The work [25] deals with frequency-selective channel striving to simultaneously maximize the mean network SNR and spectrum efficiency. For flying ad hoc networks, numerous algorithms of simultaneous optimization of trajectory and network throughput are proposed. These algorithms allocate some bandwidth for trajectory negotiation [26]. In delay-tolerant ad hoc networks, channel resources can be traded for storage resources. The work [27] considers the transmission resource and the storage resource allocation as a queuing process. This work derives the expressions for per-node throughput, expected end-to-end delay, and network throughput. The analysis of derived expressions gives the optimal setting for resource allocation. Substantial number of works employ artificial intelligence (AI) for resource allocation [28]. The work [29] uses the self-organizing feature-mapping model to process multi-dimensional data of the network state to perform multi-associated parameter aggregation-based routing and resource allocation. The work [30] uses the deep neural network attention mechanism and Q-learning for the resource allocation in the smart city hierarchical edge computing network. The work [31] uses dynamic unsupervised fuzzy clustering to find all resource combinations for the different network services. The work [32] uses recurrent neural networks for joint scheduling and power control. The work [33] proposed a graph kernel-based support vector machines method for the link scheduling. Many works formulate the resource allocation problem as a maximum weighted independent set problem [34] and solve it with AI approaches. The work [35] uses deep learning and fuzzy logic, the work [36]--uses a tree-based graph convolutional network, and the work [37]--uses a tree-based graph convolutional network with deep Q-learning. The works [38; 39] have analyzed the following problem: given the set of lines and their powers, what lines are allowed to transmit to reach the maximal sum of the lines' throughput. The work [38] showed that if all line transmission powers are lower than critical powers for each line, then the sum of line throughputs (network capacity) is larger than the throughput of one line from the set. If the power is larger than the critical power, then the throughput of one line will be larger than the sum, because of the interference, so only one line at a time should be allowed to transmit. The work [39] has expanded the work [38] and proved that subsets of multiple transmitting lines can maximize the sum of line throughput. However, the authors have not shown how to search for acceptable line subsets. Many works assume a binary or graph interference model [35], where specific pairs of wireless nodes interfere with each other or not. In contrast, the physical-based interference model assumes that all nodes emitting radio waves interfere with each other. The physical based interference model is based on signal power and propagation decay [40; 41; 42]. The physical-based model can reflect reality better than the graph-based model. However, global line interaction in the physical-based model turns resource allocation into an NP-hard problem [40]. By partitioning the network area into subspaces or by partitioning the lines into subsets, global interference becomes localized. The work [43] partitions line set into two sets: the lines that are close to the maximum transmission range of senders and other lines. The work [44] partitions the network area into large squares and then partitions each large square into small squares. Next, the work utilizes partition and shifting strategies to find multiple sets of geometrically well-separated links. The work [45] uses the hypergraph approach to partition links into sets. The work [46] is based on the fact that a short link can tolerate large interference while a long link tolerates small interference. The work partitions all links into disjoint local link sets that are spatially separated from each other.