Some Fundamental Limits on Cognitive Radio

By Anant Sahai, N. Hoven, Rahul Tandra. 2004

754 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4831a6274bbd8e847bae703d505e92d7171a407b

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Citations in the corpus

Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks (2008, background): “In order to improve spectral utilization, cognitive radio (CR) technology has been proposed as a potential communication paradigm [1].”
Cooperative Sensing among Cognitive Radios (2006, methodology): “Furthermore, we have used the conservative suburban model fit from [17] where the correlation is always positive.”
Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks (2008, methodology): “The energy detection method is optimal for detecting any unknown zero-mean constellation signals [14].”
Cooperative spectrum sensing in cognitive radio networks (2005, background): “Thus one of the major challenges that confronts this technology is: how do the cognitive (unlicensed) radios sense the presence of the primary (licensed) user? One may expect this to be trivial but as shown in [3], there are fundamental limits to the detection capabilities of CR networks.”
IEEE 802.22: An Introduction to the First Wireless Standard based on Cognitive Radios (2006, background): “Since these measurements must be made in low SNR levels, it is assumed that the detection of TV signals is done in a noncoherent manner, that is, no synchronization is assumed [19][20].”
On Capacity Under Receive and Spatial Spectrum-Sharing Constraints (2007, background): “This includes the problem of detecting currently empty spectrum (see, e.g., [6]–[8]) and game-theoretic approaches to the mechanisms and rules according to which multiple systems can dynamically arrange spectrum usage (see, e.g., [9]–[11]).”
SNR Walls for Signal Detection (2008, background): “However, in order to guarantee noninterference with potentially hidden primary receivers and deeply faded channels, the secondary system needs to be able to detect the presence/absence of very weak primary signals [5]–[7].”
Fundamental limits on detection in low SNR under noise uncertainty (2005, background): “Further, as the CLT is a statement about convergence in distribution, we cannot tell anything about the behavior of the moments of the noise.”
Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks (2008, background): “Therefore, it becomes a critical issue in cognitive radio to detect the presence of primary signals reliably and quickly [3].”
Collaborative wideband sensing for cognitive radios (2008, methodology): “The more sophisticated techniques (coherent detection and feature detection) can be used for sensing refinement or signal classification if more a priori knowledge about the primary signal is available [14], [15].”
A Survey of Dynamic Spectrum Access (2007, unknown): “environment with noise uncertainty, shadowing, and fading is a challenging problem as articulated in [20].”
Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks (2009, methodology): “If prior knowledge of the PU signal is unknown, the energy detection method is optimal for detecting zero-mean constellation signals [13].”
What is a Spectrum Hole and What Does it Take to Recognize One? (2009, background): “However, it turns out that interference temperature alone is not enough t o understand the concept of a spectrum hole [6]–[8].”
Signal Processing in Cognitive Radio (2009, methodology): “Here we elaborate the stringent signal processing requirements for detection of spatial spectrum holes from a geographic perspective as in [6] and [7].”
Dynamic Spectrum Access: Signal Processing, Networking, and Regulatory Policy (2006, unknown): “While classic signal detection techniques exist in the literature, detecting primary transmitters in a dynamic wireless environment with noise uncertainty, shadowing, and fading is a challenging problem as articulated in [24].”
Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks (2008, background): “, only the local noise power is known), then the energy detector is optimal [8].”