## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network
### Smart Grid Technology
Smart grid technology leverages AI algorithms to achieve the prediction of inter-frequency performance metrics. As illustrated in Figure 10, a neural network model is trained using the collected historical data. The mapping relationship between input and output is obtained, which is used to predict the inter-frequency information. Figure 8: Schematic diagram of Smart grids. Figure 10: Diagram of inter-frequency information prediction. Figure 9: Definition of Smart grids. Based on the distinct input features and output results, smart grid models include RSRP prediction models, spectrum efficiency prediction models, and beam ID prediction models. Taking the RSRP coverage grid as an example, a multi-layer perceptron (MLP) algorithm is employed. There are 2M inputs, 3 hidden layers, and the activation function utilizes a ReLU function. M consists of 1 service sell and M-1 Co-frequency Neighbor Cells. The loss function is Focal Loss. The output size is P, which signifies the number of predicted inter-frequency neighboring cells, and the predicted RSRP for the corresponding P inter-frequency neighboring cells can be obtained. The network structure for the spectrum efficiency grid model and beam grid model is identical to the RSRP coverage grid model. The input-output feature quantities for each type of grid are presented in Tables 3-5. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Input Feature** & **Implication** \\ \hline \multirow{2}{*}{Service Cell} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Neighbor Cells} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Cell User Count and Distribution} & RRC Connected User Count & \\ \cline{2-3}  & Pending Scheduling User Count & Sub-second Level Statistics, \\ \cline{2-3}  & Cell Average MCS & \\ \hline \multirow{2}{*}{Cell Resources} & Downlink Cell PRB Utilization Rate & \\ \cline{2-3}  & PDCCH CCE Utilization Rate & Quality for Users Within the Cell \\ \hline \multirow{2}{*}{Background Service} & Cell Last TTI Ratio & Representation of Background User \\ \cline{2-3}  & & Service Proportion \\ \hline \multicolumn{2}{c}{**Output Feature**} & **Implication** \\ \hline Inter-Frequency Target Neighbor Cell & Inter-Frequency Spectral Efficiency & Spectral Efficiency \\ \hline \end{tabular}
\end{table} 
Table 4: Spectrum efficiency grid-neural network input feature and output feature. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Input Feature** & **Implication** \\ \hline \multirow{2}{*}{Service Cell} & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \cline{2-3}  & SSB Beam ID & SSB Beam Index \\ \cline{2-3}  & SSB RSRP & SSB Reference Signal Received Power \\ \hline \multirow{2}{*}{Co-frequency Neighbor Cells} & SSB RSRP & SSB Reference Signal Received Power \\ \cline{2-3}  & SSB RSRP & Reference Signal Received Power \\ \hline \multicolumn{2}{c}{**Output Feature**} & **Implication** \\ \hline Inter-Frequency Target Neighbor Cell & SRP & Reference Signal Received Power \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RSRP coverage grid-neural network input feature and output feature.