Research On Robust Meticulous Anti-noise-level-difference Spectrum Sensing

Spectrum sensing technology is a key infrastructure element for researching and developing cognitive dynamic system. However, with the rapid development of wireless communications, electronic reconnaissance and electronic warfare technologies, the types of electronic equipment become endless, the range of various signal power levels gets wider, and communications system, detection methods and the interference pattern have become increasingly diverse. Obviously, these changes makes the electromagnetic environment more complex and brings many new demands to meet and focus continually for spectrum sensing technology.1. Robust Spectrum SensingIn practical cognitive dynamic systems, the requirement for real-time spectrum sensing in the case of lacking prior information, low signal to noise ratio and fading channels indeed poses a huge challenge to the robustness of classical spectrum sensing algorithms.2. Meticulous Spectrum SensingMore or less, new generation wireless communications, monitoring, reconnaissance and electromagnetic countermeasure systems tend to be ultra-wideband, short burst and multi interaction targets. These tendency of communications and electronics industry ask for the ability of real-time multi-user spectrum sensing in ultra-wideband, namely, meticulous spectrum sensing.3. Deep Spectrum SensingSome cognitive dynamic systems, such as cognitive radar, cognitive electronic warfare and cognitive radio, should be capable of deep analyzing to acquire the RF characteristics of arbitrary target signals, communications and interference scheme, modulation type, waveform shape, direction of arrival, position information and other critical signal information so that the systems can achieve joint optimum design. However, few current spectrum sensing technology and equipment domestic and overseas have the ability of deep spectrum sensing, which will also be a long-term evolution direction of spectrum sensing.4. Anti-Noise-Level-Difference Spectrum SensingAs the background noise is composed of ground noise, atmospheric noise, rain noise, man-made noise, interference noise and receiver thermal noise, the background noise level often shows a high dynamic range characteristics in time, space and frequency domains. To make cooperative spectrum sensing results more accurate and reliable, cooperative spectrum sensing technology must possess the ability to adapt the high dynamic range of background noise level at the cooperative nodes so that the adverse effects from cooperative nodes noise level difference will be weakened and overcome.In this paper, the new demands of spectrum sensing technology for dynamic system is studied, and the main research results are as follows:1. The system model of spectrum sensing is often established as a simple binary hypothesis problem, which overlooks the interaction between fading channel coefficients, symbol rate and white noise bandwidth. The system model built in this paper is ensured to be more accurate as it gives a full analysis for the correlation of the three factors above and considers the relevance of samples which is introduced by flat slow fading channel.2. Inspired by some frequency domain energy detection methods, a Power spectral density Split Cancellation method in use of scalar transformation is proposed in this paper to get robust spectrum sensing performance. This spectrum sensing algorithm makes use of asymptotic normality and independence of Fourier transform to get the stochastic properties of power spectral density. The algorithm takes the ratio of some PSD lines to all of the PSD lines as the detection statistics to detect signals. The decision threshold of this method is only related to the algorithm configuration and has nothing to do with noise power. Theoretical analysis and simulation results show that PSC algorithm is robust to noise uncertainty, and spectrum sensing performance does not vary with the ambient noise level of secondary users when signal to noise ratio is fixed. Meanwhile, PSC algorithm could offer high probability of detection at low probability of false alarm for a wide range of SNR in the white Gaussian noise and flat slow fading channel.3. Since conventional superheterodyne narrowband spectrum sensing technology can not quickly and accurately sense multiple targets within ultra-wideband, a novel multiuser spectrum sensing method based on bidirectional search of normalized power-spectrum(BSNP) is researched to fulfill technical requirements of meticulous spectrum sensing. Before performing bidirectional search of normalized power-spectrum, a new multichannel and polyphase Fast Fourier Transform structure is designed to get ultra-wideband spectrum. And then the BSNP takes the normalized spectrum of frequency slot as the detection statistics, and finds out all of occupied frequency slots within ultra-wideband by executing forward and reverse searches in sequence. The subband signal with irregular instantaneous power will be detected in the forward search, and the subband signal with comb spectrum will be distinguished in the reverse search.4. For the parameter difference of cooperative nodes, a cooperative spectrum sensing algorithm with broader applications is given. In the algorithm, each node upload local normalized spectrum to fusion center, which can overcome the cumulative effect of noise level dynamic changes in time, space and frequency domains of different nodes. The test statistic is calculated in the equal gain manner or optimal weighted average manner in fusion center, and this algorithm can effectively eliminate the influence on cooperative spectrum sensing performance for the background noise level high dynamic changes.5. Spectrum sensor is designed and implemented in this paper, and a highly precise synchronization data sampling methods based on spectrum sensor network is given. Finally, the single and cooperative performance of normalized spectrum method and energy spectrum sensing method is practically measured and compared.