FDD massive MIMO channel spatial covariance conversion using projection methods

Knowledge of second-order statistics of channels (e.g. in the form of covariance matrices) is crucial for the acquisition of downlink channel state information (CSI) in massive MIMO systems operating in the frequency division duplexing (FDD) mode. Current MIMO systems usually obtain downlink covariance information via feedback of the estimated covariance matrix from the user equipment (UE), but in the massive MIMO regime this approach is infeasible because of the unacceptably high training overhead. This paper considers instead the problem of estimating the downlink channel covariance from uplink measurements. We propose two variants of an algorithm based on projection methods in an infinite-dimensional Hilbert space that exploit channel reciprocity properties in the angular domain. The proposed schemes are evaluated via Monte Carlo simulations, and they are shown to outperform current state-of-the art solutions in terms of accuracy and complexity, for typical array geometries and duplex gaps.Comment: Paper accepted on 29/01/2018 for presentation at ICASSP 201

Downlink channel spatial covariance estimation in realistic FDD massive MIMO systems

The knowledge of the downlink (DL) channel spatial covariance matrix at the BS is of fundamental importance for large-scale array systems operating in frequency division duplexing (FDD) mode. In particular, this knowledge plays a key role in the DL channel state information (CSI) acquisition. In the massive MIMO regime, traditional schemes based on DL pilots are severely limited by the covariance feedback and the DL training overhead. To overcome this problem, many authors have proposed to obtain an estimate of the DL spatial covariance based on uplink (UL) measurements. However, many of these approaches rely on simple channel models, and they are difficult to extend to more complex models that take into account important effects of propagation in 3D environments and of dual-polarized antenna arrays. In this study we propose a novel technique that takes into account the aforementioned effects, in compliance with the requirements of modern 4G and 5G system designs. Numerical simulations show the effectiveness of our approach.Comment: [v2] is the version accepted at GlobalSIP 2018. Only minor changes mainly in the introductio

A unifying study of phenotypic and molecular genetic variability in natural populations of Anadenanthera colubrina var. cebil from Yungas and Paranaense biogeographic provinces in Argentina

Anadenanthera colubrina var. cebil is a native tree species in the South American subtropical forests that is discontinuously distributed. Thirteen quantitative traits and eight nuclear microsatellite loci were examined in individuals from two biogeographic provinces of Argentina in order to determine the number and composition of genetically distinguishable groups of individuals and explore possible spatial patterns of thephenotypic and genetic variability. Means of reproductive traits were higher in the Yungas than in the Paranaense biogeographic province whereas five out of eight non-reproductive quantitative traits showed higher mean values in the latter. Variance coefficients were moderate, and Analyses of Variance resulted in significant differences between and within provinces. Three clusters were defined based on spatial model for cluster membership for quantitative traits. One cluster grouped the individuals from the Paranaense biogeographic province whereas the individuals from the Yungas biogeographic province grouped regarding its population of origin. Parameters of molecular genetic variability showed higher values in the Yungas than in the Paranaense biogeographic province. Observed heterozygosity was lower than expected heterozygosity in both biogeographic provinces indicating an excess of homozygosity. The homozygosity test by Watterson and the exact test by Slatkin suggested diversifying selection for locus Ac41.1. Bayesian clustering spatial model for microsatellites loci data were performed for both all loci and all loci excluding locus Ac41.1. In both analyses two clusters were inferred. AMOVAs revealed similar results for all genotypes and for all genotypes defined excluding locus Ac41.1. Most of the total variance is attributable to genetic variation within clusters. The presence of homogeneous clusters was detected for both the phenotypic and molecular genetic variability. Two Bayesian clustering analyses were performed according to molecular genetic data, and two clusters were inferred. Individuals were assigned to their provinces of origin. Genetic molecular variation was higher in the populations of the Yungas biogeographic province which translates in highly qualified populations for conservation. Populations from the Paranaense biogeographic province showed the highest mean value of number of seeds per fruit making them valuable as well with regard to the exploitation of management strategies as a means to recover the impacted areas where these populations are located.Fil: García, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Prinz, Kathleen. Universität Göttingen; Alemania. Universitat Jena; AlemaniaFil: Barrandeguy, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Miretti, Marcos Mateo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Posadas; ArgentinaFil: Finkeldey, Reiner. Universitat Jena; Alemani

Dynamic Programming Algorithms for Optimal Control Problems in Hybrid Electric Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease.

The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes

Unlocking the Potential of Local CSI in Cell-Free Networks with Channel Aging and Fronthaul Delays

It is generally believed that downlink cell-free networks perform best under centralized implementations where the local channel state information (CSI) acquired by the access-points (AP) is forwarded to one or more central processing units (CPU) for the computation of the joint precoders based on global CSI. However, mostly due to limited fronthaul capabilities, this procedure incurs some delay that may lead to partially outdated precoding decisions and hence performance degradation. In some scenarios, this may even lead to worse performance than distributed implementations where the precoders are locally computed by the APs based on partial yet timely local CSI. To address this issue, this study considers the problem of robust precoding design merging the benefits of timely local CSI and delayed global CSI. As main result, we provide a novel distributed precoding design based on the recently proposed team minimum mean-square error method. As a byproduct, we also obtain novel insights related to the AP-CPU functional split problem. Our main conclusion, corroborated by simulations, is that the opportunity of performing some local precoding computations at the APs should not be neglected, even in centralized implementations

Driveability Constrained Models for Optimal Control of Hybrid Electric Vehicles

This work investigates the effect of three different driveability constraints on the optimal energy management strategy for a p2 parallel hybrid. Two of these constraints are used to prevent frequent gear shifting and engine start/stops, while the third is used to increase the sportiness of the vehicle by maximizing the available torque reserve at all times. The constraints are imposed by reformulating them as penalty terms to be added to the base running cost of the control strategy, which is fuel consumption. Dynamic programming, a popular optimal control technique, is then used to design the energy management strategy that minimizes the total cost. A case study is developed for a p2 parallel hybrid and simulated on a combination of the Artemis driving cycles. The impact of each driveability constraint is analyzed with respect to a set of relevant features of the control strategy, such as the choice of engine operating points and the gear shift pattern. The resulting discussion provides some useful insight for the design of real-time, rule-based control strategies

Robust Modeling for Optimal Control of Parallel Hybrids With Dynamic Programming

The aim of this work is to provide insight and guidelines for engineers and researchers when developing hybrid powertrain models to be employed in a dynamic programming optimal control algorithm. In particular, we focus on the advantages and disadvantages of the various control sets that can be used to characterize the power flow (e.g. the engine torque or a torque-split coefficient). Dynamic programming is the reference optimal control technique for hybrid electric vehicles. However, its practical implementation is not exempt from numerical issues which may hamper its accuracy. Amongst these, some are directly related to the different modeling choices that can be made when defining the system dynamics of the powertrain. To treat these issues, we first define four relevant evaluation criteria: control bounds definition, numerical efficiency, model complexity and interpretability. Then, we introduce eight different control sets and we discuss and compare them in light of these criteria. This discussion is supported by an extensive set of numerical experiments on a p2 parallel hybrid. Finally, we revisit our analysis and simulation results to draw modeling recommendations