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Spectrum Sensing of the DTV in the Vicinity of the Pilot Using 频谱感知的数字电视在附近的飞行员
Month Year John Doe, Some Company Month Year doc.: IEEE 802.22-yy/xxxxr0 John Doe, Some Company Spectrum Sensing of the DTV in the Vicinity of the Pilot Using Higher Order Statistics IEEE P802.22 Wireless RANs Date: 2007-08-07 Abstract We present an algorithm to detect the DTV Signals in Gaussian noise using Higher Order Statistics (HOS). The algorithm performs non-Gaussianity check in the frequency domain in the vicinity of the Pilot The Average results for all the 12 provided DTV Signals: At PFA = 0.04, For a capture of 5mS (continuous or staggered) PD = 0.9 at an SNR = -16.6 dB. For a capture of 10 mS (continuous or staggered), PD = 0.9 at SNR = -18.6 dB For a capture of 20 mS (continuous or staggered), PD = 0.9 at SNR = -21.6 dB For a capture of 40 mS (continuous or staggered), PD = 0.9 at SNR = -23.7 dB We use a 2048 point FFT to implement the algorithm which may be used for Spectrum Sensing as well as OFDMA demodulation. Because of the Constant False Alarm Rate (CFAR) nature of the detector, the threshold is built into the technique and NO Presetting is required. Fine adjustment is possible if needed. The proposed algorithm can be used for the detection of wireless microphone as well as DVB-T signals as well (future contribution).. DTV Signal Characteristics DTV Signal Gaussianity Check in Time and Frequency Domains Our technique relies on the non-Gaussianity of a signal to separate it from the Gaussian noise. In the Time Domain, DTV Signals show a Gaussian characteristic. However, in the Frequency Domain they are non-Gaussian. We will exploit this characteristic to detect the signals in AWGN. Signal or Noise Identification Using Higher Order Statistics (HOS) Use of Higher Order Statistics (HOS) is an efficient metric for detecting non-Gaussian signals at low SNRs. In particular, if we assume the noise to be a Gaussian random process, then all the Cumulants of Order greater than 2 are supposed to be nearly zero. Given a set of N sample
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