High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Children and adolescents in full-time special education : an epidemiological and magnetic resonance imaging study

The need for special education (SE) is increasing. The majority of those whose problems are due to neurodevelopmental disorders have no specific aetiology. The aim of this study was to evaluate the contribution of prenatal and perinatal factors and factors associated with growth and development to later need for full-time SE and to assess joint structural and volumetric brain alterations among subjects with unexplained, familial need for SE. A random sample of 900 subjects in full-time SE allocated into three levels of neurodevelopmental problems and 301 controls in mainstream education (ME) provided data on socioeconomic factors, pregnancy, delivery, growth, and development. Of those, 119 subjects belonging to a sibling-pair in full-time SE with unexplained aetiology and 43 controls in ME underwent brain magnetic resonance imaging (MRI). Analyses of structural brain alterations and midsagittal area and diameter measurements were made. Voxel-based morphometry (VBM) analysis provided detailed information on regional grey matter, white matter, and cerebrospinal fluid (CSF) volume differences. Father’s age ≥ 40 years, low birth weight, male sex, and lower socio-economic status all increased the probability of SE placement. At age 1 year, one standard deviation score decrease in height raised the probability of SE placement by 40% and in head circumference by 28%. At infancy, the gross motor milestones differentiated the children. From age 18 months, the fine motor milestones and those related to speech and social skills became more important. Brain MRI revealed no specific aetiology for subjects in SE. However, they had more often ≥ 3 abnormal findings in MRIs (thin corpus callosum and enlarged cerebral and cerebellar CSF spaces). In VBM, subjects in full-time SE had smaller global white matter, CSF, and total brain volumes than controls. Compared with controls, subjects with intellectual disabilities had regional volume alterations (greater grey matter volumes in the anterior cingulate cortex bilaterally, smaller grey matter volume in left thalamus and left cerebellar hemisphere, greater white matter volume in the left fronto-parietal region, and smaller white matter volumes bilaterally in the posterior limbs of the internal capsules). In conclusion, the epidemiological studies emphasized several factors that increased the probability of SE placement, useful as a framework for interventional studies. The global and regional brain MRI findings provide an interesting basis for future investigations of learning-related brain structures in young subjects with cognitive impairments or intellectual disabilities of unexplained, familial aetiology.

Convergence analysis of the Newton algorithm and a pseudo-time marching scheme for diffuse correlation tomography

We propose a self-regularized pseudo-time marching scheme to solve the ill-posed, nonlinear inverse problem associated with diffuse propagation of coherent light in a tissuelike object. In particular, in the context of diffuse correlation tomography (DCT), we consider the recovery of mechanical property distributions from partial and noisy boundary measurements of light intensity autocorrelation. We prove the existence of a minimizer for the Newton algorithm after establishing the existence of weak solutions for the forward equation of light amplitude autocorrelation and its Frechet derivative and adjoint. The asymptotic stability of the solution of the ordinary differential equation obtained through the introduction of the pseudo-time is also analyzed. We show that the asymptotic solution obtained through the pseudo-time marching converges to that optimal solution provided the Hessian of the forward equation is positive definite in the neighborhood of optimal solution. The superior noise tolerance and regularization-insensitive nature of pseudo-dynamic strategy are proved through numerical simulations in the context of both DCT and diffuse optical tomography. (C) 2010 Optical Society of America.