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Dl 101: Basic Introduction To Deep Learning With Its Application In Biomedical Related Fields
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Received: 22 June 2022 / Revised: 15 July 2022 / Accepted: 6 August 2022 / Published: 17 August 2022
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Fifth generation (5G) networks are ushering in a new era to meet user demands for higher data rates and massive connectivity for sharing and using information. Vertical applications such as vehicle-to-everything (V2X) communication, industrial automation, smart factory, smart farm and smart cities require ultra-fast communication and a wide range of services. Extending coverage is an important issue to support the necessary requirements for higher performance, but requires additional deployment of base or relay stations. Therefore, an effective solution must be cost-effective and easy to deploy more stations. Unmanned aerial vehicle (UAV) is seen as a candidate to overcome these problems because it is much more cost-effective than ground stations and does not require network or cell re-planning, thus improving network coverage without habits, additional event and redundant deployment of the existing network. UAVs will play an important role in 5G networks and beyond, helping as macro base stations, relay stations, small cells or mobile aggregators. The performance of wireless drone networks is highly dependent on the location or trajectory of the drones and the resource management of the entire networks. Recently, extensive studies have been conducted on performance analysis, UAV deployment, UAV trajectories, and resource management of UAV wireless networks to achieve the necessary performance requirements. This paper explores the research done for UAV deployment and how to build UAV wireless networks to extend coverage, improve throughput, and manage resources for different scenarios and use cases, in order to encourage further studies in this area.
5G wireless communication networks and beyond; UAV deployment and trajectory; cover extension; 5G machine learning and beyond wireless communication networks; UAV deployment and trajectory; cover extension; Machine learning
Global mobile data traffic reaches 77 exabytes per month in 2022, and annual traffic will reach nearly one zettabyte . Additionally, with the proliferation of wireless devices such as smartphones and tablets, an estimated 12.3 billion devices that exceed the world’s population by one and a half times will be wirelessly connected via the Internet of Things (IoT) and machine-to-machine (M2M). ). (b) communication for sharing and using information . In addition, the mobile network speed becomes 28.5 Mbit in 2022 . Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) and LTE-Advanced systems, known as Fourth Generation (4G) networks, are deployed to provide unprecedented data transfer speeds and quality of service (KoS). Currently, 4G networks are responsible for 54% of connections, but will generate 71% of all traffic, with 4G connections almost double the average connection in third generation (3G) networks . However, current LTE-Advanced systems cannot meet the demands for ultra-high data rates to support emerging applications.
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Unlike wireless communication systems up to 4G LTE-Advanced, fifth generation (5G) technologies have been developed to support unprecedented use cases and applications that require high-speed communication and ultra-reliable latency and massive connectivity. To this end, the International Telecommunication Union (ITU) proposes three use cases for IMT-2020 and beyond; Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (uRLLC) and Massive Machine Type Communication (mMTC), and eight Key Performance Indicators (KPI) as requirements of 5G . The main technologies for 5G networks have been developed and studied by researchers and experts from organizations, institutions and companies around the world. Specifically, 3GPP approved the first 5G New Radio (NR) Standalone (NSA) specifications and completed Release 15 including standalone NR (SA) specifications in 2018. The third 5G standard, 3GPP Release 17, reached milestone 3 functional freeze in March. 2022, which marks the completion of the system design, and is expected to further improve system performance and expand into new vertical applications. Coverage improvements and broadcast/multicast expansion are the main 5G milestones in 3GPP Release 17 to improve the capacity of 5G networks .
Communication service providers commercially launched 5G networks in 2019, and the first 5G device went on sale in early 2020. 5G networks are expected to support 12% of mobile traffic by the end of 2022 and drive a huge amount of traffic through high-bandwidth connectivity range (ie 100 MHz) and ultra-low range (ie one millisecond) . 5G networks will take over most of the mobile traffic from 4G networks, and the amount of mobile data traffic through the 5G connection will increase exponentially. To meet users’ demands for higher data rates and massive information delivery connectivity, coverage expansion is the key as outlined in 3GPP Release 17 to improve 5G capacity. Coverage expansion can be achieved with additional base stations or relay stations in 5G networks. However, the use of base or relay stations is expensive due to cell reorganization to optimize the location of existing stations along with new stations to provide the required KoS. Therefore, a smart solution to extend coverage and improve capacity is urgently needed.
With the development of sophisticated technologies, the unmanned aerial vehicle (UAV) has advanced in recent years, achieving light weight, high flexibility, and longer battery life . With these advances, UAVs can be used in a wide variety of applications such as military, public safety, and civilian functions [5, 6, 7, 8, 9, 10]. In addition, much attention has been focused on the use of UAVs in and out of 5G networks to improve capacity due to their rapid deployment, low maintenance costs, and high mobility . In addition, since the application of UAVs in wireless communication networks does not require cell reorganization, UAVs can provide a viable solution for this purpose in existing networks. However, wireless drone networks leave many issues to be resolved regarding stable and reliable services . Unlike other wireless networks such as Mobile Ad hoc Networks (MANETs) and Vehicular Ad hoc Networks (VANETs), rapid changes in network topology, including the number of nodes and links present, require special attention in the function and relative positions of nodes. . The routing protocol cannot be implemented in a simple proactive or reactive manner because the UAV wireless network must be reorganized several times when the UAV is out of use. In addition, it is challenging to manage the handover of user connections from an inactive UAV to an active UAV at a moment’s notice . Finally, UAV wireless networks require energy efficiency to extend the lifetime of the networks .
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Most of the problems are related to the configuration of the UAV wireless networks because it determines the state of the communication link, the optimal routing protocol and the effective energy management of the networks. In other words, the performance of wireless drone networks depends on the location of the drones, which changes the channel conditions between the ground node and the drone or between the drones. Therefore, the optimal deployment of UAVs can maximize service coverage, total bandwidth, and network lifetime when UAVs are deployed as air bases or relay stations in wireless communication networks. In addition, gathering data from massive sensors through IoT and M2M communication is key to realizing upcoming vertical applications such as smart factory, smart farm and smart city. To this end, drones are used as drone aggregators in wireless sensor networks to collect data from sensors and disseminate information to them. However, energy efficiency and total mission time are the main issues in wireless sensor networks in order to extend the lifetime of the network and save the power of both the sensor and the UAV, so in studies on the UAV trajectory, attention has been paid to the flight of the UAV to reduce the energy. consumption in wireless sensor networks.
Extensive studies have been done on the optimal deployment and trajectory of UAVs to expand cell coverage, improve throughput,
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