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Advanced Classification Of Ofdm And Mimo Signals With Enhanced Second Order Cyclostationarity Detection, Miao Shi
Dissertations
With the emergence of cognitive radio and the introduction of new modulation techniques such as OFDM and MIMO, the problem of Modulation Classification (MC) becomes more challenging and complicated. In the first part of the thesis, we explore the automatic modulation classification to blindly distinguish OFDM from single carrier signals. We use the fourth order cumulants; an approach which in the past has been also applied to classify single carrier signals. A blind OFDM parameter estimation scheme was then followed, which includes the estimation of number of subcarriers, CP length, timing and frequency offset and the oversampling factor for the …
Advanced Methods In Automatic Modulation Classification For Emerging Technologies, Hong Li
Advanced Methods In Automatic Modulation Classification For Emerging Technologies, Hong Li
Dissertations
Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate the reception performance. Although the average likelihood ratio test method can provide an optimal solution to the MC problem with unknown parameters, it suffers from high computational complexity and in some cases mathematical intractability. Instead, in this research, an array-based quasi-hybrid likelihood ratio test (qHLRT) algorithm is proposed, which depicts two major advantages. First, it is simple yet accurate enough …