## 4 AI/ML in 5G
** & **Description** & **Data and Features** & **Algorithm** & **Modus Operandi** \\ \hline \multirow{9}{*}{Power allocation} & \multirow{3}{*}{[65]} & \multirow{3}{*}{ \begin{tabular}{c} Overloms beam \\ training based on \\ measurements for a \\ subset of codewords \\ \end{tabular}  } & \multirow{3}{*}{\begin  
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**Category** & **Ref. ** & **Description** & **Data and Features** & **Algorithm** & **Modus Operandi** \\ \hline \multirow{8}{*}{[72]} & \multirow{8}{*}{Schedules HVFT application traffic} & KPIs: cell congestion, number of sessions, cell efficiency & Reinforcement: DDPG with fully-connected neural network & Initialize an RL agent; measure the KPIs; sample an action; estimate the average throughput using an RF model trained on the real data; use the throughput estimation to calculate the reward; update the agent’s network parameters \\ \cline{1-1} \cline{2-5  
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**Category** & **Ref. ** & **Description** & **Data and Features** & **Algorithm** & **Modus Operandi** \\ \hline \multirow{8}{*}{[100]} & Formulates the network slice & Numbers of users using different slices & Reinforcement: & Initialise an RL agent; observe numbers of users using the network slices under consideration; sample the bandwidth allocation policy using the agent’s policy network; measure the network throughput; update the agent & \\  & Formulates the resources, network slicing & Resources in use, the serving node, the task priority, the resources & Reinforcement: & Initialize an RL agent; observe values of the features; select the fog node for the task or reject the task using the Q-network; calculate the DQN & reward function; store the experience in a replay buffer; sample an experience from the buffer and update the agent network parameters \\ \cline{2-5}  & & & & \\  & & & & \\  & & & &  
One potential application of AI/ML in 5G is improving channel state information (CSI) estimation procedure. For example, studies [28, 76] suggest to reduce the pilot training and feedback overhead in an orthogonal frequency-division multiplexing (OFDM) system by training an AI/ML model to calculate CSI in the downlink (DL) based on the past uplink (UL) measurements. The reasoning behind the idea is that since DL and UL signals are propagated in the same environment, the former can be estimated with some level of accuracy given the latter. Alternatively, AI/ML can be used to reduce dimensionality of the CSI matrices and therefore decrease the transmission overhead as it is proposed in [77]. In [57], AI/ML is used for estimating UL channels for massive MIMO systems with mixed analog-to-digital converters (ADCs). AI/ML is also proposed to be deployed for automatic modulation classification (AMC). This allows transceivers to use the channel conditions to select the modulation coding scheme (MCS) without the need for a feedback channel between the transmitter and the receiver. For example, study [59] aims to develop a robust AMC algorithm with the help of a fully-connected neural network. Input features used for the prediction are extracted from the received signal and they include amplitude variance, maximum value of the power spectral density (PSD) of the normalised centred-instantaneous amplitude, in-band spectral variation and deviation from unit circle, and higher order statistics including cumulants and estimated signal-to-noise (SNR) values. In [31], the deep learning approach is used to extract features from symbol sequences automatically. Similarly, study [78] employs a deep learning architecture, namely AlexNet [120], to process the signal samples and retrieve the MCS used. In [32], another data-driven model for automatic modulation classification is proposed. The data input is either the received signal represented in I/Q format which includes two components: in-phase and quadrature or the averaged fast Fourier transform (FFT) magnitude information.