## 6 Optimization Scenarios for 6G MCNs
### Dynamic Spectrum Allocation in Heterogeneous Maritime Environments
This optimization function has two competing components, namely (i) the first term rewards configurations that yield higher cumulative data rates across the network and (ii) the second term penalizes total interference generated by nodes sharing the same frequency channel, especially when they are within interference range. The goal is to find a balance between maximizing network throughput and reducing mutual interference, which is a typical utility-minus-cost structure used in resource allocation problems. The solution should also comply with constraint (C1), implying that each node can be assigned at most one frequency channel, and (C2), reflecting that each node must meet a minimum required data rate, which may depend on its role (e.g., control UAVs may need higher guarantees than monitoring sensors). To find a (sub)optimal solution to problem (3), several iterative, heuristic or AI-based algorithms can be used. Below, we analyze two widely used approaches to tackle this problem:  
1. _Graph Coloring and Greedy Heuristics:_ This approach models the network as a conflict graph, where nodes represent transmitters and edges represent potential interference (i.e., two nodes within a range that cannot use the same channel) [87]. The graph coloring problem assigns frequencies (colors) to nodes so that adjacent nodes (interfering nodes) receive different or orthogonal channels. One approach may be Greedy Coloring, which sorts nodes by degree (number of neighbors) and then assigns to every node the first available frequency that does not conflict [88]. Another option could be Weighted Heuristics, in which we use metrics such as required throughput or signal strength to prioritize channel assignment. Key advantages of both are (i) their fast, interpretable, and easy implementation and (ii) their suitability for near-real-time decisions in static or slowly varying maritime environments [87, 88]. 2. _RL-based Solution:_ In dynamic settings with mobility (UAVs, ships) and time-varying interference, RL enables nodes to learn optimal spectrum usage policies over time [85]. In this framework, each communication node (e.g., a ship or UAV) can be seen as an agent that observes a state and performs actions that lead to a predefined reward. The state vector may describe the local channel occupancy, recent interference levels, and QoS status of the node. The agent, upon reading the state, selects a frequency channel from the set \(\mathcal{F}\). Then, the environment returns a reward to reflect whether the action was beneficial or not. This rewarding function can combine the achieved throughput (positive reward) and experienced interference or dropped packets (penalty). For large state-action dimensionality, the DRL method can be considered, where a neural network estimates the \(Q\)-values for each state-action pair [85]. During the training, the goal is to maximize the accumulated long-term reward. As a result, upon a series of training episodes, the agent converges to an optimal solution according to which the best frequency channel is selected for any given state [86]. Noteworthy, one of the key barriers to deploying flexible and AI-driven resource optimization schemes in MCNs lies in spectrum regulation and licensing constraints, which vary across maritime zones and national jurisdictions. The maritime spectrum is often governed by ITU-R allocations, but in coastal waters, frequency usage must also conform to national telecom regulations, creating the risk of interference with terrestrial cellular networks or spectrum overlap with nearby countries. For example, dynamic spectrum allocation or UAV-assisted relaying may cause interference to mobile base stations on shore unless carefully coordinated. These challenges necessitate the integration of policy awareness in optimization algorithms and the development of regulatory-compliant AI agents that can adapt transmission behaviors based on geographical and legal boundaries.