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This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared based on their ability to compensate nonlinear distortion of power amplifiers in a single user (SU) and multiuser (MU) Fully-Connected (FC) HBF mMIMO transmitters. The proof-of-concept is provided with a 64 × 64 FC HBF mMIMO system, with 2 RF chains. The complexity of DPD solution is reduced by using a single Real-Valued Time-Delay Neural Network with two hidden layers (RVTDNN2L) instead of using as many different DPD blocks as there are RF chains in the HBF mMIMO transmitter and it is shown that the proposed architecture better compensates nonlinear distortion compared to the traditional memory polynomial DPD. Two RVTDNN2L DPD architectures are developed and tested for linearization of MU FC HBF mMIMO systems, and it is also shown that the proposed RVTDNN2L DPD architecture efficiently linearizes MU FC HBF mMIMO transmitters in terms of Normalized Mean-Squared Error (NMSE) and Error Vector Magnitude (EVM). [ABSTRACT FROM AUTHOR]

Compensation of nonlinear distortion in hybrid beamforming MIMO transmitters is an open and challenging field of research. The goal of the study is to find an efficient method for linearization of power amplifiers in fully connected hybrid beamforming MIMO transmitters, which would not additionally increase the complexity of the system too much. In this paper, the polynomial-based compensation method of nonlinear distortion in hybrid beamforming MIMO transmitters is presented. The method is verified through comprehensive Matlab simulations. A 64x64 fully connected hybrid beamforming MIMO system with 2 RF branches on the transmitting side and 2 RF branches on the receiving side was considered. The proposed memory polynomial digital predistortion model is based on direct transmit-end feedback. Simulations were performed for different parameters of the memory polynomial model and analyzed. The obtained results are presented in graphical form, using Power Spectrum Density, and in numerical form, using metrics in time and frequency domain, such as Normalized Mean-Squared Error and Error Vector Magnitude. It is shown that the proposed memory polynomial digital predistortion model can very well compensate nonlinear distortion in fully connected hybrid beamforming MIMO transmitter. It has been also demonstrated that with increasing model complexity, ie with increasing memory depth and nonlinearity order of memory polynomial model, better compensation of nonlinear distortion in hybrid beamforming MIMO transmitters can be achieved. [ABSTRACT FROM AUTHOR]

In this paper, a literature overview of propagation models based on the application of deep learning is presented. Given the shortcomings of traditional empirical and deterministic models, papers dealing with the formation of propagation models are increasingly turning to modern solutions such as deep learning. This paper discusses the classification of propagation models based on deep learning techniques based on their ability to make predictions independently or in combination with a traditional model. Different architectures of deep learning models that are most commonly used in the creation of propagation models are analyzed, such as deep feedforward neural networks, convolutional neural networks and generative adversarial networks. The basic differences arising from different deep learning models and types of input data are analyzed, as well as their impact on the need for expert knowledge in the selection of data that appear as elements in the vector representing input. [ABSTRACT FROM AUTHOR]

WMNs (Wireless Mesh Networks) represent a new trend in wireless communications, providing greater flexibility, reliability and better network performances than standard WLANs (Wireless Local Area Networks). Having in mind a need for more efficient and faster data transmission, one of the greatest challenges in designing WMNs is to provide greater throughput. This is done by introducing networks the nodes of which have multiple interfaces that can work on one of multiple channels (the channel is chosen by a predefined algorithm), socalled Multi-Channel Multi-Interface (MCMI) WMNs. In order to provide better network performances, it is necessary to define routing protocols and metrics used to find and select the optimal route from the source to the destination node. Since WMNs are characterised by a high dynamic range of the received signal level, especially when they are realized in complex environments such as indoors, it is necessary to define routing metrics that can follow these fast changes in propagation conditions that occur in each link in the network. [ABSTRACT FROM AUTHOR]