Research On High Efficient Spectrum Sensing And Analysis In Complex Electromagnetic Environment

Communication technology is evolving towards the direction of integra-tion in terrestrial, marine and aerial conditions. The in-depth perception of global and new radio environment becomes an essential part of national radio monitoring strategy. Moreover, the frequency hopping time interval in some modern wireless communication systems can be less than a millisecond. To capture and analyze the consequent signals, spectrum monitoring techniques should accomplish spectrum sensing in the scale of sub-millisecond. The ability to sense the highly complicated and dynamic spectrum has become a key indi-cator of national strength and is the basis of spectral depth analysis. In addition,the development of wireless communication is facing a major opportunity for changes, i.e., changing from the traditional static spectrum management and us-age strategy into a dynamic and efficient management and usage strategy, which also requires the depth analysis of the electromagnetic environment. However,our related researches in China are still in the early stage, especially a large step behind in capturing and processing of short burst signals, weak and overlapped signals. Therefore, with the rapid demand of the rapid spectrum sensing and depth analysis, we starts from the shortcomings of the existing technologies and then investigates the fast spectrum sensing method, the weak signal and overlapped sources modulation classification techniques. The main contents of this paper are listed as follows:(1) Space-Time Correlation Based Fast Regional Spectrum SensingIn this paper, a space-time correlation based fast regional spectrum sensing(RSS) scheme is proposed to reduce the time and energy consumption of tradi-tional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial cor-relation among them. In each group, some representative meshes are selected as detecting meshes (DMs) using a multi-center mesh (MCM) clustering algo-rithm, while other meshes (EMs) are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle.Thus, detecting fewer meshes saves the sensing consumption. Since two in-dependent estimation processes may provide contradictory results, minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the 51% consumption of traditional sensing method with acceptable sensing performance.(2) Multi-center Cooperative Estimation based Fast Spectrum SensingTo reduce the huge consumption of traditional sensing, a multi-centers es-timation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group,the states of other channels (estimated channels, ECs) are estimated according to their correlations with the DCs and the dependence on history to save sensing time. Specifically, number of groups (Ng) and number of DCs in each group(NDC) can be adjusted jointly to improve sensing performance. Moreover, two Hidden Markov Model (HMM) based estimation methods, namely joint esti-mation (JE) and cooperative estimation (CE), are formulated. In JE, the DCs are modeled as the observed vectors and utilized jointly to estimate ECs' states.While in CE, each DC estimates ECs' states separately and a weight-based co-operative algorithm is designed to merge their results. Tested with real-world measurement data, results show the reduced sensing consumption is consider-able at the expense of slight sensing accuracy loss. On these bases, it is signifi-cant to note that NDC should be adjusted according to sensing consumption to optimize performance.(3) Feature based Modulation Classification using Multiple Cumulants and Antenna Array for low SNR signalsAutomatic modulation classification (AMC) conducted by a single receiver plays a crucial role in spectrum monitoring and signal interception. To improve the accuracy of feature based AMC, a novel multi-cumulant based modula-tion classification scheme using uniform linear array is proposed in this paper.Moreover, two methods are formulated to combine the signal from different an-tenna branches, i.e., DOAC (Direction of arrival estimation based Combination)and CC (Cooperative Combination). With an estimate of the incident angle of the signal, DOAC combines signals from different branches using maximum ratio combining. CC calculates the feature value of each branch independently and utilizes the average feature value of all branches for classification. Simula-tion results prove that using multiple cumulants yields performance gain over traditional methods using a single cumulant. Moreover, the influence of an-tenna number and sample length on performance is also explored.(4) Automatic Modulation Classification of Overlapped Sources Using Multiple CumulantsAutomatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper,we propose a feature based AMC framework for multiple overlapped sources.The framework firstly separates the overlapped sources via blind channel esti-mation and then conducts a novel maximum likelihood based multi-cumulant classification (MLMC) for each of the sources. MLMC employs multiple cu-mulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification on condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations named FICA and NICA are presented to facilitate signal separation process. Extensive simula-tions are also conducted to verify the validity and superiority of the proposed framework and MLMC algorithm. Moreover, when considering the high com-plexity of multi-antenna scenario, we propose a single cumulant based maxi-mum likelihood classification (CMLC) using only one antenna for overlapped sources. The sample estimate of cumulant is utilised for classification and clas-sification decision is made by maximising the asymptotic distribution function of the cumulant. Note that there is no channel matrix estimation and overlapped sources separation in single antenna scenario, the complexity is largely reduced.Simulation results prove the superior performance of CMLC over existing al-gorithms.Finally, we summarize this paper and introduce the future work.