# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 4 State-of-the-Art FANET
### Physical Layer Advancements in FANETs  
Full optimization and deployment of a UAV-based communication network would not be accomplished without the profound investigation of suitable physical layer protocols. At the physical layer where the nodes perform the data transmissions in different forms of waveforms and numerology designs [83], the selection of an appropriate interface, like those explained in [82], affects data rate, coverage range, reliability, throughput, and other system requirements. Nevertheless, the investigation of the physical layer in FANETs includes various topics such as channel state information (CSI), Doppler effect, inter-symbol interference, packet collisions, etc.  
Herein, a hierarchical model in [35] showed that the physical layer has a significant impact on the efficiency of FANET networks. It detailed signal propagation of different bands, including sub-6 GHz and mmWave bands in different levels of the atmosphere; signal creation and conversion in FANET transceivers; and signal conversion in antennas. Furthermore, attenuation and inter-symbol interference (ISI) were considered in that model by the proposal of inertial frequency-dependent links and multipath channels. Besides, the authors in [84] analyzed the mmWave UAV-assisted networks with the vision of providing wireless mobile access. Another model in [85] proposed a cross-layer of physical- and network-layers for anti-jamming routing protocol in FANETs. It used cognitive radio concept along with taking the physical layer link quality parameters into account for routing decisions. Indeed, it showed that the use of cognitive radio in FANETs improves spectrum efficiency while allowing UAVs and ground-based cellular users to cohabit in the same frequency range. Furthermore, in contrast to conventional networks, UAV networks are capable of having more transparent mobility models, which enables more precise forecasting of time-varying interference. The authors in [86] proposed a technique to predict the level of interference that is applicable to such UAV networks. In addition, in [87], a general framework was proposed to forecast the interference behavior via analysis of the mean value and moment-generating function of the interference prediction. Moreover, the authors in [88] designed parameters for adaptation of the physical layer for data transmission in self-organizing FANETs. Furthermore, a communication interface manager was proposed in [89] for improving the performance of heterogeneous FANETs. It dynamically defined the optimal means of transmitting data based on real-time analysis of the state of the wireless medium. In [90], the physical layer in 5G new radio (NR) was modified to piggyback the FANET. It assessed the waveform and a scalable numerology for both frequency division duplex (FDD) and time division duplex (TDD), and a multi-antenna transmission and beamforming.  
Physical layer security for UAVs in FANETs, like models proposed in [91; 92; 93], is another important topic with great potential to exploit the randomness nature of the wireless channels for secure communications. Practical UAV channel modeling, position acquisition [94], pilot contamination [95], limited buffer size and resources [96; 97], and NOMA [98; 99; 100] are other important aspects of the physical layer that affect the system performance in FANETs significantly. For instance, the study in [84] depicted the performance of NOMA on the FANETs in terms of power efficiency, data-rate improvement, and radio resource allocation.  
Overall, within the physical layer realm of FANET, there is still more work to be done to have a general framework. Having a full framework will further help to enhance the system modeling, which results in more reliable data processing in higher layers.