Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (SLNR) precoding and MMSE combined with different system configurations by means of achievable per-user rate.

One of the main open problems for next generation wireless networks, is to find the new OFDM-based waveform to be used in 5G. The new modulation scheme must primarily be able to achieve higher spectral efficiency than its predecessor. The main 3GPP's candidate is a new version of OFDM, called Filtered Orthogonal Frequency-Division Multiplexing (f-OFDM), which is similar to OFDM but with additional filtering in order to reduce Out-Of-Band (OOB) emissions and to obtain a better spectral-localization. Another option is windowed-OFDM (w-OFDM), which is basically a classical OFDM scheme where each symbol is windowed and overlapped in the time domain. In this paper we compare classic OFDM signals using Cyclic Prefix (CP-OFDM) with f-OFDM and w-OFDM, each one with multiple parametric options and numerologies. A multirate transmitter simultaneously operating with multiple numerologies is considered, where the transmitted sub-bands must be up-sampled and interpolated in order to generate the composite numerical signal fed to the Digital to Analog Converter (DAC). Finally, we discuss advantages and disadvantages of the various schemes.

Cracks on the surfaces inside road tunnels are among the most critical problems in the management of such tunnels and, if not properly addressed, can have severe consequences in relation to safety and costs. Nowadays, the main techniques used for analysis of such surfaces make use of either human inspections or complex automated systems, which are, respectively, very time consuming and expensive, and/or difficult to implement. There is therefore great interest in a low-cost data-acquisition platform coupled with Artificial Intelligence-based automated crack detection system. This paper introduces a low-cost technique for road tunnel inspections based on a simple system that does not require complex preliminary work and can also be used in tunnels with a normal traffic flow. In particular, thanks to a second-generation data-acquisition system developed in the 2017–2018 academic year, a series of high-resolution pictures can be obtained and used in a pre-trained deep neural network able to identify the presence of cracks through the classification of the pictures. Thanks to deep learning techniques, it is possible to exploit the power of Inception-v4, a deep convolutional neural network provided by Google, which can be retrained for the specific purpose of crack detection. This kind of network has been trained with a pictures database generated using a second data-acquisition prototype developed during the 2017–2018 academic year.

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