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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.

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.

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.

Minimizing Energy Consumption Using Cognitive Radio

In this paper, we show how cognitive radio can help minimize energy consumption of a wireless mobile communication device. We propose an energy optimization framework using cognitive radio for a given quality of service requirement based on the channel and the radio capabilities. The cognitive radio not only adjusts modulation, coding, and radiated power, as with conventional adaptive modulation, but also adjusts component characteristics (e.g., power amplifier characteristics) so that the radio operates with the highest energy efficient possible way. Simulation results show that significant energy savings (up to 75%) can be achieved compared to conventional adaptive modulation. This framework also can be applied to optimize radio operations to achieve additional goals.

Green Communications: A Call for Power Efficient Wireless Systems

Telecommunication usage has skyrocketed in recent years and will continue to grow as developing world reaches to wireless as the communication medium of choice. The telecommunications world is only now addressing the significant environmental impact it is creating as well as the incredible cost on power usage. This realization has led to a push towards Green Communications that strives for improving energy efficiency as well as energy independence of telecommunications. A survey of existing metrics for energy efficiency is discussed with specific adaptations for a communication centric viewpoint. This paper reviews recent energy efficient advances made at specific point within the communications cycle such as components, network operation and topology, and incorporating renewable and alternative energy into base stations. We further survey several holistic approaches that illustrate the dependencies between layers of the communications stack and operation/deployment. These approaches include cross layer design, cognitive radio, and wireless distributed computing.

Virginia tech cognitive radio network testbed and open source cognitive radio framework

The Wireless @ VT research group has embarked on a effort to develop a unique testbed named the Virginia Tech Cognitive Radio Network (VT-CORNET), for the development, testing, and evaluation of cognitive engine techniques and cognitive radio network applications. An open cognitive radio network testbed provides the infrastructure for researchers at Virginia Tech and partner institutions to evaluate independently developed cognitive radio engines, sensing techniques, applications, protocols, performance metrics, and algorithms in a real world wireless environment, in contrast to a computer simulation or single node-to-single node environment.

System power consumption minimization for multichannel communications using cognitive radio

Power consumption has been a significant issue for many mobile and wireless devices, especially those with high rate applications. This paper presents a methodology and framework to minimize system power consumption for multichannel communications using cognitive radio (CR) based on the application quality of service requirement, the channel condition, and the radio capabilities and characteristics. The CR framework enables an adaptation process that is aware of the radio (component) capabilities and characteristics. This paper mathematically formulates a system power consumption minimization problem under a rate constraint for multichannel communications and develops numerical solutions. Simulation results show that the knowledge of the radio capabilities and characteristics can help to reduce system power consumption significantly (e.g., up to 55% for a multichannel system with Class A power amplifiers).

Energy consumption minimization for mobile and wireless devices - a cognitive approach

Energy consumption for mobile and wireless communication device, such as cell phones, has long been an important aspect for both designers and customers. This paper shows how a cognitive radio (CR) framework can help to reduce system energy consumption of a mobile and wireless communication device based on the application quality of service requirement, the channel condition, and the radio capabilities and characteristics. The CR framework enables not only adaptation of modulation, coding rate, coding gain, and radiated power as conventional adaptive modulation (AM) scheme, but also joint adjustment of radio component characteristics (e.g., power amplifier (PA) characteristics) to achieve high energy efficiency. A unified PA efficiency model characterizing theoretical Class A, Class B, and practical PAs is adopted and enables the analysis of the impact of different radio configurations and channel conditions on energy efficiency. Significant energy savings (up to 90%) using the proposed CR framework for systems with theoretical PAs and with a realistic PA can be achieved compared with the conventional AM approach in simulation. This framework can also be used to manage other radio resources.

A High Efficiency Outphasing Transmitter Structure for Wireless Communications

An outphasing radio transmitter can provide high linearity. The overall system efficiency for an outphasing transmitter is constrained by the combiner at the output stage. The combiner efficiency can be as low as 20% for an OFDM signal due to its high peak-to-average power ratio (PAPR). We propose a digital signal processing approach to improve the average system efficiency of an outphasing transmitter from 20% to about 80%. The efficiency improvement is achieved by combining PAPR reduction technique with the outphasing transmitter structure. The efficiency achieved by this approach can be optimized according to the probability density functions of the input signals. This new approach is insensitive to the mismatch of the two branches in the outphasing structure.