# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 1 Introduction  
The efforts to define beyond-5G and 6G performance targets are emerging [1], envisioning high capacity links with low end-to-end latency, network softwarization, and massive connections for the future network architecture. In addition to network densification with small cells [2], millimeter wave (mmWave) radio and optical communication are considered as key enablers of an envisioned beyond-5G network ecosystem [1]. Merging the optical and radio channels can further increase the efficiency of the radio access network (RAN), and techniques, such as analog radio-over-fiber (ARoF) are promising solutions towards this direction [3]. These enablers, however, also increase the number and type of access point (AP) nodes (small cells, macro cells, remote radio heads (RRHs), roadside units, etc.) [4, 5], diversifying the parameters to be included in resource management decisions together with the dynamism of massive communications. The complexity further increases as the multi-dimensional resources of dense networks include not only frequency, time, power, space, and multi-user diversity but also involve energy, computation and storage resources [4], making resource management a key topic in the next-generation communication systems. The increase in data traffic and the number of users, devices and network components also lead to a huge increase in data which can be analyzed with machine learning (ML) techniques for optimization in user quality of service (QoS), network management, and service provisioning. The increasing number of unknowns in the system also creates an interest in the use of learning techniques in network management. This fact motivated us to conduct a review to determine the key performance indicators (KPIs), performancetargets, and the optimization algorithms in the published works. Another goal of this review is to identify the methods adapted to converged optical and mmWave radio networks, understanding the complexity of the algorithms and the diversity of techniques implemented with an overview on related activities. Finally, Artificial intelligence (AI) contributions have a tremendous impact on the design and development of autonomic networking solutions [6]. Thus, we also aim to detect the steps to be taken from applying the ML techniques to providing a holistic autonomic (re)configuration for mmWave optical and radio networks. The solution methods are categorized under the main objectives of throughput maximization, delay minimization, energy-efficiency, and virtual resource allocation. Technological enablers and infrastructural changes resulting from the implementation of these novel network paradigms also affect the way that resource allocation algorithms are designed. For this reason, we put special emphasis on the relation between the resource management solutions and the technological enablers that led to a paradigm shift in network management, such as software-defined networking (SDN), network function virtualization (NFV), and multi-access edge computing (MEC). The remainder of this article is structured as follows: The background concepts of mmWave networks and AI/ML used in communication networks under autonomic network management (ANM) framework are presented in Section 2. The preparation, operation, and reporting stages of the survey are explained in Section 3 to reveal how we planned and conducted the survey. We also present an overview of the selected papers based on their optimization algorithms, performance metrics, and the evaluation criteria in this section. The results of our survey are categorized under the main optimization objective topics and presented in Sections 4-7, respectively. A discussion specific to each of these optimization topics is also provided in these sections. Section 8 concludes the paper by summarizing our findings and providing a pointer for future research.