# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 6 Graph-Based RA in CR Networks
### Lessons Learned and Trends in Development
Cooperating CPs can collaborate by aggregating just their individual RA decisions/results or GNN parameters (such as in [50]) at higher-level nodes within a network's architecture (such as a remote cloud server of an IoT/cellular network). * **A significant challenge in the implementation of GRIST is the overhead exchange between the CPs and among the sub-networks' nodes. ** It can be reduced through decentralized prediction of the wireless nodes' movement behavior, traffic, and link delay [92]. This is made possible by the availability of information at each node, as well as the requirements and conditions of the vertices connected to it [95]. The prediction process can be further incorporated into a FL scheme for sub-network RA. It has been envisioned that the power consumption of the incurred overhead exchange will be reduced through renewable energy sources, dedicated to the wireless nodes [107]. Furthermore, compression of the redundant overhead information has also been recognized as an important research direction for decentralized learning in 6G. In addressing these challenges, the design process of RA algorithms can be viewed as an optimization problem by itself, which can also be modeled via a graph and driven by particular network application (IoT/cellular/air and space communications, and so on). The graph-based model of the design process of ultra-dense networks (UDNs) from [108] is here further developed (Figure 9) to incorporate the dependencies on application requirements for RAT, BS/AP/UE densities, throughput, and delay in ISTNs. These factors, as well as the respective scenario a network operates in, will determine the weights of the edges that connect the different design aspects (vertices), with the vertex attribute describing the loss (cost) coefficients of neglecting a particular aspect (For example, the SSH vertex attribute describes the largest tolerable interference between PUs and SUs on particular channels, while the RA vertex attribute illustrates the required throughput to be achieved on the per user or network-wide basis, to ensure the efficient spectrum usage). To develop the concept presented in [108], this model expands the resource management and interference avoidance challenges that are more prevalent in traditional cellular and UDNs by including the RA, UA, SSh, and CC aspects, thus making its application broader. These four aspects comprehend the allocation, utilization, distribution of the available frequency, and power for inter-operability between various wireless technologies. They are also addressed by various methods such as network self-reconfiguration, resource distribution, multiple access, spectrum mobility, channel estimation, and spectrum sensing. The Delay and AoI, EE, and Data Caching are also addressed, in a broader sense, by the functions that ensure the efficiency of wireless networks in terrestrial, aerial, marine, and space applications. These functions require not only low algorithm complexity, but also agile energy usage depending on the traffic, definition of constraints in terms of time, and energy conservation through caching of specific data to APs that are close to the users.