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Abstract

It has been shown in the present paper that exploitation of the experimental potential of a photoacoustic technique can provide information on a type of intermolecular interactions in aqueous mixtures containing organic liquids, when the basic parameters of these mixtures, such as density, ρ, specific heat, cp, or thermal conductivity, λ, are unknown. Earlier investigations of concentration dependence of effusivity in different aqueous solutions of organic liquids demonstrated that the photoacoustics method is a sensitive tool to identify hydrophobic properties of such liquids. In our experiment this suggestion was exploited for a solution of methanol which is known to display much weaker hydrophobicity than other alcohols.

It was confirmed that the location of extreme deviations from linearity for the thermal effusivity, Δe, agrees well with that of characteristic points for the isentropic compressibility coefficient, κS, and the excess molar volume, V_m^E, as a function of the concentration.

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Authors and Affiliations

Nikodem Ponikwicki
Łukasz Szczepanik
Joanna Gondek
Bogumił B.J. Linde
Ewa Barbara Skrodzka
Vladimir Molchanov
Konstantin B. Yushkov
Rostislav Grechishkin
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Abstract

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.
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Authors and Affiliations

Maciej Soszka
1

  1. Institute of Radioelectronics and Multimedia Technology, Warsaw University of Technology, Warsaw, Poland

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