# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services
### Radio Management  
The rudimentary concepts of machine learning are reviewed in [123] and the authors proposed ML employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-to-device communications, and so on.  
In [124] the authors proposed an efficient online Channel State Information (CSI) prediction scheme for predicting CSI from historical data in 5G wireless communication systems. Specifically, they first identified several important features affecting the CSI of a radio link and a data sample consisted of the information of these features and the CSI. They designed a learning framework that was a combination of a CNN (convolutional neural network) and an LSTM (long short term with memory) network.  
A deep learning-based pilot assignment scheme (DL-PAS) for a massive MIMO system is proposed in [125]; the system utilized a large number of antennas for multiple users. The proposed DL-PAS improved the performance in cellular networks with severe pilot contamination by learning the relationship between pilot assignment and the users' location pattern. The authors designed a novel supervised learning method, where input features and output labels were users' locations in all cells  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Management Area** & **Prediction** & **Anomaly Detection** & **Clustering or Classification** \\ \hline \multirow{2}{*}{**Fault management**} & Fault Prediction; Automated Mitigation & Fault Detection; Root Cause Analysis & \multirow{2}{*}{Alarm Correlation} \\ \multirow{2}{*}{**Configuration m.**} & Resource Optimization & Configuration & Realizing similarities \\  & (SDN, Base Station power adj., Cloud resource allocation) & pattern recognition & or differences in node configs \\ \hline \multirow{2}{*}{**Accounting m.**} & Churn prediction; Service utilization prediction & \multirow{2}{*}{Misuse or Fraud} & Traffic characterization; Usage profiling \\ \hline \multirow{2}{*}{**Performance m.**} & Utilization prediction (feeding Config. mgmt.) & Detecting under- or over-utilization of segments & Resource Planning QoS and QoE correlation \\ \hline \multirow{2}{*}{**Security mgmt.**} & Intrusion Prevention & Detection of suspicious activities DDoS Detection & Intrusion Detection \\ \hline \hline \end{tabular}
\end{table}
Table 10: Deep Learning application examples of FCAPS Management tasks in the mobile telecommunications domain [120].  
and pilot assignments, respectively. The proposed DL-PAS provided a near-optimal pilot assignment from the produced inferred function by analyzing the training data.  
In [126], an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) was proposed. In particular, each UAV aimed at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem was cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, was proposed. The introduced deep ESN architecture was trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV used ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm was shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence.