Unmanned Aerial Vehicles for Internet of Things (IoT). Группа авторов

Figure 2.1 depicts the design issues faced in the UAV-assisted communication networks.

The rest of the chapter discusses in detail the above mentioned design issues encountered in deployment of UAV-assisted communication networks, the solutions proposed till date and the open research areas that need to be worked upon for optimizing the performance of the UAV systems. Designing of UAV-assisted communication networks is a great challenge in itself.

      Figure 2.1 Design issues of UAV-assisted communication networks.

      2.2.1 Variations in Channel Characteristics

      The control signaling and data signaling in UAV communication need two types of channels—UAV-ground and UAV–UAV channel. Both types of channels exhibit several unique characteristics.

      a) Air to Ground Channel Modeling Due to complexity in the operating environments, systematic measurements and modeling of UAV-ground channels needs to be carried out as in Refs. [6, 7]. Problems to be considered are link blockage due to obstacles like terrain, buildings, shadowing during aircraft maneuvering in critical operations, multipath links due to reflections, scatterings, diffractions, from different physical contours like mountains, ground surface, foliage, water bodies, desert, etc.

      Compared to the characteristics of terrestrial communication channels, the A2G channel characteristics differ in terms of coverage and capacity [8–12]. This demands for optimal designing and deployment of A2G channel models for various applications like cellular-connected UAV-UE and IoT communication. We need to consider the type of UAV, the altitude at which the UAV is placed, the angle of elevation between transmitter and receiver in the A2G link, type of propagation scenario (such as the rural scenario, suburban scenario, urban scenario, high rise urban scenario), the movements of the antennas at the UAVs, shadowing caused by the body of UAV, locations of ground users, etc. A probabilistic path loss model proposed by Hourani et al. [9, 13] has been widely adopted by researchers authoring the literature as in [14–24]. Table 2.1 lists the studies carried out till date for understanding the effect of various design parameters on the operation of A2G channel in UAV systems.

      Deterministic models using environmental parameters and for studying large scale fading effects in A2G channels have been proposed in [27, 28]. The effect of propagation conditions on the coverage and optimal UAV positioning have been discussed in Refs. [29–33]. Geometric based stochastic models have been proposed for evaluating the spatial-temporal characteristics in a geometric simulation environment [34–39]. Further work needs to be carried out in the area of channel modeling of A2G links taking into consideration the communication over water bodies and communication in highly urban scenarios.

      Table 2.1 Parameters determining the performance of A2G channel.

Parameter worked upon	Reference
Effect of environment on path loss, delay spread, fading	[25]
Effect of high altitude of platform	[10, 11]
Effect on signal strength due to path loss and shadowing on account of various propagation scenarios and high elevation angle between transmitter and receiver.	[10]
Effect of low altitude UAV in suburban environments	[12]
Effect of angle of elevation and building height in urban environment	[26]

      b) Air to Air Channel Modeling A dominant LoS (Line of Sight) component is preferred for the air to air (A2A) communication channels of UAV communication. A2A propagation channel is useful in multi hop UAV networks for sensing and coordination applications. In case of emergency situations, it can replace the existing communication systems and provide a backhaul wireless connectivity. Large scale fading statistics in A2A propagation channels have been discussed in Refs. [40–45]. But the impact of antenna orientation, gain from UAV-MIMO system and Doppler spectrum of the A2A channel are yet to be worked upon. The major challenge encountered by these channels is the higher Doppler frequency shifts which occur due to higher relative velocity between UAVs. In 5G networks, if millimeter waves are used for backhauling than it would result in wider spectrum allocations, higher data rates, reduced latency in A2A channels. But the drawback is higher Doppler shifts. Hence we need to work out suitable techniques to be used in A2A links.

      Various empirical and analytical channel models characterizing A2A and A2G propagation channels have been discussed in Ref. [46]. Multidimensional UAV channel modeling is yet to be explored thoroughly.

      2.2.2 UAV-Assisted Cellular Network Planning and Provisioning

Network planning is more challenging in the case of UAV assisted cellular network. Parameters such as mobility, line of sight interference, energy constraints, and wireless backhaul connectivity have to be considered while planning such networks. The network needs to be planned taking into consideration the LoS interference between numbers of UAV-UE in the uplink connections. The BS in UAV assisted networks should be able to offer 3D communication coverage as the UAV-UEs are located at heights greater than the conventional BS antenna heights. The types of antennas at the ground stations involved in the UAV-UE in downlink communication need to be redesigned to provide wider coverage over the sky. The characteristics of the channel between UAV and BS are different from the channels of conventional terrestrial systems, as strong LoS links exist in this scenario. Such strong communications links result in efficient communication between UAV and the associated BS but also poses the threat of inter-cell interference from adjacent but non-associated BSs, in scenarios having both aerial and terrestrial UEs [47]. One also needs to consider the signaling and overhead involved in such networks due to the mobility feature of the UAVs. Studies carried out by researchers and presented in Refs. [18, 20, 48–50] focuses on problems like user association, backhaul connectivity, optimization of the number of UAVs that should be deployed in a network, placement of UAVs etc. Researchers have thrived to prove that optimal planning of UAV assisted cellular networks would require exhaustive work to be carried out to achieve an enhancement in throughput, reduction in delay, reduction of signaling overheads, reduction in interference in case of multiple UAV scenarios, less operational cost, reduced energy consumption, better effective interference mitigation techniques to deal with UAV-BS channel, techniques for supporting asymmetric UAV traffic requirements and so on.

      2.2.3 Millimeter Wave Cellular Connected UAVs

The challenges encountered by cellular networks operating at mmWaves (Millimeter Waves) can be enumerated as, high attenuation, reduced transmission range, increased scattering, high penetration losses when encountering objects, frequent signal blockage, etc. These can be overcome to a certain extent by the use of UAV-assisted cellular networks. But such UAV based wireless communication systems operating at mmWaves also face certain issues. The channel characteristics of UAV mmWave communication networks are quite different as compared to those of the traditional UAV communication networks as well as the terrestrial cellular network communication. Channel models incorporating air to air channels, air to ground channel, air to sea channels need to be designed for UAV mmWave communication networks. There is need to carry out both empirical as well as analytical studies for UAV mmWave channels used in dense urban scenarios. Need arises to develop models that would assist in study of effects of weather on performance of UAV assisted mmWave networks, as mmWave propagation and the stability of UAV both get affected by rain and wind. The fast channel estimation methods should be developed to solve the problems due to reduced channel coherence time in high-mobility