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Cognitive Radio Spectrum-Sharing Technology
Published in Krzysztof Iniewski, Wireless Technologies, 2017
Danijela Cabric, Robert W. Brodersen
Spectrum sensing addresses SNR regimes that are often much below the ones encountered in UWB channels. Similar to impulse detection, the optimal way for any signal detection is a matched filter (Figure 5.17), since it maximizes received SNR. However, a matched filter effectively requires demodulation of a primary user signal. This means that the CR has a priori knowledge of primary user signal at both physical layer (PHY) and MAC layers, e.g., modulation type and order, pulse shaping, packet format, etc. Such information might be prestored in a memory, but the cumbersome part is that for demodulation the CR has to achieve coherency with primary user signal by performing timing and carrier synchronization, even channel equalization. This is still possible, since most primary users have pilots, preambles, synchronization words, or spreading codes that can be used for coherent detection. For example, a TV signal has narrowband pilot for audio and video carriers; code division multiple access (CDMA) systems have dedicated spreading codes for pilot and synchronization channels; OFDM packets have preambles for packet acquisition. The main advantage of matched filter is that due to coherency, it requires minimal time to achieve high processing gain, since only T ~ O(1/SNR) samples are needed to meet a given probability of detection constraint. However, a significant drawback of a matched filter is that a CR would need a dedicated receiver for every primary user class.
The Matched Filter Receiver and the Ambiguity Function
Published in Bassem R. Mahafza, Introduction to Radar Analysis, 2017
where T0 = τ0 +t0 and R¯xix(t−T0) is a cross-correlation between xf(t) and x(T0−t). Therefore, the matched filter output can be computed from the cross-correlation between the radar received signal and a delayed replica of the transmitted waveform. If the input signal is the same as the transmitted signal, the output of the matched filter would be the autocorrelation function of the received (or transmitted) signal. In practice, replicas of the transmitted waveforms are normally computed and stored in memory for use by the radar signal processor when needed.
Machine Learning Techniques for Wideband Spectrum Sensing in Cognitive Radio Networks
Published in Mahmoud Elkhodr, Qusay F. Hassan, Seyed Shahrestani, Networks of the Future, 2017
Sufi Tabassum Gul, Asad Ullah Omer, Abdul Majid
Matched filter–based sensing is a coherent detection and is applicable only when knowledge of a primary signal is a priori to the SU. It is so because CR has to demodulate the received signal, hence different features necessary for correct demodulation are required. If this information is corrupted and becomes non-coherent, then CR could give poor results. A matched filter is a linear filter, and it maximizes the signal-to-noise ratio (SNR). In this technique, a filter whose impulse response, which is modified from a reference signal, is convolved with the received signal. This impulse response is modified by taking a mirrored replica of the reference signal and then shifting it in the time domain. For each PU, it requires a dedicated receiver, which is a major disadvantage of a matched filter. For optimal detection of the received signal, O(1SNR) samples are required [18]. The block diagram of matched-filter implementation is shown in Figure 3.4.
Maritime cognitive radio spectrum sensing based on multi-antenna cyclostationary feature detection
Published in International Journal of Electronics, 2020
Jingbo Zhang, Feng Ran, Da Liu
At present, some useful research results have been obtained for the spectrum sensing method in the land wireless communication environment. Some spectrum sensing methods have been proposed in (Y¨ucek & Arslan, 2009), such as energy detection, cyclostationary detection and matched filter detection. Energy detection is the most widely used blind detector because it requires a priori knowledge of very few signals. However, it is sensitive to noise anomalies, and detection performance depends on accurate noise power (Tandra & Sahai, 2008). Matched filter detection requires a large amount of prior knowledge of the signal, which is often difficult to satisfy. The cyclostationary feature detection does not require prior knowledge of the signal and is not affected by the noise wall problem. In addition, it can detect the modulation type, symbol rate, carrier frequency and other characteristics of the signal (Gardner, Brown, & Chen, 1987), so it has received extensive attention. The typical cyclostationary feature detection method is calculated by the Fourier cycle spectral density (Tkachenko, Cabric, & Brodersen, 2007) or its multiple Loève model (Haykin, Thomson, & Reed, 2009), and the computational complexity is relatively large. Literature (Jeon, Jeong, Han, Ko, & Song, 2008) proposed a method to reduce the computational complexity by using energy detection results to trigger cyclostationary feature detection.
From Least Squares to Signal Processing and Particle Filtering
Published in Technometrics, 2018
Nozer D. Singpurwalla, Nicholas G. Polson, Refik Soyer
Preceding the work of Bode and Shannon (1950), and that of Zadeh and Ragazzini (1950), is the unpublished work of North (1943), and the published work of van Vleck and Middleton (1946) on what is known as ”matched filters” (see Turin 1960). Underlying the idea of a matched filter is the requirement that a signal s(t) be a deterministic and of known waveform, as opposed to a stochastic process. When such is the case, the smoothing filter h(t) is easy to specify via an inverse Fourier transform. Such a filter is known as a matched filter because it is matched to s(t), and its virtue is an enhanced ability to detect the presence or the absence of a signal s(t). With s(t) fully specified, the matched filter can be seen as a stepping stone to a structured stochastic process like the Kalman filter.
The Design and Analysis of the Sidelobe Reduction Filter for Polyphase Frank Codes
Published in IETE Journal of Research, 2023
A Balaraju, S. P. Singh, Dhiraj Sunehra
The matched filter, which is generally used in radar receivers is a correlator. Therefore, the output of the filter is the correlation between the received signal and the replica of the transmitted signal. The matched filter is used in the receiver of the radar for maximizing signal-to-noise ratio that increases the detection capability of the radar system. Let the Frank codes be used as radar signals and they are denoted by X(n) and the length of the sequence “N” is represented as The output of the matched filter of the above sequence is given by