## 3 ML for SatCom RRM: Architecture and Techniques
### Supervised Learning for RRM
#### 3.2.3 Cnn-Based RRM: Power and Bandwidth Flexible  
A CNN architecture, generally considered in image classification problems [44], is proposed to adapt it to solve the RRM problem, in this case for power, bandwidth and beamwidth management [33]. The adaptation of the traditional CNN architecture is performed at the input layer and the output layer. At the input layer of the CNN, there is a matrix tensor, and each matrix represents the traffic demand at each geographic location in the service area. The service area does not have a regular geometric shape; thus, the geographic coordinates contained in each matrix outside the service area are zero-filled. In this proposed work, the matrix tensor does not represent the channels of an image but the time instants (states) at which the system is evaluated. That is, the depth of the matrix tensor is given by vector \(\{t,t-1,t-1,t-2,...,t-T\}\), where \(t\) is the current time instant, and \(T\) is the size of the time window and represents the size of the observed states for RRM.  
Depending on features extracted by the convolutional layers, the constraints and the payload flexibility, there is a set of possible configurations for the resource allocation in beams. These possible configurations are encoded in a vector of size L, representing the number of possible configurations of the payload resources. Taking advantage of this, the CNN has at the output layer the configuration that minimizes the cost function in (1) for the input layer conditions.  
A schematic of the different layers of the CNN is shown in Figure 6. A matrix tensor starting with the traffic demand at each geographic coordinate is the input layer. Then, the convolution layers are used to obtain the main features of the traffic demand at the geographic coordinates. Full connection layers are used to map the resource configuration with the features obtained in the convolution layers. The output layer results in a resource allocation that minimizes RRM cost function (1). In the convolutional layers, the features are extracted from the kernels obtained at the output \(Y_{j}\), as represented in the following:  
\[Y_{j}=f_{1}(p_{j}+\sum_{i}K_{ij}\otimes Y_{i}) \tag{10}\]  
where \(Y_{j}\) represents the output of the \(j\)th neuron and is a matrix computed as the linear combination of the outputs \(Y_{i}\) of the neurons in the previous layer. Each operated on with the convolutional kernel \(K_{ij}\) corresponding to that connection; this quantity is added to a con  
Figure 5: Data generation to DNN training for Beam Hopping (BH) Systems.  
nection \(p_{j}\) and then passed through a nonlinear activation function \(f_{1}(\cdot)\). The convolution operator has the effect of filtering the input matrix with a previously trained kernel.  
The CNN that manages resource allocation is the core of the RRM. The CNN determines how to match resources to a demand pattern while minimizing payload resource consumption. Network training is performed offline; thus, the CNN represents an intelligent switch that functions as DRM for the satellite communication system.