# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 4 Throughput Maximization and Resource Allocation Algorithms
### Discussion  
The increasing number of external stakeholders and the need for information exchange among the mmWave radio and optical networks is already reflected in several works [42; 43; 52; 54; 57]. Considering the expanding level of information with the increasing number of users, nodes, antennas in massive MIMO mmWave networks, distributed federated learning approaches provide a novel optimization solution on top of the existing beamforming and DBA solutions. Among the joint optimization algorithms for radio and optical access and transport resources, we were not able to detect any federated learning models, which can overcome the information exchange dependencies of the stakeholder interaction models listed in Table 3. This might be due to the fact that federated learning is considered a bandwidth-consuming method with the model updates requiring the transmission of many parameters that scale with the size of the access node deployments [60]. However, the capacity boost provided by the optical transport network for massive UDN deployments can overcome this limitation, thus providing a future research direction for resource optimization in radio and optical mmWave networks.  
Regarding interference mitigation, the selected algorithms grouped under this category in Section 4.1 have complexity issues with the increasing number of users. Predictive analytics can, therefore, help in interference handling by monitoring user behavior and traffic load variation in the mmWave radio and optical networks [38]. However, the computational cost of learning from inter-channel information to mitigate inter-cell interference at the access nodes of dense massive MIMO systems should also be considered for throughput maximization [61]. A future research direction for throughput maximization might therefore be creating a multi-layer learning framework [6] to adjust fronthaul resources by learning the aggregate radio access node interference behavior.  
The evaluation criteria in Table 3 show that the number of users are monitored frequently in throughput maximization solutions, bringing the integration of user demand-driven ML solutions, such as the deep reinforcement learning throughput maximization algorithm used to achieve a minimum throughput target of 1 Mbit/s for 50 users [62]. The throughput scales well above 1 Mbit/s with the available bandwidth in mmWave frequencies, dense deployments and the use of MIMO; making eMBB applications such as V2X and mobile augmented reality the use case targets of mmWave radio and optical networks. For this reason, reinterpreting such ML solutions should be considered carefully as mmWave radio and optical network deployments aim to achieve significantly higher the throughput targets, e.g., 1 Gbit/s for eMBB use cases such as V2X collective perception [63].  
Due to the scaling up of the network elements and the dependent increase in the amount of information, ML applications were discussed for massive MIMO, cognitive radios, heterogeneous networks, and small cells at an early phase of the convergence of AI-ML methods and communications technologies [64]. The number of access nodes and antennas are also considered are evaluation parameters in the selected papers [53; 55; 58]. These parameters should be considered not only in the operations and management phases, but also during the cost-effective planning and pre-deployment phases of mmWave access and transport networks. Finally, the communication-efficient methods to push training processes of AI models to edge nodes [65] should also be considered to increase the distributed network control capability and make use of the increasing amount of information at the edge.