# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
### Fanet
#### 1.2. 5g  
5G is the next-generation wireless network (see Figure 1) that provides mobile internet connectivity with promising download and upload speeds, wider coverage, and higher stability. 5G incorporates various types of new technologies (e.g., D2D communication), and coordinates different kinds of network cells (e.g., macrocells and SCs, including picocells and femtocells) to reduce interference [10; 11; 12; 13]. The network is generally segregated into \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(i\) & Number of agents, where \(i=1,2,3\ldots\),\(N\). \\ \(s_{i}^{i}\) & State of an agent \(i\) at time \(t\). \\ \(m_{i}^{i}\) & Mobility of an agent \(i\) at time \(t\). \\ \(e_{i}^{i}\) & Residual energy of an agent \(i\) at time \(t\). \\ \(E_{r}\) & Residual energy of an agent. \\ \(a_{i}^{i}\) & Action of an agent \(i\) at time \(t\). \\ \(A\) & Set of possible actions. \\ \(x_{m,E_{r}}^{h}\) & Action (i.e., a selected next-hop node \(x^{h}\)) taken based on mobility \(m\) and residual energy \(E_{r}\). \\ \(r_{t}(s_{r}^{i},a_{r}^{i},s_{l+1}^{i})\) & Delayed reward received by an agent \(i\) at time \(t\). \\ \(Q(s_{r}^{i},a_{i}^{i})\) & State-action pair or Output Q-value. \\ \(\mu_{\theta}(s_{r}^{i},a_{i}^{i})\) & Policy for the selection of state-action pair Q-value \(Q(s_{r}^{i},a_{i}^{i})\). \\ \(R_{max}^{h}\) & Memory for storing the experiences used for training deep neural network. \\ \((s_{r}^{i},a_{r}^{i},r_{t}^{i},s_{l+1}^{i})\) & \(k^{th}\) experiences stored in reply memory \(R_{mem}\). \\ \(\alpha\) & Learning rate. \\ \(\gamma\) & Discount factor. \\ \(\epsilon\) & Exploration rate. \\ \(\epsilon_{max}\) & Maximum exploration rate. \\ \(\epsilon_{min}\) & Minimum exploration rate. \\ \(\epsilon_{decay}\) & Decaying variable of exploration. \(\epsilon\) from maximum exploration rate \(\epsilon_{max}\) to \\ \(y_{i}\) & Desired target function. \\ \(\theta\) & Network parameters of the main network. \\ \(\tilde{\theta}\) & Network parameters of the target network. \\ \(\
abla_{\tilde{\theta}}L_{i}(\theta_{i})\) & Gradient descent based on a loss function for network parameters \(\theta\). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Routing notations. different network planes comprised of different network cells; for instance, a macro-plane consists of macrocells. 5G caters for next-generation network scenarios characterized by _ultra-densification_ whereby there is a large number of active UAVs per unit area generating a massive amount of data, and _high heterogeneity_ whereby there is a diverse range of transmission capabilities among UAVs distributed in different network planes (see Section 3). One of the key features of 5G is the presence of a CC and distributed controllers (DCs) to support the hybrid approach. The CC manages global information (e.g., the residual energy of a UAV and the network plane in which a UAV resides), and allocates network-wide resources (e.g., channels with spatial reuse). The DC manages local information (e.g., the geographical location, node degree, and relative speed, of a UAV), and allocates local resources (e.g., bandwidth and buffer space). This hybrid approach allows the CC and DCs to exchange the global and local information with each other. The presence of DCs allows control functions to be brought closer to UAVs and local infrastructure, particularly the BSs, leading to a reduced interaction time between UAVs and controllers, and increased throughput performance with higher bandwidth availability at the CC. Using the new technologies of 5G, particularly D2D, across different kinds of network cells in FANETs reduces congestion level and increases throughput. D2D increases bandwidth availability at BS and can support the deployment of different network cells through spatial reuse of frequency bands.