# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 4 Methods and Materials
### Data Collection Procedures
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**UAV-Bs (30 m)**} & & & & **UAV-Bs (60 m)** & \\ \hline \begin{tabular}{c} **Cluster** \\ **Index** \\ \end{tabular} & \begin{tabular}{c} **Alt.** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Rss** \\ **Values** \\ \end{tabular} & \begin{tabular}{c} **SNR** \\ **Values** \\ \end{tabular} & \begin{tabular}{c} **Throughput** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Power** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Alt.** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Rss** \\ **Values** \\ \end{tabular} & \begin{tabular}{c} **SNR** \\ **Values** \\ \end{tabular} & \
\begin{tabular}{c} **Throughput** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Power** \\ **Value** \\ \end{tabular} \\ \hline \multirow{4}{*}{C\({}_{1}\)} & \multirow{4}{*}{30} & \(\text{Rss}_{1}\) & \(\text{S}_{\text{NR1}}\) & T\({}_{1}\) & Pt\({}_{1}\) & & \(\text{Rss}_{1}\) & \(\text{S}_{\text{NR1}}\) & T\({}_{1}\) & Pt\({}_{1}\) \\  & & \(\text{Rss}_{2}\) & \(\text{S}_{\text{NR2}}\) & T\({}_{2}\) & Pt\({}_{2}\) & 60 & \(\text{Rss}_{2}\) & \(\text{S}_{\text{NR2}}\) & T\({}_{2}\) & Pt\({}_{2}\) \\  & & \(\text{Rss}_{n}\) & \(\text{S}_{\text{NRn}}\) & T\({}_{n}\) & Pt\({}_{n}\) & & \(\text{Rss}_{n}\) & \(\text{S}_{\text{NRn}}\) & T\({}_{n}\) & Pt\({}_{n}\) \\ \cline{1-1}  & & \(\text{Rss}_{1}\) & \(\text{S}_{\text{NR1}}\) & T\({}_{1}\) & Pt\({}_{1}\) & & \(\text{Rss}_{1}\) & \(\text{S}_{\text{NR1}}\) & T\({}_{1}\) & Pt\({}_{1}\) \\ \cline{1-1}  & & \(\text{Rss}_{2}\) & \(\text{S}_{\text{NR2}}\) & T\({}_{2}\) & Pt\({}_{2}\) & 60 & \(\text{Rss}_{2}\) & \(\text{S}_{\text{NR2}}\) & T\({}_{2}\) & Pt\({}_{2}\) \\ \cline{1-1} C\({}_{2}\) & & \(\text{Rss}_{n}\) & \(\text{S}_{\text{NRn}}\) & T\({}_{n}\) & Pt\({}_{n}\) & & \(\text{Rss}_{n}\) & \(\text{S}_{\text{NRn}}\) & T\({}_{n}\) & Pt\({}_{n}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of record values for training datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Parameter** & **Description** \\ \hline \(P_{L}\) & Path loss & \(T_{cut}\) & Effective user throughput \\ Tx & Transmitter & \(T_{s}\) & Time slot \\ M & UAV & \(T_{s}/s\) & Produced signal values per time slot \\ U & Users equipment’s & \(G_{\text{T}_{Y}}\), \(G_{\text{R}_{X}}\) & \(T_{x}\) and \(R_{x}\) antenna gains \\ Rx & Receiver & \(\lambda\) & Wavelength of the radio rate \\ \(D_{um}\) & Altitude from M and U (30, 60 m) & \(Rssn\) & Received signal strength \\ \(f_{c}\) & Carrier frequency (900 MHz) & \(SNR\) & Signal noise ratio \\ \(PL^{mu}\) & Path loss between M and U & \(R_{1}\), \(R_{2}\), \(R_{3}\) & Resources 1, resource 2, resource 3 \\ \(S^{mu}\) & Small-scale fading coefficient between M to & \(S_{t}\) & State value at a time \\ U & & & & \\ \(C(D_{um})\) & Channel gain from M to U & \(Q_{t}\) & Target value at a time \\ \(Pt^{1}\) & Power transmission one & \(A_{t}\) & Action value at a time \\ \(C^{D_{um}1}\) & Channel gain from UAV to user 1 & \(S_{t}^{\prime}\) & Next state value \\ \(N_{0}\) & Noise power & \(R_{t}\) & Reward value a time \\ \(D_{um}n\) & Altitude from M to user n & \(\gamma\) & Represent to control the changing \\ \(Phy_{u}\) & Physical capacity data rate & \(\pi_{t}^{*}\) & Value of optimal policy \\ \(PhyD_{um}\) & Physical capacity data rate from M to U & \(A_{t}\)\({}^{\prime}\) & Next action value \\ \(T_{U}\) & User throughput & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of notations. multi-agent RM approach, and the resources are allocated. Initially, users generate services from the sources. Then, users acquire their resource interest. The algorithm determines the availability of resources in parallel with the resource request. The classifier method aims to sort resource requests based on their preferences for assigning a ranking order. A comparison of requests is made for the execution phase. In the end, scheduling is done for the available resources.