Hybrid beamforming designs for frequency-selective mmWave MIMO systems with Per-RF chain or per-antenna power constraints

Configuring precoders and combiners is a major challenge to deploy practical multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication systems with large antenna arrays. Most prior work addresses the problem focusing on a total transmit power constraint. In practical transmitters, however, power amplifiers must operate within their linear range, so that a power constraint applies to each one of the input signals to these devices. Therefore, precoder and combiner designs should incorporate per-antenna or per-radio frequency (RF) chain transmit power constraints. We focus on such problem for frequency-selective channels with multicarrier modulation, and assuming hybrid analog/digital architectures based on fully connected analog blocks implemented with finite-resolution phase shifters. We first derive an all-digital solution which aims to maximize spectral efficiency. Then, we develop hybrid precoders and combiners by approximately matching the corresponding all-digital matrices while still enforcing the power constraints. Numerical results show that the proposed all-digital design performs close to the upper bound given by the standard waterfilling-based solution with a total power constraint. Additionally, the hybrid designs exhibit a moderate loss even when low-resolution phase shifters are considered.Agencia Estatal de Investigación | Ref. PID2019-105717RB-C21Xunta de Galicia | Ref. ED431C 2021/4

Dental Treatment under General Anesthesia in Healthy and Medically Compromised/Developmentally Disabled Children: A Comparative Study

Aim: To compare the type, number of procedures and working time of dental treatment provided under dental general anesthesia (DGA) in healthy and medically compromised/developmentally disabled children (MCDD children). Design: This cross-sectional prospective study involved 80 children divided into two groups of 40 children each. Group 1 consisted of healthy and Group 2 consisted of MCDD children. Results: Healthy children needed more working time than MCDD children, the means being 161±7.9 and 84±5.7 minutes, respectively (P= 0.0001). Operative dentistry and endodontic treatments showed a significant statistical difference (P= 0.0001). The means of procedures were 17±5.0 for healthy children and 11±4.8 for MCDD children (P= 0.0001). Conclusions: Healthy children needed more extensive dental treatment than MCDD children under DGA. The information from this sample of Mexican children could be used as reference for determining trends both within a facility as well as in comparing facilities in cross-population studies

Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors

Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data. The dictionary learning problem is non-convex, and usually solved via computationally complex iterative algorithms. Furthermore, the resulting transforms obtained generally lack structure that permits their fast application to data. To address this issue, this paper develops a framework for learning orthonormal dictionaries which are built from products of a few Householder reflectors. Two algorithms are proposed to learn the reflector coefficients: one that considers a sequential update of the reflectors and one with a simultaneous update of all reflectors that imposes an additional internal orthogonal constraint. The proposed methods have low computational complexity and are shown to converge to local minimum points which can be described in terms of the spectral properties of the matrices involved. The resulting dictionaries balance between the computational complexity and the quality of the sparse representations by controlling the number of Householder reflectors in their product. Simulations of the proposed algorithms are shown in the image processing setting where well-known fast transforms are available for comparisons. The proposed algorithms have favorable reconstruction error and the advantage of a fast implementation relative to the classical, unstructured, dictionaries