Adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles

The article discusses the features of adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles operating in the conditions of "smart cities". The concept of cities of this type is defined, the historical path of formation, the current state and prospects for further development in the aspect of transition to "smart cities" of the third generation are shown. Cities of this type are aimed at providing more comfortable and safe living conditions for citizens and autonomous automated work of all components of the urban economy. The perspective of the development of urban mobile automated technical means of infocommunications is shown, one of the leading directions of which is the creation and active use of wireless self-organizing networks based on unmanned aerial vehicles. The advantages of using small-sized unmanned aerial vehicles for organizing networks of this type are considered, as well as the range of tasks to be solved in the conditions of modern "smart cities". It is shown that for the transition to self-organizing networks in the conditions of "smart cities" of the third generation, it is necessary to ensure the adaptation of various levels of OSI network models to dynamically changing operating conditions, which is especially important for the physical layer. To maintain an acceptable level of the value of the bit error probability when transmitting command and telemetry data, it is proposed to adaptively change the coding rate depending on the signal-to-noise ratio at the receiver input (or on the number of channel decoder errors), and when transmitting payload data, it is also proposed to adaptively change the coding rate together with the choice of modulation methods that differ in energy and spectral efficiency. As options for the practical implementation of these solutions, it is proposed to use an approach based on the principles of neuro-fuzzy control, for which examples of determining the boundaries of theoretically achievable efficiency are given.