# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 5 Machine Learning as a Toolbox for NR-U  
The realization of flexibility in any of the above challenges is not practical until the domains adapt to the dynamics of the coexisting environment, such as adjusting UL/DL, neighboring cell topology information, information of coexisting cells' time, frequency domain, etc. The observations of the wireless medium and their performance or coordination with the neighboring system comprise one of the relevant research topics of optimization technology [8; 61]. We can categorize coexistence scenarios  
Figure 6: Multi-dimensional coexistence. (**a**) Coexistence gaps in one dimension (time domain); (**b**) Coexistence gaps in multiple dimensions (time and space domain); (**c**) The utilities of two coexisting networks.  
Figure 7: Coexistence of synchronous and asynchronous NR-U.  
into two: uncoordinated and coordinated schemes. In the former, networks implement coexistence mechanisms on their own without any consultation with their neighbors, whereas in the latter, systems directly or indirectly coordinate to ease their coexistence. The majority of the existing solutions are uncoordinated (local observation), as this does not require any infrastructure or change in the current network protocols. On the other hand, the coordinated solutions promise higher performance at the expense of higher complexity.  
The uncoordinated systems look simple at first glance. Still, they require more sophisticated techniques to implement a neighbor-aware coexistence scheme due to the heterogeneity of technologies or the diversity in the ownership of the IoT devices in NR-U networks. Machine learning (ML) can provide a cognition toolbox to address the related problems. ML can enable identification (classification) schemes for recognizing coexistent communication systems and their basic rules of operation. For the \"action\" aspect, it yields adaptation-oriented intelligence, e.g., activation of transmission parameters such as time or frequency for NR-U coexistence or selection of spectrum mobility actions for specific coexistence contexts. ML classification allows the radios to identify the type of neighboring networks (e.g., an LTE or a WiFi network), channels, and their characteristics (addressing heterogeneity and lack of cross-technology communication). At the same time, reinforcement learning provides action-reward-based decision support in unstructured environments. Figure 8 below shows various ML techniques that can be used for NR-U systems to provide intelligence in the NR-U system. Table 7 gives a brief overview of the ML techniques and tools that can be used for the above-mentioned NR-U challenges.  
Further, these proposed techniques also have some fundamental challenges for incorporating ML in 5G networks. The first one is complexity: low complexity is necessary for cost-efficient and practical systems. Secondly, the convergence time of decisions should be timely to provide tangible benefits for system operation. Additionally, the behavior of the coexistence scheme until convergence is vital since that may correspond to a significant portion of system time. Mobile or dynamic environments with transient responses causing a never converging ML brings the third learning challenge.  
Figure 8: ML as a toolbox for NR-U