Signal Detection And Parameter Estimation In Vehicular Cognitive Radios

In order to enable future wireless communication systems for more commercial useor public services, the development of system capacity has been an important researcharea in wireless communications. In recent years, the cognitive radio technology hasbeen widely researched as the most competitive candidate since it utilizes theopportunistic spectrum sharing to increase system capacity. The first and foremost stepin cognitive radios is to detect the usage of spectra of interest, i.e., to decide whetherthere are primary users. In order to avoid interferences to primary users and to savemore available time for cognitive radio users, the primary signal detection should beconducted under the cases of low signal to noise radio (SNR) and/or small observationperiod. Therefore，the challenge of detection problem in cognitive radios is to design thenew algorithm and strategy which should keep good performances under the scenariosof low SNR and small samples. In this dissertation, multiple vehicles are used ascarriers of detection devices to detect primary users in a short time. To meet differentdetection requirements and various detection environments, some detection techniquesand strategies are explored, including time-frequency domain detection techniques,cooperative strategies and spatial processing technologies. The main work andcontributions are presented as follows.1.Taking the ATSC digital TV signal as an example which is the primary signal inan open frequency band for cognitive radio users, various time-frequency domaindetection algorithms are proposed. Based on the modeling of the ATSC digital TVsignal, the pilot tone detection algorithm is proposed by analyzing the characteristic ofspectrum. The data field synchronization signal detection algorithm and the datasegment synchronization signal detection algorithm are designed by utilizing thespecific coding scheme of the ATSC digital TV signal. The cyclostationary detectionalgorithm is explored based on statistical properties of the ATSC digital TV signal. Theperformances of these algorithms are analyzed by numerical simulations which providethe reliable theoretical basis for the engineering realization.2. The vehicular spectrum sensing system is discussed and modeled in which multiple vehicular sensors cooperatively detect the ATSC digital TV signal. This systemconsists of signal generation，signal detection and data fusion. The part of signalgeneration is to produce received ATSC digital signals which are transmitted viacomplicated vehicular channels. Path loss, shadowing and multipath in variousenvironments are considered. Simple blind detection algorithms and specific detectionalgorithms could be chosen in the part of signal detection. The options of data fusion aresoft decision and hard decision. The classical channel coefficients and signal parametersare used, and different combinations of detection algorithms and data fusion techniquesare adopted in numerical simulations. The best detection strategies in different scenarioscould be chosen on the basis of simulation results.3.The eigenvalue-based detection algorithms are proposed with the multi-signalcharacteristic analysis. In new detection algorithms, the data matrix consists ofobservation samples received by multiple vehicles. Then, sample eigenvalues obtainedby the eigen decomposition are used to construct new detection statistics. Comparingwith the detection threshold, the result could be decided lastly. Two new detectionstatistics are proposed which have good performances in the cases of low SNR andsmall samples. Taking into full account of the fluctuation of sample eigenvalues, thecorresponding detection thresholds are derived. Numerical simulations illustrate that thenew algorithms could figure out primary users in a short period.4.The estimation of the number of primary users is explored. The reason whyperformances of traditional estimation algorithms break down when observationsamples are in starvation is analyzed. The required energy of a signal detected fromnoise is derived under the limiting condition. Two new detection statistics are proposedby utilizing sample eignvalues. Combining the sequential hypothesis testing method,these two new statistics are used to estimate the number of primary users. According totheoretical analyses and simulation results, new estimation algorithms perform betterthan traditional algorithms and the improved algorithms proposed in recent years in thecase of small samples.5. The direction of arrival (DOA) estimation algorithm is discussed. The reasonwhy performances of the subspace-based DOA estimation algorithms deteriorate in thecases of low SNR and small samples is analyzed. Then, a parametric iterative adaptivealgorithm based on amplitude and phase estimation (PIAA-APES) with low computational complexity is proposed. The subspace swap phenomenon which is themain reason of the performance breakdown of the subspace-based algorithms is avoidedin the proposed algorithm. The simulation results illustrate that the accuracy andresolution of the orientation detection of the new algorithm are better than those ofMUSIC algorithm. Moreover, this dissertation proposes a new synchronization strategyby utilizing the two-way beacon signal in the distributed array system to generatesynchronous local oscillator signals under their local clocks.