This article presents a converged 5G network infrastructure and an overarching architecture to jointly support operational network and end-user services, proposed by the EU 5G PPP project 5G-XHaul. The 5G-XHaul infrastructure adopts a common fronthaul/backhaul network solution, deploying a wealth of wireless technologies and a hybrid active/passive optical transport, supporting flexible fronthaul split options. This infrastructure is evaluated through a novel modeling. Numerical results indicate significant energy savings at the expense of increased end-user service delay.

This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks. The quantizer performance is investigated in a wide dynamic range of data variances, and for that purpose, we derive novel closed-form expressions. Moreover, we propose two selection criteria for the variance range of interest. Binary quantizers are further implemented for compressing neural network weights and its performance is analysed for a simple classification task. Good matching between theory and experiment is observed and a great possibility for implementation is indicated.

Three main approaches on how audio signals can be used as input to a deep learning model are: extracting hand-crafted features from audio signals, mapping audio signals into appropriate images such as spectrogram-like ones, and using directly raw audio signals. Among these approaches, the usage of spectrogram-like images represents a compromise regarding the bias enforced by the processing (seen in hand-crafted features) and computational demands (seen in raw audio signals). When any of the spectrogram-like images is used as a deep learning model input, then different techniques for image processing become available and can be implemented. They include techniques for assessing the image similarity, implementing image matching, and image recognition. The topic of this paper is similarity of spectrogram-like images obtained from DC motor sounds. In that respect, relevant measures of image similarity are first reviewed, and then one of them - the Pearson correlation coefficient - is applied for evaluating the similarity within the same class and between two classes of different spectrogram-like images.

Design of Switched Quantizers and Speech Coding Based on Quasi-Logarithmic Compandor

The downlink transmission in a single-cell wireless system is considered. The base station is equipped with an antenna array, and each user has one antenna. It is assumed that the channel state information at the base station is erroneous, with the exact channels lying in specified uncertainty regions. Linear spatial filters of all participants in the system are jointly optimized in order to minimize the total transmit power of the base station, while satisfying the users' predefined mean square error targets for all channels that comply with the adopted error models. The solutions are obtained using semidefinite programming methods from convex optimization theory.

The sounds of certain industrial products (machines) carry important information about these products. Product classification or malfunction detection can be performed utilizing a product’s sound. In this regard, sound can be used as it is or it can be mapped to either features or images. The latter enables the implementation of recently achieved performance improvements with respect to image processing. In this paper, the sounds of seven industrial products are mapped into mel-spectrograms. The similarities of these images within the same class (machine type) and between classes, representing the intraclass and interclass similarities, respectively, are investigated. Three often-used image similarity measures are applied: Euclidean distance (ED), the Pearson correlation coefficient (PCC), and the structural similarity index (SSIM). These measures are mutually compared to analyze their behaviors in a particular use-case. According to the obtained results, the mel-spectrograms of five classes are similar, while two classes have unique properties manifested in considerably larger intraclass as opposed to interclass similarity. The applied image similarity measures lead to similar general results showing the same main trends, but there are differences among them as mutual relationship of similarity among classes. The differences between the images are more blurred when the SSIM is applied than using ED and the PCC.