# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 2 System Model
### AI-Driven Pilot Placement
#### 2.2.2 Supervised ML  
In supervised machine learning, the model is trained on a labeled dataset. During the training process, the model learns from input data that are already tagged with the correct output. The goal is for the model to learn the underlying patterns in the data so that it can accurately predict the output for new, unseen data. In the context of reducing pilot overhead, a supervised machine learning approach can help reduce this overhead by leveraging prior observations of the channel to predict the optimal RF beamforming/combiningvectors directly from the channel measurement vectors. This is achieved through a neural network architecture known as auto-pre-coder, which is trained end-to-end as a multi-task classification problem. The auto-pre-coder network is designed to optimize the transmitter/receiver measurement vectors in an unsupervised way to focus the sensing power on the most promising directions. Linear regression, in particular, aims to build a model that designs the correlation between one or more input characteristics (independent variables) and a continuous target variable (dependent variable). The algorithm forms a linear equation that optimally represents the dataset, enabling it to predict new and unobserved target variables on the input characteristics [14]. The objective of this method is to estimate the channel characteristics from a reduced set of pilot signals. This is achieved through a five-step process: data preparation, feature selection, model training, model prediction, and optimization. In the first step, data are collected. The data consist of pilot signals and corresponding channel characteristics such as channel capacity and number of streams. In the second step, relevant features such as pilot signals, SINR values, and other relevant parameters are identified. Then, linear regression is used to train a model. The model uses pilot signals to predict the channel characteristics. Based on the data, a relationship between the input pilot signals and the output channel characteristics is learned. Once the model is trained, it becomes capable of making predictions about channel characteristics for new pilot signals. Afterwards, these predictions are used for channel characteristics estimation to transmit data without requiring full pilot overhead. Linear regression model is represented by the following equation:  
\[\hat{y}=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\ldots+\beta_{n}x_{n} \tag{13}\]  
where \(\hat{y}\) is the predicted channel characteristic; \(x_{1},x_{2},\ldots,x_{n}\), are the input pilot signals (features); \(\beta_{0},\beta_{1},\beta_{2},\ldots,\beta_{n}\) are the coefficients or parameters that are learned by the linear regression model. Finally, for optimization, the coefficients \(\beta_{0},\beta_{1},\beta_{2},\ldots,\beta_{n}\) undergo a process of optimization throughout the training phase in order to achieve a reduction in the prediction error, which is calculatedby comparing the predicted channel characteristics and the actual channel characteristics observed in the training data. Random forest regression is a supervised machine learning algorithm that is part of the ensemble learning methods. Specifically tailored for regression tasks, it aims to predict a continuous outcome variable by leveraging one or more predictor variables. Random forest regression creates multiple decision trees, each trained on a random subset of the data and features. The final prediction is the average of all the trees' predictions [19]. In the realm of minimizing pilot overhead in beamforming systems, random forest regression offers a viable approach for predicting optimal pilot arrangements based on input parameters like channel characteristics and system specifications [20].