8 research outputs found
ΠΠ»ΡΡΠ΅Π²ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠ²ΡΠ·ΠΈ Π΄Π»Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΡΠΈΡΡΠ΅ΠΌ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠ΅ΠΌ Π³ΡΠ°ΠΆΠ΄Π°Π½ΡΠΊΠΈΡ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΡΡ Π»Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ Π°ΠΏΠΏΠ°ΡΠ°ΡΠΎΠ² (ΠΎΠ±Π·ΠΎΡ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΠΎΠΉ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΡ)
Not less than one hundred thousand Unmanned Aerial Vehicles (UAVs) are expected to perform flights simultaneously in Russia by 2035. The UAV fleet capacity triggers the development of the systems for informational support, operating control and management of UAV flights (Unmanned Aircraft System Traffic Management (UTM) systems) similar to that one already operating in manned aviation. The challenges arising in the sphere of civil aviation cannot be solved without wireless communication. The goals of this article are as follows: 1) familiarization of communication experts with the latest scientific developments of unmanned aerial technologies 2) description of the telecommunication-related problems of extensive systems of UAV control encountered by development engineers. In this article a schematic architecture and main functions of UTM systems are described as well as the examples of their implementation. Special emphasis is put on enhancing flight safety by means of a rational choice of communication technologies to manage conflicts (Conflict Management) known as "collision avoidance". The article analyzes the application of a wide range of wireless technologies ranging from Wi-Fi and Automatic Dependent Surveillance Broadcast (ADS-B) to 5G cellular networks as well as cell-free networks contributing to the development of 6G communication networks. As a result of the analysis, a list of promising research trends at the intersection of the fields of wireless communication and UAVs for civil application is made.ΠΠΆΠΈΠ΄Π°Π΅ΡΡΡ, ΡΡΠΎ ΠΊ 2035 Π³ΠΎΠ΄Ρ Π² Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΌ Π½Π΅Π±Π΅ Π±ΡΠ΄ΡΡ ΠΎΠ΄Π½ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡΡ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅ ΡΡΠ° ΡΡΡΡΡ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΡΡ
Π»Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ
Π°ΠΏΠΏΠ°ΡΠ°ΡΠΎΠ² (ΠΠΠ). Π’Π°ΠΊΠ°Ρ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ ΡΠ»ΠΎΡΠ° ΠΠΠ Π΄Π΅Π»Π°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠΌ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ, ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ»Π΅ΡΠ°ΠΌΠΈ ΠΠΠ (Π°Π½Π³Π». Unmanned Aircraft System Traffic Management β UTM), ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
ΡΠΎΠΉ, ΡΡΠΎ ΡΠΆΠ΅ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ Π΄Π»Ρ ΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠΉ Π°Π²ΠΈΠ°ΡΠΈΠΈ. ΠΡΠΎΠ±Π»Π΅ΠΌΡ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΠ΅ ΠΏΠ΅ΡΠ΅Π΄ Π°Π²ΠΈΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎΠΌ, Π½Π΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΠ΅ΡΠ΅Π½Ρ Π±Π΅Π· ΠΏΠΎΠΌΠΎΡΠΈ Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ. Π¦Π΅Π»ΡΠΌΠΈ Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΎΠ·Π½Π°ΠΊΠΎΠΌΠ»Π΅Π½ΠΈΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² ΡΠ²ΡΠ·ΠΈ Ρ ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠΌΠΈ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡΠΌΠΈ Π³ΡΠ°ΠΆΠ΄Π°Π½ΡΠΊΠΎΠΉ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠΉ Π°Π²ΠΈΠ°ΡΠΈΠΈ ΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΡΠ΅Π»Π΅ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ°, ΡΡΠΎΡΡΠΈΡ
ΠΏΠ΅ΡΠ΅Π΄ ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌΠΈ ΠΌΠ°ΡΡΡΠ°Π±Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΠΠ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° ΠΈ Π³Π»Π°Π²Π½ΡΠ΅ ΡΡΠ½ΠΊΡΠΈΠΈ ΡΠΈΡΡΠ΅ΠΌ UTM, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΈΡ
ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ. ΠΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΠ»Π΅ΡΠΎΠ² ΠΏΡΡΠ΅ΠΌ ΡΠ°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ Π²ΡΠ±ΠΎΡΠ° ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΡΠ²ΡΠ·ΠΈ Π΄Π»Ρ ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΠ½ΡΠΌΠΈ ΡΠΈΡΡΠ°ΡΠΈΡΠΌΠΈ (ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠ°ΠΊ Β«ΠΈΠ·Π±Π΅ΠΆΠ°Π½ΠΈΠ΅ ΡΡΠΎΠ»ΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΠΉΒ»). ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π° ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΡΠΏΠ΅ΠΊΡΡΠ° Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ: ΠΎΡ Wi-Fi ΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΠ³ΠΎ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΡΠ°Π΄ΠΈΠΎΠ²Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° (ΠΠΠ-Π) Π΄ΠΎ ΡΠΎΡΠΎΠ²ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΠΏΡΡΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ 5G, Π° ΡΠ°ΠΊΠΆΠ΅ Π±Π΅ΡΡΠΎΡΠΎΠ²ΡΡ
ΡΠ΅ΡΠ΅ΠΉ (Π°Π½Π³Π». cell-free), ΡΠ²Π»ΡΡΡΠΈΡ
ΡΡ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅ΠΉ ΡΠ²ΡΠ·ΠΈ ΡΠ΅ΡΡΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ 6G. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ ΡΠΏΠΈΡΠΎΠΊ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π° ΡΡΡΠΊΠ΅ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π±Π΅ΡΠΏΡΠΎΠ²ΠΎΠ΄Π½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ ΠΈ Π³ΡΠ°ΠΆΠ΄Π°Π½ΡΠΊΠΎΠΉ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠΉ Π°Π²ΠΈΠ°ΡΠΈΠΈ
Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
Cellular connectivity for UAV systems is interesting because it promises coverage in beyond visual line of sight scenarios. Inter-cell interference has been shown to be the main limiting factor at high altitudes. Using a realistic 3D simulator model, with real base station locations, this study confirms that UAVs at high altitudes suffer from significant interference, resulting in a worse coverage compared to ground users. When replacing the existing base stations by mmWave cells, our results indicate that ground coverage is decreased to only 90%, while UAVs just above rooftop level have a coverage probability of 100%. However, UAVs at higher altitude still suffer from excessive interference. Beamforming has the potential to improve mmWave link budget and to decrease interference and is for this reason a promising technology for ensuring connectivity to aerial users
Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
Cellular connectivity for UAV systems is interesting because it promises coverage in beyond visual line of sight scenarios. Inter-cell interference has been shown to be the main limiting factor at high altitudes. Using a realistic 3D simulator model, with real base station locations, this study confirms that UAVs at high altitudes suffer from significant interference, resulting in a worse coverage compared to ground users. When replacing the existing base stations by mmWave cells, our results indicate that ground coverage is decreased to only 90%, while UAVs just above rooftop level have a coverage probability of 100%. However, UAVs at higher altitude still suffer from excessive interference. Beamforming has the potential to improve mmWave link budget and to decrease interference and is for this reason a promising technology for ensuring connectivity to aerial users.status: Published onlin
Performance evaluation of next generation wireless UAV relay with millimeter-wave in access and backhaul
Future wireless communication, particularly densified 5G networks, will bring numerous innovations to the telecommunication industry and will support 100-fold gain in throughput rates, 100-fold in capacity (for at least 100 billion devices), individual user data rate of up-to 10 Gb/s, extremely low latency and response times. In such a scenario, the use of Unmanned Aerial Vehicle (UAV) as a Base Station (gNB) becomes a viable option for providing 5G services, both on-demand and on a regular basis. Recent development of UAVs have made its deployment faster and reliable, resulting in a shift in its usage from traditional military to more commercial and corporate industries. On the other hand, due to the abundant availability of bandwidth in the millimeter-wave band (mmWave), there is an immense potential to utilize this band for next generation radio systems. In this case, smart integration of UAVs in 5G network provides immense potential, however, such network require efficient placement mechanism for providing blazingly fast wireless cellular network services. In this study, we analyze and describe the distinctive characteristics of mmWave propagation. The main goal is to investigate and evaluate the use of mmWave in Access and Back-haul communication links simultaneously for Amplify-and-Forward relays deployed on UAVs. We formulate the required mathematical framework for calculating the UE received power for direct path (gNB-UE) and relay path (gNB-UAV-UE) based on two cases; (i) Friis Transmission Equation and (ii) Log-Distance Path loss Model. We conduct simulations using ray-tracing simulator in different scenarios while comparing and verifying the simulation results vs mathematical equations. For the proposed system architecture, International Telecommunication Union (ITU) recommendation city model is used to calculate the probability for Line of Sight (LoS) and Non Line of Sight (NLoS) paths in different urban environments. Furthermore, we study and identify different parameters i.e., UAV location, and amplification factor to maximize the performance of an Amplify-and-Forward UAV based relay for providing enhanced coverage to the users. Similarly, the optimum UAV-gNB height is evaluated in different urban environments while providing coverage to the users via an Amplify-and-Forward relay. The study concludes with the Signal to Noise Ratio (SNR) analysis for the relay path compared with the direct path where we identify the constraints for effective relaying