A Low-complexity Successive Detection Method for OFDM Systems over Doubly Selective Channels

For OFDM systems, the time variation of a multipath channel results in inter-carrier interference (ICI). It leads to performance degradations. In reducing the problem, current successive detection methods cost very high computationalcomplexities. Among them, the minimum-mean squareerror successive detection (MMSE-SD) method has the best performance. This work proposes an improved data detection method with low complexity by integrating the techniques of Newton’s iterative matrix inversion method and the MMSE-SD method which considers the effects of signal-to-interference-plusnoise ratio (SINR). In order to efficiently integrate Newton’s and MMSE-SD method, we develop an effective scheme with low complexity for generating the initial values required by Newton’s method. Based on the new initial value scheme, we are able tosimplify the criterion of maximum SINR determination to an equivalent one with lower complexity. As a result, the proposed algorithm has a much lower complexity of O(N2) than O(N3) of the MMSE-SD algorithm, where N is the number of subcarriers. Moreover, simulation results in different channel conditions show that performances of the proposed MMSE-SD method are very close to the MMSE-SD method

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C-reactive Protein Positively Correlates With Metabolic Syndrome in Kidney Transplantation Patients

ObjectiveC-reactive protein (CRP) is an independent risk factor for renal allograft loss and predicts all-cause mortality in kidney transplantation patients. Metabolic syndrome has also been associated with increased mortality in kidney transplantation patients. The aim of this study was to investigate the relationship between CRP and metabolic syndrome in kidney transplantation patients.Materials and MethodsFasting blood samples were obtained from 55 kidney transplantation patients. Metabolic syndrome and its components were defined using diagnostic criteria from the International Diabetes Federation.ResultsIn total, 13 kidney transplantation patients (23.6%) had metabolic syndrome. Fasting CRP levels positively correlated with metabolic syndrome (p = 0.001). Univariate linear regression analysis indicated that fasting serum CRP values were positively correlated with body weight (p = 0.001), waist circumference (p = 0.008), body mass index (p < 0.001), and body fat mass (p = 0.042). Multivariate forward stepwise linear regression analysis of the significant variables showed that body mass index (β = 0.455, R2 = 0.207, p < 0.001) was an independent predictor of serum CRP levels in kidney transplantation patients.ConclusionCRP level positively correlated with metabolic syndrome in kidney transplantation patients. Body mass index was an independent predictor of serum CRP levels in kidney transplantation patients