Radio Signals Research Articles

A novel direct detection lidar compatible with software-defined radio

This paper proposes a novel direct detection lidar compatible with software-defined radio, which can effectively absorb the advantages of radio frequency technology. The radio frequency signal is firstly loaded onto the output light of the lidar through a fiber phase modulator and then demodulated from its backward reflected light through a combination of fiber Fabry-Perot interferometer and photodetector. The generation of the transmitting radio frequency signal and the sampling and processing of the receiving radio frequency signal are completed by a software-defined radio. Theoretically, it has been proven that the range and velocity of the target cause phase delay and Doppler frequency shift of the radio frequency signal loaded on the lidar, just like using the radio frequency signal to directly detect the target in radar. In the experiment, by programming the software-defined radio, the ranging and velocimetry algorithms of radar have been applied to this lidar. The theoretical and experimental results indicate that the lidar proposed in this paper has software definable characteristics. Through programming, different functions can be achieved, and in the future, more advanced algorithms of radar can be easily transplanted to further improve its performance.

Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions

Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions

ЗАСТОСУВАННЯ ФІЛЬТРА ЧАСТОЧОК ДО ЗАДАЧ САМОЛОКАЛІЗАЦІЇ ЛИШЕ ЗА ВІДСТАННЮ ДО ДЖЕРЕЛ РАДІОСИГНАЛІВ

This work is aimed at searching for a relatively accurate and computationally efficient algorithm for range-only self-localization. Key requirement to the data sources is cost effectiveness. The radio signal strength indicator (RSSI)-based distance estimation model was chosen, due to wide availability of hardware and cost effectiveness. Key requirements to the algorithm are robustness towards noise introduced by RSSI-based distance estimation errors and flexibility to introduce additional data sources. Particle filter algorithm was chosen, because it satisfies both requirements, although it has bigger computational costs comparing to Kalman filter. A simulation environment was created to conduct experiments with assumed transmitted signal strength of 1W and square root signal decay law. A Particle filter-based actor was implemented. Actor starts by translating geo-spatial coordinates of beacons into relative planar cartesian coordinates, generates particles around beacons. Particles are placed with uniform distribution within a torus with co-planar circular axis radius equal to distance estimation. In each experiment, simulation software walks actor through a pre-defined path, compares obtained position estimates with expected state and calculates MAE and RMSE. Obtained results were satisfactory, in some cases showing MAE=2.5981 and RMSE=2.8474 after short stabilization period of few iterations. This period exists due to suboptimal initial particles placement. A Kalman filter-based actor was implemented as a comparison. A reference Kalman filter-based implementation showed slightly worse overall results with MAE=12.5199 and RMSE=13.2238. This can be explained by a lack of input from IMU. Further research will be directed towards UAV swarm self-localization and collision avoidance.

Antena Tatasusun Grid Lut Sinar Gandaan Tinggi bagi Aplikasi Jalur-Ku

roducing compact devices for space technology. Transparent antennas are seen as the solution to this problem. With the presence of a transparent antenna, it can save antenna installation space as its transparency properties allow it to be integrated with solar technology. However, the use of transparent material on the antenna causes the antenna gain to be low due to the low conductivity of the material. When a low conductivity material is used it also causes the antenna efficiency to decrease. Therefore, modifications to the antenna design are needed to overcome such problems. This study aims to design a transparent antenna that can operate on Ku-band. The aim of this study is to identify the effect of transparent materials on conductivity, design a transparent antenna with an efficiency as low as 85% and design a grid array antenna to increase gain. The material verification test is carried out by making a comparison of the material in the antenna design simulation that works on Ku-band. The transparent antenna material selected is Indium Tin Oxide (ITO). Then, the design of the grid array antenna is carried out to increase the antenna gain hence for high efficiency. In this research, 8 loops grid array antennas increase the gain by 52% compared to reference antennas. The antenna meets performance requirements and can be integrated with solar panels to efficiently harness sunlight for electricity generation, while effectively transmitting and receiving wireless signals.

Alternating optimized RIS-assisted noma and nonlinear partial differential deep reinforced satellite communication

Satellite communication is crucial for 6 G industrial wireless networks due to its comprehensive communication services, but traditional methods face challenges such as high latency, limited bandwidth, high cost, and integration with terrestrial networks. This highlights the need for innovative communication methods to boost satellite communication efficiency, reliability, and coverage within the context of 6 G industrial applications, which is attempted to address by this research. RISs (Reconfigurable Intelligent Surfaces) are thin sheets with tiny antennas that are programmed to manipulate radio signals. The RIS has the potential to revolutionize industrial wireless communication by providing greater dynamic and goal-oriented control over radio signals. From there, the wireless environment is being transformed into a service. Besides, the NOMA (Non-Orthogonal Multiple Access) mechanism is a highly effective radio access method in advanced wireless communications that optimizes spectrum utilization and offers improved spectrum efficiency, lower latency, more reliability, and increased connectivity. The current industrial communication network faces limitations such as limited capacity, signal interference overload, complex and expensive infrastructure, and latency issues. The fusion of RIS and NOMA is seen as promising strategy to meet the stringent demands of next-generation networks for immense connectivity. It is particularly innovative for the demanding environment of smart factories by enabling efficient resource sharing, dynamic signal control, ultra-reliable, low-latency communication, cost-effective network, overcoming the signal interference and simplifying the network infrastructure. In this article, we propose a novel approach for device-to-device satellite communication in industrial networks. This approach leverages a RIS (Reconfigurable Intelligent Surface) -assisted cooperative network with NOMA (Non-Orthogonal Multiple Access) and NL-PDDRL (Nonlinear Partial Differential Deep Reinforced Learning) termed AONPDDR (Alternating Optimized and Nonlinear Partial Differential Deep Reinforced) communication. This article focuses on important aspects related to industrial communication networks such as power consumption, energy efficiency, data delivery rate, data loss rate, throughput, latency, coverage, false alarm rate, and bit error rate. The Alternating Optimization-based RIS configuration algorithm optimizes reflecting elements in RIS by maximizing channel vectors and providing a sophisticated upper bound on the highest number of reflective elements. Next, The NL-PDDRL algorithm is designed to function effectively despite environmental uncertainties and constraints. The results indicate that the proposed AONPDDR method outperforms previous benchmark methods in system-achievable aspects.

Correlation between rate of TEC index and positioning error during solar flares and geomagnetic storms using navigation with Indian constellation receiver measurements

Abstract The real-time position accuracy of the Global Navigation Satellite System (GNSS)/Navigation with Indian Constellation (NavIC) receiver is limited by the dynamic behavior of the ionosphere, particularly in adverse conditions like solar flares and geomagnetic storms. The NavIC satellites broadcast dual coherent radio beacon signals on L5 (1,164.5 MHz) and S (2,472.5 MHz) bands for providing position, velocity, and timing services in all weather conditions. The Total Electron Content (TEC) and Rate of TEC Index (ROTI) are the potential indicators for characterizing the ionosphere and its irregularities. In this research work, the TEC and ROTI are computed from the code and carrier phase observations of the NavIC receiver located at Kurnool low latitude station (15.79° N, 78.07° E) with geomagnetic coordinates (7.30° N, 151.65° E). This paper presents a statistical study of TEC, ROTI, and the correlation between ROTI and NavIC positioning error during highly intense solar flares (X9.3 and X2.2) and geomagnetic storm conditions. Compared to quiet days mean TEC, the enhancement is 3 TECU due to X9.3 flares, and the maximum peak of TEC on storm day (September 8, 2017) is 80.92 TECU. Moreover, the correlation coefficient between ROTI and position error is 0.76 on a quiet day (September 4, 2017), 0.54 on an intense solar flares day (September 6, 2017), and 0.24 on a storm day (September 8, 2017), this indicates positional accuracy degradation on a geomagnetic storm day. The outcome of this research work would be helpful for investigating characteristics of the northern low latitude ionospheric irregularities and, in turn, useful for developing suitable ionospheric nowcasting/prediction models for GNSS applications.

A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN).

Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays' localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme's effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000-1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%.

Decision Making to Choose Communication Network System for Teleremote Dozer Operation Using Analytic Hierarchy Process

One type of heavy equipment is a mining material pushing tool called a dozer. Dozers generally work in areas near cliffs that are prone to landslides, where these areas can be classified as dangerous areas. New technology is needed to increase the safety of dozer operators from the threat of danger when operating a dozer. Teleremote dozers are one method that can reduce the risk of accidents that can happen to dozer operators. Instead of operating the dozer from inside the cabin, the operator operates the dozer via a remote control device. There is an important aspect in operating a teleremote dozer, namely the need for a signal that will transmit data and commands from the remote control to the dozer unit operating in the field. Good and uninterrupted signal quality is the main key to good teleremote dozer operations with minimal risk. The wireless signal network system currently used by companies will enter its obsolete period. However, the old system was tough and not easily damaged. There are strategic options for supporting the signal network for teleremote dozer operations: maintaining the old network system, replacing it with a new one, or using the old and new systems in a hybrid manner. Because there will only be one strategy chosen, a decision must be made. The SWOT AHP method is used for decision making regarding the strategy to be taken. The results of the AHP SWOT will produce a strategy, which will become a benchmark for making subsequent alternatives. Determining alternative types of network systems to support teleremote dozer operations will use the AHP method.

A Multipath Approach to Self-Interference Cancellation in MIMO Rayleigh Fading Channels

An optically enabled multipath self-interference (MSI) cancellation technique for in-band full duplex (IBFD) multiple input multiple output (MIMO) Radio over Fiber RoF systems under Rayleigh fading channels is proposed. A digitally assisted Rayleigh fading channel model generates the MSI signal. An optically tunable multipath delay lines (OTMDL) module is designed using optical delay lines and an optical attenuator for coarse and fine-tuning of time delays and amplitude matching between the locally generated reference (LR) signal and the MSI signal. The OTMDL module adjusts the LR signal to cancel the effect of MSI on the signal received through the fading channel. The concept is validated using a remote sensor node of a 3 × 3 IBFD MIMO RoF system with a Rayleigh fading channel. The system's performance is evaluated by determining the MSI cancellation depths using the OTMDL section for various cancellation channels and by computing the MSI cancellation for both single band and wideband signals across different cancellation channels. An average value of MSI cancellation depth of 20.38 dB is obtained for a single-tone radio frequency signal in the range 15–25 GHz. Thus, the proposed system for MSI cancellation can be utilized in wireless communication systems in multiple ways.

Strain-invariant stretchable radio-frequency electronics.

Wireless modules that provide telecommunications and power-harvesting capabilities enabled by radio-frequency (RF) electronics are vital components of skin-interfaced stretchable electronics1-7. However, recent studies on stretchable RF components have demonstrated that substantial changes in electrical properties, such as a shift in the antenna resonance frequency, occur even under relatively low elastic strains8-15. Such changes lead directly to greatly reduced wireless signal strength or power-transfer efficiency in stretchable systems, particularly in physically dynamic environments such as the surface of the skin. Here we present strain-invariant stretchable RF electronics capable of completely maintaining the original RF properties under various elastic strains using a 'dielectro-elastic' material as the substrate. Dielectro-elastic materials have physically tunable dielectric properties that effectively avert frequency shifts arising in interfacing RF electronics. Compared with conventional stretchable substrate materials, our material has superior electrical, mechanical and thermal properties that are suitable for high-performance stretchable RF electronics. In this paper, we describe the materials, fabrication and design strategies that serve as the foundation for enabling the strain-invariant behaviour of key RF components based on experimental and computational studies. Finally, we present a set of skin-interfaced wireless healthcare monitors based on strain-invariant stretchable RF electronics with a wireless operational distance of up to 30 m under strain.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.

Behavioral Modeling and Digital Predistortion for Power Amplifier Based on the Sparse Smooth Twin Support Vector Regression Method

In this paper, a sparse smooth twin support vector regression (Sparse‐STSVR) model for power amplifier (PA) behavioral modeling is obtained by pruning the kernel matrix based on Cholesky decomposition. Based on the primal smooth twin support vector regression (STSVR) model, the Nystrom approximate matrix of the kernel matrix is found to replace the original kernel matrix, thus simplifying the Newton iterative parameter extraction process of the primal STSVR model and accelerating the convergence of the algorithm. In addition, the new rank approximation kernel matrix has the characteristic of sparse parameters, which further reduces the computational complexity of the feedforward link of the digital predistorter. The 100 MHz 5G New Radio (NR) signal is used for verify the effect of PA modeling and digital predistortion (DPD) experiment. The results show that the proposed method can improve the normalized mean square error (NMSE) by about 2 ~ 3 dB with fewer coefficients compared with the previously proposed machine learning model, and the predistortion linearization effect improves by nearly 3 dB on the adjacent channel power ratio (ACPR), which achieves a good trade‐off between model performance and computational complexity. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Accurate calculation method of terrain viewshed for wireless signal as line of sight

AbstractViewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications.

Isomerism-enabling low-temperature ultrasensitive and moisture-resistant fire-alarming “Guarder” via color changing

Aiming to integrate a low-temperature, ultrasensitive and wireless remote-monitoring fire alarming with a fire suppression into a commercialized system, in view of a heat-induced tailored isomerization of Salen molecules and sunflower configuration for a heat capturing, we constructed a quasi-core-shell MAPP@Salen with silaned ammonia polyphosphate guiding a vertical deposition of Salen lamellas assembly. Resultantly, MAPP@Salen demonstrated an ultralow triggering temperature of 120 °C and 1 s time at 250 oC with a remarkable color from yellow to dark red. A Salen isomerization and its vertical alignment on MAPP accounted for an ultra-sensitiveness feature. A model acrylic coating with MAPP@Salen presented a 0.3 s responsive time at 250 °C as well as instant (0.5 s) and unstopped warning once flame approached. A wireless signal communication system via a color recognition algorithm and 4 G IoT was realized. Additionally, the coating provided >10 min and ultrastrong intumescent fire protection via a Salen-participative charring optimization as well as well-persevered structural integrity after a long-term water attack. The fire-alarming capability was retained as well. This work pertaining to a bio-inspired hierarchical assembly integrates a low-temperature ultra-sensitive fire alarming and an optimized fire resistance for commercial all-time-scale fire retardants in multi-scenario universal applications.

Simulation Model Design of VHF Omni-Directional Range (VOR) Based on Microcontroller

The process of giving directional information to assist airplanes in making an accurate landing at the airport of destination is known as aviation navigation. The VHF Omnidirectional Range is a crucial navigation tool for guiding planes to the airport (VOR). Since it costs a lot of money to learn how to fly an airplane, a VOR simulator was developed that can mimic the movement of an aircraft passing through the VOR. The simulation that was produced is a prototype that sends and receives signals to the aircraft utilizing an antenna and microcontroller as supporting hardware. The VOR/DME flight navigation system is constructed in this study using mathematical modeling; a formulation representing the essential features of the system is expressed as a set of connected variables. The ESP32 module that powers the system serves as a DME by sending out Bluetooth radio signals. where one ESP32 module serves as a DME object (moving DME) and three ESP32 modules serve as DME stations (ground DME). Information on the distance between the DME station and the DME object will be communicated using the MQTT protocol, and this data will be processed using the trilateration method to predict the location and movement of the DME object. The measurement accuracy at DME station 1, DME station 2, and DME station 3 are 99.52%, 99.92%, and 99.98% respectively. The enhanced capabilities to estimate the position of objects observed from different directions or omni-directionally on a two-dimensional scale are made possible by the performance results of combining the performance of three ESP32 devices as Distance Measurement Equipment (DME).

Code communication method for water injection wells based on transient flow waves in tubular strings

Aiming at the current problems of heavy duty and long periods of measurement and adjustment for water injection wells, a new code communication method based on transient flow waves in tubular strings was studied. Then, the generation and response characteristics of transient flow wave control signals were explored. The coding and decoding methods of transient flow wave were established, simulated by PIPENET software, and verified by the well tests. The results shows that water injection does not need to be stopped during data transmission by using the wave code method. Transient flow wave encoding can be achieved by operating the valves at the wellhead and downhole. Furthermore, the format of data frame is defined and the data frame can be extended according to the actual situations. In the simulation test, the communication between the wellhead and the downhole water regulator is realized and the optimal control is achieved. The moving average method is more suitable for signal filtering processing. Therefore, the method has the advantages of wireless signal transmission and light duty of testing and regulation. The code communication method for transient flow waves is beneficial to reducing water loss and improving water injection efficiency.

Identification of Shipborne VHF Radio Based on Deep Learning with Feature Extraction

In the feature identification of maritime VHF radio communication signals, shipborne VHF communication technology follows the same international technical standards formulated by IMO, uses analog communication technology and uses the same communication channel in the same area, and cannot effectively achieve signal feature identification by adding feature elements in the process of signal modulation. How to effectively identify the ship using VHF radio has always been a technical difficulty in the field of ship perception. In this paper, based on the convolutional neural network, combined with the feasibility of CAM feature extraction and BiLSTM feature extraction in non-cooperative signal recognition, a deep learning recognition model of shipborne VHF radio communication signals is established, and the deep learning approach is employed to discern the features of VHF signals, thereby accomplishing the identification and classification of transmitting VHF radio stations. Several experiments are designed according to the characteristics of ship communication scenes at sea. The experimental data show that the method proposed in this paper can provide a new feasible path for ship target perception in terms of radio signal characteristics and identification.

RFBoost: Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation

Deep learning shows promising performance in wireless sensing. However, deep wireless sensing (DWS) heavily relies on large datasets. Unfortunately, building comprehensive datasets for DWS is difficult and costly, because wireless data depends on environmental factors and cannot be labeled offline. Despite recent advances in few-shot/cross-domain learning, DWS is still facing data scarcity issues. In this paper, we investigate a distinct perspective of radio data augmentation (RDA) for WiFi sensing and present a data-space solution. Our key insight is that wireless signals inherently exhibit data diversity, contributing more information to be extracted for DWS. We present RFBoost, a simple and effective RDA framework encompassing novel physical data augmentation techniques. We implement RFBoost as a plug-and-play module integrated with existing deep models and evaluate it on multiple datasets. Experimental results demonstrate that RFBoost achieves remarkable average accuracy improvements of 5.4% on existing models without additional data collection or model modifications, and the best-boosted performance outperforms 11 state-of-the-art baseline models without RDA. RFBoost pioneers the study of RDA, an important yet currently underexplored building block for DWS, which we expect to become a standard DWS component of WiFi sensing and beyond. RFBoost is released at https://github.com/aiot-lab/RFBoost.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.

МЕТОДИКА РОЗРАХУНКУ ЗОН ПОКРИТТЯ ЦИФРОВОГО ТЕЛЕВІЗІЙНОГО МОВЛЕННЯ

The introduction of DVB-T2 digital television broadcasting in the VHF waveband is currently underway, and test trials have already been conducted in some regions of Ukraine. To assess the effectiveness of the projects, it is of particular interest to compare the radio coverage areas of digital TV transmitters operating in the VHF and UHF wave bands. This requires extensive calculations for specific locations. For comparison, the city of Kyiv was chosen, where there is one multiplex MX-7 (8 TV channels) in the VHF band, and the following four multiplexes are represented in the UHF band: MX 1 (26 TV channels) and MX 3 (49 TV channels), which operate at the lowest and highest frequencies of the band. In this article, to predict the coverage areas of digital television broadcasting transmitters, the justification of the selected Longley-Rice radio wave propagation model is provided, which allows calculating the radio signal attenuation compared to free space propagation using terrain profiles (digital elevation maps) and the electrical characteristics of the Earth's surface, Taking into account the main mechanisms of radio wave propagation (interference on the line of sight, diffraction on irregular terrain, tropospheric scattering), it is more accurate and its results are closer to actual measurements. The calculations will be carried out using Radio Mobile software based on the Longley-Rice Irregular Terrain Model (ITM) and using Terrain Mission SRTM satellite maps with the possibility of further adding map data and roads from various sources. Based on the ITU R Recommendations on the design of DVB-T2 digital television broadcasting networks, the minimum required field strength to ensure reception with a given quality was calculated and the initial data for predicting the coverage areas of DVB-T2 digital television transmitters were prepared using the example of Kyiv. The proposed methodology and the data obtained will allow to calculate the boundaries of the coverage areas of DVB-T2 digital standard television transmitters in the VHF and UHF wave bands for selected television channels