Texas A&M University

About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Gene. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.

Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks

Abstract: Cognitive radio technology has been proposed to improve spectrum efficiency by having the cognitive radios act as secondary users to opportunistically access under-utilized frequency bands. Spectrum sensing, as a key enabling functionality in cognitive radio networks, needs to reliably detect signals from licensed primary radios to avoid harmful interference. However, due to the effects of channel fading/shadowing, individual cognitive radios may not be able to reliably detect the existence of a primary radio. In this paper, we propose an optimal linear cooperation framework for spectrum sensing in order to accurately detect the weak primary signal. Within this framework, spectrum sensing is based on the linear combination of local statistics from individual cognitive radios. Our objective is to minimize the interference to the primary radio while meeting the requirement of opportunistic spectrum utilization. We formulate the sensing problem as a nonlinear optimization problem. By exploiting the inherent structures in the problem formulation, we develop efficient algorithms to solve for the optimal solutions. To further reduce the computational complexity and obtain solutions for more general cases, we finally propose a heuristic approach, where we instead optimize a modified deflection coefficient that characterizes the probability distribution function of the global test statistics at the fusion center. Simulation results illustrate significant cooperative gain achieved by the proposed strategies. The insights obtained in this paper are useful for the design of optimal spectrum sensing in cognitive radio networks.

The Protective Action Decision Model: Theoretical Modifications and Additional Evidence

Abstract: The Protective Action Decision Model (PADM) is a multistage model that is based on findings from research on people's responses to environmental hazards and disasters. The PADM integrates the processing of information derived from social and environmental cues with messages that social sources transmit through communication channels to those at risk. The PADM identifies three critical predecision processes (reception, attention, and comprehension of warnings or exposure, attention, and interpretation of environmental/social cues)--that precede all further processing. The revised model identifies three core perceptions--threat perceptions, protective action perceptions, and stakeholder perceptions--that form the basis for decisions about how to respond to an imminent or long-term threat. The outcome of the protective action decision-making process, together with situational facilitators and impediments, produces a behavioral response. In addition to describing the revised model and the research on which it is based, this article describes three applications (development of risk communication programs, evacuation modeling, and adoption of long-term hazard adjustments) and identifies some of the research needed to address unresolved issues.

The Structure and Function of Collective Constructs: Implications for Multilevel Research and Theory Development

Abstract: We address gaps in the multilevel organizational theory development literature by critically examining the structure and function of collective constructs. Structure emerges from interaction and can, over time, come to influence systems of interaction. Functions represent the causal outputs of constructs and provide a mechanism for integrating constructs across levels. We then discuss implications arising from this perspective and present a set of guidelines for multilevel research and theory development.