# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 3 Optimisation Algorithm
### Average Power Allocation Algorithm Based on Partitioning Strategy
Since this paper uses a physical layer network model where the connections are not predetermined, we consider a fixed number of c available neighbors with the strongest channel environment as candidates. In addition, in order to ensure sufficient detection capability, we also add a re-detection action, which means that if the agent determines that no current strongest neighbor is a suitable next hop, it will continue to detect c strongest neighbors until it finds a suitable next hop. Thus, on a single band, the agent's action space consists of c+1 actions: (1) c independent action: Select each of the c strongest neighbors. (2) reprobate: Repeated detection of the new c strongest neighbors. If the agent reexplores, the agent stays at the current boundary node at the current time. In the next time step, the agent explores the next c nearest neighbors and collects the status information of these new neighbor sets. We consider a wireless AD hoc network in a 1000 km \(\times\) 1000 km region with F = 3 data streams. The data stream will be accessed for spectrum selection using B = 8 available frequency bands, each with 5 MHz bandwidth. The node density distribution is specified on nine equally divided subregions, each of which is randomly distributed with 6, 8, 7, 6, 5, 10, 8, 9, and 6 nodes. We randomly generated 290,000 satellite wireless AD hoc network layouts to train our agents. Of these, 20,000 layouts are used for spectrum and communication node selection based on random exploration to generate the initial experience of the agent, 250,000 layouts are used to train the agent based on spectrum selection access based on the -greedy strategy, and the remaining 20,000 layouts are used for extended training, where 0 is set. To test, we randomly generate 500 new satellite wireless AD hoc network layouts based on the same distribution, in which case the number of satellite nodes accessing the network is about 65. This paper uses the bottleneck SINR in the path as the future cumulative reward assigned Q to each state-action pair. For actual training, SINR is represented on the dB scale to get a more appropriate range of values. In addition, we added a constant bias to ensure that rewards are almost always positive. This extends the reward definition to situations where you can later add a delay-related discount factor. Specifically, the Q function is defined in terms of SINR in actual training as follows:

Figure 10: Spectrum access strategy update map based on Dueling DQN and empirical playback. coverage of 50 Mbps rates under a 100-MHz bandwidth. In summary, this study provides an effective solution and theoretical support for the coordinated development of IMT-2020 and FSS services. Methodology, M.J.; validation, S.M. ; investigation, H.W. ; writing--original draft preparation, Z.T. ; writing--review and editing, Z.J. All authors have read and agreed to the published version of the manuscript. This work was supported by National Natural Science Foundation of China No. 62231012. Data Availability Statement: Data is contained within the article. The authors would like to thank all reviewers for their helpful comments and suggestions regarding this paper. The authors declare no conflicts of interest. ## References

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