## 4 AI/ML in 5G
For instance, study [39] formulates the resource allocation problem in cellular long term evolution (LTE) communications in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) as a non-cooperative game. The players in this game are the small BSs (SBSs) which learn autonomously which unlicensed channels to use. The framework proposed by the authors solves this game problem using policy-based reinforcement learning with the LSTM-based model as the policy function. The solution allows the SBSs to learn each channel access probability taking into account future environmental changes caused by LTE-LAA traffic loads and additional WLAN transmissions. The authors in [117] propose a scheduler to infer the free slots in a multiple frequency time division multiple access (MF-TDMA) network to avoid congestion and high packet loss. Study [72] focuses on the problem of scheduling high volume flexible time (HVFT) application traffic in future computer networks. A common property of HVFT applications is that the mobile network operator must serve a large volume of traffic during the day but has significant flexibility in scheduling this traffic, i.e., these applications can tolerate delays on the order of a few hours up-to a day. The problem is formulated as an MDP and it is solved with DDPG. In addition, AI/ML can also be a promising feature for routing optimization. For example, a framework for smart wireless network management is proposed in [83]. In particular, the study focuses on the scenario when a user moves in an area with multiple access-points (APs), and only associates with one AP at a time. APs can monitor the received signal strength indicator (RSSI) of uplink user traffic data. The target is to find an optimal access policy for the user while maximising the user's throughput. The experiments in the study are conducted using a real-time software-defined networking (SDN) based heterogeneous wireless network testbed [124]. In [73], a framework for online routing in optical transport networks (OTNs) is proposed. The authors consider the scenario in which an agent operates over a logical topology composed of reconfigurable optical add-drop multiplexer (ROADM) nodes and some predefined lightpaths connecting them. The role of the agent is to route incoming traffic demands through particular sequences of lightpaths. The problem is modelled as an MDP and it is solved with TRPO. Furthermore, cognitive radio capabilities empowered by machine learning allow for performing spectrum awareness and spectrum sharing. For example, in study [9], an AI/ML model at an environmental sensing capability (ESC) station detects citizens broadband radio service (CBRS) as an incumbent user. If such service is not observed in the channel under consideration, the ESC allows a BS to communicate to UEs. Otherwise, the BS cannot use this channel and it is reconfigured to vacate this channel to avoid interference with transmissions of the incumbent user. Finally, security of mobile networks can be enhanced significantly by employing state-of-the-art AI/ML models for anomaly detection [41, 45]. The resulting intrusion detection systems (IDSs) can be used to classify the vectors of features extracted from network traffic flows as either benign or malicious. In the RAN domain, the main security application of AI/ML is jamming detection [47, 74]. In this case, an AI/ML model is trained to distinguish between the signals transmitted by normal devices and the one broadcast by the jammer. The input features may include the location of the device sending the signal, SNR value, traffic volume, frequency of transmissions and several others.