## 4 AI/ML in 5G
As a rule, the IA includes two main components: beam sweeping during which the received signal strength (RSS) is measured for each beam, and beam selection which essentially is picking an optimal beam for the transmitter-receiver pair given. Usually the beam sweeping procedure takes much longer time than the beam selection, and therefore the total IA time can be improved significantly by reducing the beam sweep time via utilising fewer beams. The study attempts to solve this problem with deep learning. In particular, it proposes to pick a small subset of the beams available and measure RSS only for the beams in this subset. After that, the best beam overall is selected based only on these few RSS values measured with the help of an AI/ML model. Study [63] proposes to train an AI/ML model to find the best beam based on uplink training pilots collected at several neighbouring BSs, whereas the AI/ML-driven beam selection framework proposed in [34] uses the data from the Global Navigation Satellite System (GNSS), lidars and cameras. Employing AI/ML models can also allow for more energy-efficient power allocation. Despite there are several existing optimization methods for solving the power allocation problem such as weighted minimum mean squared error (WMMSE) algorithm, using neural networks allows for learning solution approximations which may be beneficial at the inference stage as it requires less computing resources [68, 71]. Furthermore, study [37] proposes an interesting AI/ML-based power allocation scheme which calculates the optimal power allocation policies based on the UE positions. In [67], a data-driven algorithm for dynamic BS sleeping control is proposed. The problem is formulated as an MDP and it is solved with an RL approach. Each state is traffic belief calculated with the help of a interrupted Poisson process (IPP) [122] using the Baum-Welch algorithm [123], each action is either sleep or not, the agent is awarded for each served request and penalised for each queued, retransmitted and failed one. In [82], an unmanned aerial vehicle (UAV) aided cellular framework against jamming is proposed. The study focuses on the scenario in which a jammer is located close to the current serving BS of the user and sends jamming signals to prevent the BS from receiving messages from the user. This scheme enables an UAV to optimise its relay power to help the cellular system to resist jamming without knowing the cellular topology, the message generation model, the server computation model, and the jamming model. The UAV chooses its relay power in order to maximise the utility function which depends on the bit error rate (BER) of the user message received by the serving BS, the BER of the weaker message signal received by UAV or the relay BS, and the relay cost. Another potential AI/ML application in 5G is dynamic resource allocation to radio access network (RAN) slices. The studies devoted to this problem can be divided into two main categories. The studies in the first category use the supervised learning approach to adjust the resource distribution by predicting the usage of each RAN slice [86, 118]. The alternative approach involves formulating the resource allocation problem as a Markov decision process (MDP) and solving it with a reinforcement learning algorithm [96, 97, 98, 100]. The former approach would most likely be easier to execute assuming a labeled dataset is available. However, the resulting prediction model would not be able to take into account the effect of the resource allocation decisions on the network environment. The RL approach is therefore probably more suited for such a dynamically complicated task even though the model training in this case requires implementing a simulation software and shaping an efficient reward function as mentioned in Section 3. AI/ML can also be a good alternative for intelligent scheduling.