Timothy R. Newman

A Survey of Artificial Intelligence for Cognitive Radios

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) techniques. To help researchers better understand the practical implications of AI to their CR designs, this paper reviews several CR implementations that used the following AI techniques: artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). Factors that influence the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed. To provide readers with a more concrete understanding, these factors are illustrated in an extended discussion of two CR designs.

KUAR: A Flexible Software-Defined Radio Development Platform

In this paper, we present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. The KUAR hardware implementation and software architecture are discussed in detail. Radio configurations and applications are presented. Future research made possible by this flexible platform is also discussed.

Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications

On Nov. 4 2008, the Federal Communications Commission adopted rules for unlicensed use of television white spaces. The IEEE 802.22 Wireless Regional Area Networks (WRAN) standard is the first IEEE standard utilizing cognitive radio (CR) technology to exploit the television white space. A decision engine that is able to respond to the changes in the radio environment is necessary to efficiently exploit underutilized spectrum resources and avoid interfering with the licensed systems (e.g., TV services). This paper discusses the development of a case-based reasoning cognitive engine (CBR-CE) for the IEEE 802.22 WRAN applications. The performance of the CBR-CE is evaluated under various radio scenarios and compared to that of several multi objective search based algorithms, including the hill climbing search (HCS) and the genetic algorithm (GA). The simulation results show that the developed CBR-CE can achieve comparable utility with faster adaptation than the search based cognitive engines after appropriate training / learning. The learning process of the CBR is also simulated and discussed.

Designing and deploying a building-wide cognitive radio network testbed

Wireless communication technology is constantly advancing with the primary objective being to improve the quality of service for the end user. Cognitive radio is a technology capable of advancing wireless communications to the next generation of intelligent devices. Integrating cognition into wireless applications such as dynamic spectrum access, radio resource management, wireless distributed computing, and even traditional protocol stacks has already been shown to provide benefits related to the communications quality of service. The majority of cognitive radio related research has been limited to theoretical frameworks and simulations or in a few cases, demonstrating prototype DSA devices on a small scale. In order to continue advancing in this area, larger-scale experiments that are reproducible and able to be moved beyond theoretical simulations are required. Virginia Tech has built a testbed for software-defined and cognitive radio related research for the purpose of rapid next-generation communication system prototyping using a medium scale size network of flexible wireless nodes. In this article we present the details of the development, design decision rationale, and deployment of this testbed in hopes that it will be both used by the research community, and duplicated and improved in order to further the development of the many different facets of cognitive radio research.

Population Adaptation for Genetic Algorithm-based Cognitive Radios

Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.

Power Consumption Minimization for MIMO Systems — A Cognitive Radio Approach

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 × 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

Genetic algorithm-based optimization for cognitive radio networks

Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for a genetic algorithm to reach a decent solution substantially increases with system complexity, and thus techniques are needed that will help facilitate achieving adequate results over a short period of time.

Population adaptation for genetic algorithm-based cognitive radios

Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantially increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor, which represents the amount of change in the environment parameters since the previous cognition cycle.

Physical Layer Authentication Watermarks through Synthetic Channel Emulation

We present an authentication device allowing for the validation of wireless transmissions by means of a watermark signal applied at the physical layer, and demonstrate how the method may be applied to digital broadcast television signals. The novel watermarking approach presented conveys the authentication signal through explicit emulation of innocuous channel responses, further preventing Primary User Emulation attacks in Dynamic Spectrum Access theaters. The undesirable effects of the watermark signal design are removed by the receiver by traditional channel equalization practices, resulting in nearly zero impact to the bit error rate (BER) of the primary signal received. The proposed mechanism may be implemented without modification to existing Digital Television (DTV) transmission equipment using a retrofitting approach, and does not require the modification of existing receivers or protocols. A key benefit of the proposed method is that the authentication signal may be received at a BER much lower than the primary-signal, all within original transmission power and bandwidth constraints. We discuss physical layer details of the new watermarking method, and demonstrate how proven cryptographic authentication measures may be applied to the problem.

COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - An Agile Radio for Wireless Innovation

We present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. We describe the KUAR hardware implementation and software architecture and present example application of the KUAR to modulation, spectrum measurement, channel estimation, and rapid configuration and adaptation. We outline research directions enabled by the KUAR