Low-Complexity Reconstruction of Covariance Matrix in Hybrid Uniform Circular Array

Spatial covariance matrix(SCM) is essential in many multi-antenna systems such as massive multiple-input multiple-output(MIMO). For multi-antenna systems operating at millimeter-wave bands, hybrid analog-digital structure has been widely adopted to reduce the cost of radio frequency chains.In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid multi-antenna systems. To address this issue, beam sweeping algorithm(BSA) which can reconstruct the SCM effectively for a hybrid uniform linear array, has been proposed in our previous works. However, direct extension of BSA to a hybrid uniform circular array(UCA)will result in a huge computational burden. To this end, a low-complexity approach is proposed in this paper. By exploiting the symmetry features of SCM for the UCA, the number of unknowns can be reduced significantly and thus the complexity of reconstruction can be saved accordingly. Furthermore, an insightful analysis is also presented in this paper, showing that the reduction of the number of unknowns can also improve the accuracy of the reconstructed SCM. Simulation results are also shown to demonstrate the proposed approach.

Co-channel interference rejection for MIMO-OFDM systems

A method based on the maximum a posteriori probability （MAP） criterion is proposed to estimate the channel frequency response （CFR） matrix and interference- plus-noise spatial covariance matrix （SCM） for multiple input and multiple output orthogonal frequency division multiplexing （MIMO-OFDM） systems. An iterative solution is proposed to solve the MAP-based problem and an interference rejection combining （IRC） receiver is derived to suppress co-channel interference （CCI） based on the estimated CFR and SCM. Furthermore, considering the property of SCM, i. e., Hermitian and semi-definite, two schemes are proposed to improve the accuracy of SCM estimation. The first scheme is proposed to parameterize the SCM via a sum of a series of matrices in the time domain. The second scheme measures the SCM on each subcarrier as a low-rank model while the model order can be chosen through the penalized-likelihood approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder:a prospective cohort study

Background The isolated rapid-eye-movement sleep behavior disorder(iRBD)is a prodromal condition of Lewy body disease including Parkinson’s disease and dementia with Lewy bodies(DLB).We aim to investigate the longitudinal evolution of DLB-related cortical thickness signature in a prospective iRBD cohort and evaluate the possible predictive value of the cortical signature index in predicting dementia-first phenoconversion in individuals with iRBD.Methods We enrolled 22 DLB patients,44 healthy controls,and 50 video polysomnography-proven iRBD patients.Participants underwent 3-T magnetic resonance imaging(MRI)and clinical/neuropsychological evaluations.We characterized DLB-related whole-brain cortical thickness spatial covariance pattern(DLB-pattern)using scaled subprofile model of principal components analysis that best differentiated DLB patients from age-matched controls.We analyzed clinical and neuropsychological correlates of the DLB-pattern expression scores and the mean values of the whole-brain cortical thickness in DLB and iRBD patients.With repeated MRI data during the follow-up in our prospective iRBD cohort,we investigated the longitudinal evolution of the cortical thickness signature toward Lewy body dementia.Finally,we analyzed the potential predictive value of cortical thickness signature as a biomarker of phenoconversion in iRBD cohort.Results The DLB-pattern was characterized by thinning of the temporal,orbitofrontal,and insular cortices and relative preservation of the precentral and inferior parietal cortices.The DLB-pattern expression scores correlated with attentional and frontal executive dysfunction(Trail Making Test-A and B:R=−0.55,P=0.024 and R=−0.56,P=0.036,respectively)as well as visuospatial impairment(Rey-figure copy test:R=−0.54,P=0.0047).The longitudinal trajectory of DLB-pattern revealed an increasing pattern above the cut-off in the dementia-first phenoconverters(Pearson’s correlation,R=0.74,P=6.8×10−4)but no significant change in parkinsonism-first phenoconverters(R=0.0063,P=0.98).The mean value of the whole-brain cortical thickness predicted phenoconversion in iRBD patients with hazard ratio of 9.33[1.16-74.12].The increase in DLB-pattern expression score discriminated dementia-first from parkinsonism-first phenoconversions with 88.2%accuracy.Conclusion Cortical thickness signature can effectively reflect the longitudinal evolution of Lewy body dementia in the iRBD population.Replication studies would further validate the utility of this imaging marker in iRBD.