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 x 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.

Spectrum sensing is required for many cognitive radio applications, including spectral awareness, interoperability, and dynamic spectrum access. Any approach is highly dependent on the signal environment in which it operates. Noise, multipath channel distortion, sample-rate offsets, and center-frequency mis-estimations can make feature-based signal detection difficult. Additionally, as these values change in a dynamic environment, statically-trained classifiers cannot track the evolving class statistics. Unsupervised learning allows a cognitive radio system to evolve its classifier as the radio environment evolves, without the need for expert-annotated signal samples. While this allows improved classifier performance, previous work has demonstrated that unsupervised techniques developed without security in mind suffer from classifier manipulation through transmission of carefully crafted signals that shift decision boundaries. This paper develops countermeasures to the class manipulation attacks that mitigates their efficacy, and shows the robustness of unsupervised learning for signal classification in an evolving RF environment.

Dynamic Spectrum Access technology is now well beyond the developmental stages that it was several years ago. Several prototype devices have been created and have been successfully demonstrated at various venues. Recently the FCC has approved the decision for moving ahead with allowing white space devices to operate in the same frequency bands as TV transmitters. Through rigourous testing the FCC has validated the fundamental concept of these DSA radios showing that they can avoid primary users and cause a minimal amount of interference in trusted wireless environments. However, wireless environments can not always be trusted to contain naturally occuring features. Malicious users may introduce false environments such as increased noise levels or emulated primary user signals, in order to fool the DSA device. These uncontrolled environments must be explored in order to develop and embed proper security protocols within these next-generation devices to prevent future security issues. Even in the case of IEEE 802.11, the commercial world found that the security threats were severely overlooked, causing developers to play catch up while many systems were, and still are, left vulnerable. In this paper, we provide a security analysis on a well established DSA radio developed by Shared Spectrum Company under the DARPA xG program. Our analysis demonstrates the unique security vulnerabilities associated with DSA protocols and expose the extremely low barrier required for a malicious user to exploit them if not security measures are in place. We identify several of these unique vulnerabilities and suggest methods for mitigating them.

Wireless distributed computing has several unique problems compared with currently well investigated wireless sensor networks. These problems include the impact of channel variation on power allocation, different traffic pattern with higher utilization, and more restricted delay constraints. This paper investigates the impact of communication channel condition on the average execution time of the computing task within wireless distributed computing networks (WDCN). It has been found that the delay performance of wireless distributed computing is influenced by both the average channel condition and the variation of channels. In addition, the impact of channel heterogeneity is also investigated to show the possibility of the optimal workload distribution for energy saving and robustness. Finally, a workload distribution approach combined with a power allocation scheme exploiting the spatial heterogeneity of the channel condition is proposed to balance energy efficiency and robustness.

Advanced wireless applications such as sensor networks involve a close interaction between the communication and computation processes that deliver the services under stringent power constraints. Wireless network distributed computing (WNDC) is a potential solution to reducing the power consumption per node as well as that of the network. In WNDC, a computational task is executed among a network of collaborative nodes in a distributed manner as against performing the same task on a single node. In addition to providing power savings, WNDC enables power demand-supply matching that allows for system operation under a constrained power supply such as solar power. This paper presents fundamental power efficiency analysis of WNDC. The conditions for achieving power demand-supply matching and positive network power savings under power and computational latency constraints are derived. The results show the impact of non-linearity in the computational system characteristics and the communication overhead on the power savings.

Abstract 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 (GAs) 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. Copyright © 2007 John Wiley & Sons, Ltd.