# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 2 Background Concepts
### Autonomic Network Management  
Compared to existing wireless networks, a much more dynamic environment is foreseen due to mmWave propagation characteristics and massive deployments of small cells, leading to a dynamically changing large scale network topology. As a result, providing a stable network communication that is able to withstand varying network conditions will become more complex, pushing any human dependent network management approach out of the equation.  
A number of approaches to transfer the intelligence to mobile networks have been around for many years from auto-configuration of network entities [29] to self-optimization of network operations [30], leading to the ANM paradigm for anticipating and diagnosing impairments in networks driven by operation and management (O&M) layer goals [31]. The emergence of SDN also contributed to network optimization efforts by offering programmability and network system reconfigurability to today's networks by decoupling the control plane from data plane [32]. With the support of NFV and cloud technologies to SDN, softwarization and programmability are considered as the main enabling technologies to manage the dynamic topologies in a responsive way and achieve 5G traffic requirements [33].  
An autonomic networking framework for 5G and beyond requires translating of high-level O&M goals into low-level technical parameters and then monitoring the network to adapt network status by making use of observations from the monitoring stage [6]. The management paradigm shifts towards avoiding the rigid properties and the limitations arising from the difficulties in modeling the entire state space beforehand. The type of data to be collected for monitoring, the possible actions and control rules, the learning algorithms and data analytics functions should also be defined properly, resulting in a control loop through which multiple stakeholders can become involved in decision-making inside a hierarchical functional decomposition.  
All these ANM framework requirements and the increasing interest in AI/ML-assisted approaches to resource orchestration and optimization led to the efforts of ITU-T [34] and 3 GPP [35] to define the architectural framework for integrating ML solutions to 5G and beyond networks, whereas exploiting AI solutions in every network segment possible to learn and adapt to network dynamics is conceptualized as the 'AI Everywhere' principle for networks [1; 36]. The 3 GPP has introduced the network data analytics function (NWDAF) and the management data analytics function (MDAF) for core services [37] that can be used for centralized optimization of the network resources. On the other hand, the 5G RAN data analytics function (DAF) is proposed for radio resource management [38], which targets managing machine learning and AI solutions in the RAN with open interfaces inside the O-RAN project [33]. RAN DAF can also be extended with local monitoring lightweight data analytics capabilities for decision-making at different types of RAN nodes inside a distributed and hierarchical framework [39], allowing local and distributed resource management optimizations to take place inside the network.  
In light of these technological developments, the authors' objective in this study are to identify the benefits and the barriers of the existing resource management methods for converged optical and mmWave wireless networks, and to discuss the open issues from an ANM perspective.