Autonomous Airborne Wireless Networks

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Discover what lies beyond the bleeding-edge of autonomous airborne networks with this authoritative new resource Autonomous Airborne Wireless Networks The book evaluates advances in the cutting-edge of unmanned aerial vehicle wireless network technology while offering readers new ideas on how airborne wireless networks can support various applications expected of future networks. The rapidly developing field is examined from a fresh perspective, one not just concerned with ideas of control, trajectory optimization, and navigation. 
Autonomous Airborne Wireless Networks A thorough discussion of distributed drone base station positioning for emergency cellular networks using reinforcement learning (AI-enabled trajectory optimization) An exploration of unmanned aerial vehicle-to-wearables (UAV2W) indoor radio propagation channel measurements and modelling An up-to-date treatment of energy minimization in UAV trajectory design for delay tolerant emergency communication Examinations of cache-enabled UAVs, 3D MIMO for airborne networks, and airborne networks for Internet of Things communications Perfect for telecom engineers and industry professionals working on identifying practical and efficient concepts tailored to overcome challenges facing unmanned aerial vehicles providing wireless communications, 
 also has a place on the bookshelves of stakeholders, regulators, and research agencies working on the latest developments in UAV communications.

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where картинка 19is the distance‐dependent path loss, картинка 20is the large‐scale fading consisting of power variation on a large scale due to the environment, and картинка 21is the small‐scale fading. Parameters of channel model, such as path loss exponent and LoS probability, are dependent on the altitude level because propagation conditions change at different altitudes. The airspace is often segregated into three propagation echelons or slices as follows:

Terrestrial channel: For suburban and urban environments, altitude is between 10 and 22.5 m, respectively [7]. In this case, the terrestrial channel models can be used to model AG propagation because the airborne UAV is below the rooftop level. As a result, NLoS is the dominant component in the propagation.

Obstructed AG channel: For suburban and urban environments, altitude is 10–40 m and 22.5–100 m, respectively. In this case, LoS probability is higher than that of the terrestrial channels.

High‐altitude AG channel: All channels are in LoS for the altitude ranges between 100 and 300 m or above. Consequently, the propagation is similar to that in the free space case. Moreover, no shadowing is experienced for these channels.

2.4.1.1 Path Loss and Large‐Scale Fading

Air‐to‐Air ChannelFree space path loss model is the simplest channel model to represent the AA propagation at a relatively high altitude. Thus, the received power is given by [6]

(2.2) where denotes the transmitted signal power and - фото 22

where картинка 23denotes the transmitted signal power, картинка 24and картинка 25represent the gain of the transmitter and receiver antennas, respectively, картинка 26is the ground distance between the transmitter and receiver, and картинка 27is the carrier wavelength. Path loss exponent картинка 28is the rate of distance‐dependent power loss, where картинка 29varies with environments. In Eq. ( 2.2), Autonomous Airborne Wireless Networks - изображение 30for free space propagation. Therefore, the distance‐dependent path loss expression can be generalized as

(2.3) Autonomous Airborne Wireless Networks - изображение 31

Air‐to‐Ground ChannelIn urban environment, the AG channel may not experience complete free space propagation. In the existing literature on UAV communications, the log‐distance model is the prominently used path loss model due to its simplicity and applicability when environmental parameters are difficult to define. Therefore, path loss in dB is given by

(2.4) where is the path loss for the reference distance For the same p - фото 32

where is the path loss for the reference distance For the same propagation distance - фото 33is the path loss for the reference distance картинка 34. For the same propagation distance between the ground device and the UAV, large‐scale variations are different at different locations within the same environment because the materials of obstacles vary from each other, which affects the radio signal propagation. As a result, at any distance картинка 35, картинка 36in Eq. ( 2.1) is the shadow fading measured in dB and modeled as the normal random variable with variance картинка 37in dB. This model is extensively applied for modeling of the terrestrial channels. Table 2.2lists some measurement campaigns for the estimations of path loss and large‐scale effects.

Another popular channel model to characterize the AG propagation in UAV communications is the probabilistic path loss model in [4] and [17]. In [17], the path loss between the ground device and the UAV is dependent on the position of the UAV and the propagation environments (e.g. suburban, urban, dense‐urban, high‐rise). Consequently, during the AG radio propagation, the communication link can be either LoS or NLoS depending on the environment. Many of the existing works [18–35] on UAV communications adopted the probabilistic path loss model of [4] and [17]. In these works, the probability of occurrence of LoS and NLoS links are functions of the environmental parameters, height of the buildings, and the elevation angle between the ground device and the UAV. This model is based on environmental parameters defined in the recommendations of the International Telecommunication Union (ITU). In particular, ITU‐R provides statistical parameters related to the environment that determine the height, number, and density of the buildings or obstacles. For instance, in [36], the height of the buildings can be modeled by using the Rayleigh distribution. The average path loss for the AG propagation in [17] is given as

(2.5) where and are the LoS and NLoS path loss respectively for the free s - фото 38

where картинка 39and картинка 40are the LoS and NLoS path loss, respectively, for the free space propagation. is the LoS probability given as 26 Table 22 Measurement campaigns to - фото 41is the LoS probability given as

(2.6) Table 22 Measurement campaigns to characterize the path loss and largescale - фото 42

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