## 4 AI/ML in 5G
Another interesting application of AI/ML in mobile networking is related to channel coding. For example, study [79] interprets the entire end-to-end communication system as an autoencoder which learns robust representation of the messages transmitted with respect to the channel distortions allowing for its recovery with a small probability of error. The study also proposes using radio transformer network (RTN) which is a modified attention-based spatial transformer applied in computer vision [121]. Further studies [61, 80] use AI/ML for the signal decoding. To generate training samples, the authors of both studies pick an information vector from codebook set randomly and then perform channel \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Ref.** & **Description** & **Data and Features** & **Algorithm** & **Modus Operandi** \\ \hline \multirow{6}{*}{[45]} & \multirow{6}{*}{Logos for an } & \multirow{6}{*}{Network traffic} & \multirow{6}{*}{Supervised: } & Collect traffic flows from backhaul and core network links; transform feature vectors into images; select an optimal CNN architecture using RNN; train the resulting CNN model; use the model trained to classify network traffic flows \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & \multirow{6}{*}{Classifies jamming} & \multirow{6}{*}{Node state} & \multirow{6}{*}{Supervised: } & Collect traffic; extract features; train a neural network to classify samples as either normal or corresponding to a particular class of jamming; feed the samples classified as normal to an SVM; train the SVM to classify the samples as either normal or malicious; use the model trained to identify and classify jammers \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & \multirow{6}{*}{Detects and classifies jamming} & Subcarrier spacing, symbol time, subcarrier length, cyclic prefix & Supervised: } \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._encoding, apply the binary phase shift keying (BPSK) mapping and simulate channel noise in order to obtain the received vector. After that, the received signal acts as the input to the neural network whereas the decoded signal as the output. Study [60] uses RL to find effective decoding strategies for binary linear codes. The study focuses on bit-flipping (BF) decoding which is based on constructing such a metric that allows the decoder to rank the bits based on their reliability depending on the code constraints given. As a rule, BF uses the hard-decision output and iteratively looks for the bit that, after flipping it, would maximally reduce the number of currently violated parity-check (PC) equations. The decoding method proposed is mapped to an MDP and it is solved with Q-learning. In [81], a decoding iterative neural network based architecture for linear codes is proposed. It concatenates the CNN trained with a belief propagation (BP) decoder and iterates between them. A block of uniformly distributed bits is encoded to a binary codeword through a linear channel encoder. The codeword is then mapped to a symbol vector through the BPSK modulation which in turn is passed through a channel with additive Gaussian noise. A BP decoder is then used to decode the transmitted information bit vector from the received vector to estimate transmit symbols. A CNN is trained to estimate the channel noise. The procedure is iterative: the new version of the received vector is constructed using the noise estimated with the CNN and fed back to the BP decoder and another round of BP decoding is performed. AI/ML can also be employed for beamforming. For example, study [64] aims to improve the initial access (IA) procedure with the help of machine learning.