A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs

Coordinated multi-point (CoMP) schemes have been widely studied in the recent years to tackle the inter-cell interference. In practice, latency and throughput constraints on the backhaul allow the organization of only small clusters of base stations (BSs) where joint processing (JP) can be implemented. In this work we focus on downlink CoMP-JP with multiple antenna user equipments (UEs) and propose a novel dynamic clustering algorithm. The additional degrees of freedom at the UE can be used to suppress the residual interference by using an interference rejection combiner (IRC) and allow a multistream transmission. In our proposal we first define a set of candidate clusters depending on long-term channel conditions. Then, in each time block, we develop a resource allocation scheme by jointly optimizing transmitter and receiver where: a) within each candidate cluster a weighted sum rate is estimated and then b) a set of clusters is scheduled in order to maximize the system weighted sum rate. Numerical results show that much higher rates are achieved when UEs are equipped with multiple antennas. Moreover, as this performance improvement is mainly due to the IRC, the gain achieved by the proposed approach with respect to the non-cooperative scheme decreases by increasing the number of UE antennas.Comment: 27 pages, 8 figure

A hybrid supervised/unsupervised machine learning approach to solar flare prediction

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data

Expectation Maximization for Hard X-ray Count Modulation Profiles

This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized for the analysis of count modulation profiles in solar hard X-ray imaging based on Rotating Modulation Collimators. The algorithm described in this paper solves the maximum likelihood problem iteratively and encoding a positivity constraint into the iterative optimization scheme. The result is therefore a classical Expectation Maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of Expectation Maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. If optimally stopped, Expectation Maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data

A consistent and numerically efficient variable selection method for sparse Poisson regression with applications to learning and signal recovery

We propose an adaptive 1-penalized estimator in the framework of Generalized Linear Models with identity-link and Poisson data, by taking advantage of a globally quadratic approximation of the Kullback-Leibler divergence. We prove that this approximation is asymptotically unbiased and that the proposed estimator has the variable selection consistency property in a deterministic matrix design framework. Moreover, we present a numerically efficient strategy for the computation of the proposed estimator, making it suitable for the analysis of massive counts datasets. We show with two numerical experiments that the method can be applied both to statistical learning and signal recovery problems

Bad and good errors: value-weighted skill scores in deep ensemble learning

In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidence that, on the one hand, a false alarm just anticipating an occurring event is better than one in the middle of consecutive non-occurring events, and that, on the other hand, a miss of an isolated event has a worse impact than a miss of a single event, which is part of several consecutive occurrences. Relying on this idea, we introduce a novel definition of confusion matrix and skill scores giving greater importance to the value of the prediction rather than to its quality. Then, we introduce a deep ensemble learning procedure for binary classification, in which the probabilistic outcomes of a neural network are clustered via optimization of these value-weighted skill scores. We finally show the performances of this approach in the case of three applications concerned with pollution, space weather and stock prize forecasting

Inverse diffraction for the Atmospheric Imaging Assembly in the Solar Dynamics Observatory

The Atmospheric Imaging Assembly in the Solar Dynamics Observatory provides full Sun images every 1 seconds in each of 7 Extreme Ultraviolet passbands. However, for a significant amount of these images, saturation affects their most intense core, preventing scientists from a full exploitation of their physical meaning. In this paper we describe a mathematical and automatic procedure for the recovery of information in the primary saturation region based on a correlation/inversion analysis of the diffraction pattern associated to the telescope observations. Further, we suggest an interpolation-based method for determining the image background that allows the recovery of information also in the region of secondary saturation (blooming)

A hybrid time-frequency parametric modelling of medical ultrasound signal transmission

Medical ultrasound imaging is the most widespread real-time non-invasive imaging system and its formulation comprises signal transmission, signal reception, and image formation. Ultrasound signal transmission modelling has been formalized over the years through different approaches by exploiting the physics of the associated wave problem. This work proposes a novel computational framework for modelling the ultrasound signal transmission step in the time-frequency domain for a linear-array probe. More specifically, from the impulse response theory defined in the time domain, we derived a parametric model in the corresponding frequency domain, with appropriate approximations for the narrowband case. To validate the model, we implemented a numerical simulator and tested it with synthetic data. Numerical experiments demonstrate that the proposed model is computationally feasible, efficient, and compatible with realistic measurements and existing state-of-the-art simulators. The formulated model can be employed for analyzing how the involved parameters affect the generated beam pattern, and ultimately for optimizing measurement settings in an automatic and systematic way

A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics

In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exists a discrepancy between the maximization of such scores and the minimization of the loss function in the training phase. In this paper, we provide a complete theoretical setting that formalizes weighted classification metrics and then allows the construction of losses that drive the model to optimize these metrics of interest. After a detailed theoretical analysis, we show that our framework includes as particular instances well-established approaches such as classical cost-sensitive learning, weighted cross entropy loss functions and value-weighted skill scores

Efficiency Improvement of a Natural Gas Marine Engine Using a Hybrid Turbocharger

The use of a computer simulator, previously developed and validated, applied to a four-stroke marine dual-fuel engine, has allowed the authors to present in this paper a solution to improve the overall efficiency of the engine by adopting a hybrid turbocharger. This component replaces the original one allowing, in addition to maintaining the previous usual functions, the production of electricity to satisfy part of the ship's electric load. In this study the application of the hybrid turbocharger concerns an engine powered by natural gas in particular. The turbocharger substitution involves a significant variation of the engine load governor operating mode. The improved engine characteristics that the hybrid turbocharger facilitates, compared to the original, are highlighted by the results reported in tabular and graphical form, for different engine loads and speed