Computer Networks and Communications, 2012

The concept of cognitive radio (CR) focuses on devices that can sense their environment, adapt configuration parameters, and learn from past behaviors. Architectures tend towards simplified decision-making algorithms inspired by human cognition. Initial works defined cognitive engines (CEs) founded on heuristics, such as genetic algorithms (GAs), and case-based reasoning (CBR) experiential learning algorithms. This hybrid architecture enables both long-term learning, faster decisions based on past experience, and capability to still adapt to new environments. This paper details an autonomous implementation of a hybrid CBR-GA CE architecture on a universal serial radio peripheral (USRP) software-defined radio focused on link adaptation. Details include overall process flow, case base structure/retrieval method, estimation approach within the GA, and hardware-software lessons learned. Unique solutions to realizing the concept include mechanisms for combining vector distance and past f...

2016 IEEE Global Communications Conference (GLOBECOM), 2016

Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognition capabilities and intelligence is largely open. Deriving the intelligence capabilities of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different intelligence capabilities in a more cost-efficient way. In this paper, for the first time, we propose a data-driven methodology to quantitatively analyze the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance results on different metrics. Then we apply factor analysis on the performance results to identify and quantize the intelligence capabilities of the CR. More specifically, we present a case study consisting of sixty three different types of CRs. CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, and processing speed. Based on our methodology, we analyze the intelligence capabilities of the CRs through extensive simulations. Four intelligence capabilities are identified for the CRs through our analysis, which comply with the nature of the tested algorithms.

2007

This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GA) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space.

2015

Over the last several years the world of wireless communications has undergone some critical changes, which have brought it at the front position of international research and development interest, finally resulting in the introduction of a huge number of innovative technologies and associated products such as WiFi, WiMax, 802.20, 802.22, wireless mesh networks and software defined radio. Cognitive radio system uses a base of software defined radio technology and makes use of intelligent software packages that enhance their transceivers with the extremely smart properties of selfresponsiveness, flexibility and ability to learn. A cognitive radio system has the capability to change its operating parameters, monitor the results and, finally take actions, that is to say, decide to operate in a specific radio configuration (i.e. radio access technology, carrier frequency, modulation type, etc.), expecting to move the radio toward some optimized operational state. For such a process, lea...

2013

Radio spectrum is the most valuable resource in wireless communication. The cognitive radio and cognitive based networking are transforming the static spectrum allocation based communication systems in to dynamic spectrum allocation. Cognitive radios are intelligent devices with ability to sense environmental conditions and can change its parameters according to the requirements to get the optimized performance at the individual nodes or at network level. This paper covers the basics and origin of software defined radio, cognitive radio, cognitive radio network, cognitive cycle, performance metrics and the concept of cross layer design. The performance metrics explain the node and network level performance measurements. This paper also covers the different network paradigms.

Vehicular …, 2010

Abstract—Cognitive radio (CR) is an enabling technology for numerous new capabilities such as dynamic spectrum access, spectrum markets, and self-organizing networks. To realize this diverse set of applications, CR researchers leverage a variety of artificial intelligence (AI) ...

International Journal of Future Generation Communication and Networking

The emergence of Internet of Things and other applications of wireless communication has resulted in increase of air interference among various wireless devices. In upcoming time we will be connecting more and more devices wirelessly. In addition to an increase in number of devices, many devices also demand higher bandwidth. We have a limited spectrum available for communication and as the demand increases it creates more and more congestion in the available spectrum. Besides this scarcity of spectrum, it has been observed that all available frequencies in this spectrum are not efficiently utilized. Some frequency bands face congestion while others are underutilized. The solution of all these issues is Cognitive Radio. The fundamental theory of cognitive radio deals with the issues mentioned above and provides efficient utilization of available spectrum. In cognitive radio when a frequency is not utilized by primary user (Licensed user), it is allocated to secondary user (Unlicensed user) who can use the frequency until there is no primary user. For searching primary and secondary user we use spectrum sensing. Depending on the type of users and the environment, this spectrum sensing can be a time consuming task which can severely impact the QoS. To deal with this critical issue we use machine learning techniques, which predict spectrum holes in an available frequency band. This in turn reduces spectrum sensing time and power consumed in sensing. Among various Machine learning techniques, Artificial Neural Networks is one of the most popular and widely used technique. Unlike other Machine learning Techniques, Neural Network doesn't require prior knowledge of the system and in most cases it doesn't require the model to be retrained an every instance. These advantages makes it one of the most popular technique for cognitive radios. So far a lot of work has been done on implementing Artificial Neural Network models for predicting the most suitable frequency for a secondary user. In this paper a comprehensive survey has been conducted on various ANN techniques, its comparison with other machine learning techniques and discussion on various learning models to increase the decision making ability of cognitive radio's cognitive engine. ANN uses supervised learning and this paper compares it with other supervised learning techniques (like SVM) and also unsupervised learning techniques and statistical models. The paper provides detailed knowledge about what factors influence the use of ANN in cognitive engines and under certain conditions which ANN technique is most suitable.