## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network
### Smart Grid Technology  
In wireless networks, terminals often incur time costs or performance losses to obtain target information. For instance, terminals need to perform inter-frequency GAP measurements to acquire inter-frequency RSRP information. During these inter-frequency GAP measurement periods, the terminal suspends both uplink and downlink transmissions, leading to reduced throughput. Smart grid technology takes the grid as the fundamental unit, and a virtual wireless air interface environment characteristic library is established by incorporating air interface measurement information. Leveraging the correlation between co-frequency and inter-frequency measurement reports of historical users, an Artificial Intelligence (AI) model is trained to predict inter-frequency information based on co-frequency information. This approach effectively mitigates the impact of inter-frequency GAP measurements on services. To achieve optimal network-level performance for time-frequency-space resource allocation, the optimization formula mentioned above needs to be solved, yielding the optimal parameter combinations. For each user's utilization of air interface resources on each frequency band \(\alpha_{k,i,n,b}(t)\), we propose an RSRP prediction method based on smart coverage grids. When the predicted RSRP corresponding to UE in beam \(n\) on carrier \(i\) at time \(t\) falls below a predefined minimum threshold, \(\alpha_{k,i,n,b}(t)\) is set to 0; otherwise, it is set to 1. Note that RSRP, as defined in the 3 rd Generation Partnership Project (3 GPP) protocol, generally refers to Synchronization Signaling Block (SSB) RSRP or Channel State Information Reference Signal (CSI-RS) RSRP. Here, we refer to SSB RSRP. The frequency domain position of the SSB is independent of the specific allocation of frequency resources b; hence the predicted value of \(\alpha_{k,i,n,b}(t)\) can be obtained from \(\alpha_{k,i,n}(t)\), which, in turn, can be predicted using coverage grids. Moreover, we introduce a prediction method for spectrum efficiency \(SE_{k,i,t,n,b}\) based on smart spectrum efficiency grids, as well as a prediction method for \(BW_{k,i,n,b}(t)\) based on smart beam grids. By intelligently predicting the beam ID information of each user, users with the same beam ID are collectively considered, and the total resources are divided by the number of users. We can achieve frequency resource allocation at the granularity of RBGs. As depicted in Figure 8, we achieve predictions for RSRP, spectrum efficiency, and beam ID across different frequency bands by constructing high-resolution smart grids. These predictions provide essential inputs for solving the network-level optimization algorithm. Through multi-dimensional measurements of wireless signals, UE with similar wireless characteristics can be classified together. The base station gNodeB in 5G networks groups users with identical measurement results into the same grid, referred to as a smart grid. Utilizing machine learning techniques, gNodeB takes these smart grids as crucial features to extract the mapping relationship between all smart grids within a cell and the signal characteristics of a specific frequency point. This mapping relationship is constructed by a smart grid model. For instance, if a 5G cell is configured with M neighboring frequency points, there would be M smart grid models. As illustrated in Figure 9, within a same-frequency network at frequency point F1, there are two cells labeled Cell1 and Cell2. If UE2 measures a frequency point and its results are [(Cell 1, RSRP 1), (Cell 2, RSRP 2),...], and UE3 measures the same frequency point with identical results as UE2, UE2, and UE3 are considered to belong to the same smart grid. Utilizing the Cell ID and RSRP for each cell under the neighboring frequency point F3 as labels, the RSRP coverage grid is used to train the relationship model between frequency point F1 [(Cell 1, RSRP 1), (Cell 2, RSRP 2),...] and frequency point F3[(Cell 1, RSRP 1), (Cell 2, RSRP 2),...].