SNR Walls for Signal Detection

By Rahul Tandra, Anant Sahai. IEEE Journal on Selected Topics in Signal Processing, 2008

1,622 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2b55d50de52e0323b1dc500cd92634b61f2f8cf4

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

What is a Spectrum Hole and What Does it Take to Recognize One? (2009, background): “It was shown in [16] that the traditional metrics can be suitably modified to characterize detector robustness to device-level uncertainties.”
Modulation in the Air: Backscatter Communication Over Ambient OFDM Carrier (2017, background): “Since the signal u[n] is white Gaussian and independent with the CSCG noise v[n], the log-likelihood ratio (LLR) for this binary hypothesis test is proportional to the received energy ∑Nc+D−1 n=L−1 |z[n]|2, which implies that the corresponding optimal noncoherent detector is the energy detector [29]”
Cognitive radio networking and communications: an overview (2011, background): “Furthermore, ED is susceptible to the uncertainty of noise power [20], which makes it challenging to determine the detection threshold.”
Cognitive Network Interference (2011, background): “Such uncertainty can be resulted from the unknown number of interferers and unknown locations of the interferers as well as channel fading, shadowing, and other uncertain environment-dependent conditions [11], [12].”
Spectrum Sensing for Cognitive Radio (2009, neutral): “Alternatively, we may resort to the use of cyclostationarity, which is an inherent property of digital modulated signals that naturally occur in the transmission of communication signals over a wireless channel [6], [ 7 ].”
Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems (2009, background): “This behavior is predicted by the SNR wall [28].”
Spectrum allocation in tiered cellular networks (2009, background): “First, F-ALOHA avoids transmission delays and increased RF sensitivity requirements for sensing frequency subchannels[11].”
Ten years of research in spectrum sensing and sharing in cognitive radio (2012, methodology): “With the help of the PU signal information, the SNR wall can be mitigated, but it cannot be eliminated [25].”
Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent Advances (2012, background): “It was shown in [14] that under this model, the number of samples LN that are required to meet a P D requirement, tends to infinity as the SNR 5 g 2 /s t 2 S 1r 2 2 1 / r2.”
Cooperative spectrum sensing in cognitive radio networks: A survey (2011, background): “More importantly, the detection performance cannot be improved by increasing the sensitivity, when the SNR of PU signals is below a certain level known as a SNR wall [8].”
Multiple antenna spectrum sensing in cognitive radios (2010, background): “, αdB, in receiver is normally 1-2 dB which due to the existence of interference can be much higher [10], [15].”
A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions (2010, background): “Thirdly, the noise/interference level may change with time and location, which yields the noise power uncertainty issue for detection [9–12].”
Advances in cognitive radio networks: A survey (2011, background): “In low signal-to-noise ratio (SNR) regimes where the noise power is very high, reliable identification of a primary user is even impossible [27].”
On Covert Communication With Noise Uncertainty (2016, methodology): “theorem [4], [5]: T (yw)|H0 ∼ N ( σ 2 w, 2σ 4 w/N ) , T (yw)|H1 ∼”
Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective (2009, neutral): “[88], [92].”
Spectrum sensing in cognitive radio (2016, methodology): “5.1 Model Uncertainties and SNR-Walls . . . . . . . . . . . . . . . . . . . . .”
Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “tainty will result in the SNR-wall problem [81] in which an infinite number of samples is not enough to guarantee”