## 1 Introduction
Deep learning (DL) has grown in popularity in recent years due to the dramatic increase in the availability of computing power [7; 8]. This success has led to an increased interest in the application of DL in communications and signal processing [9; 10]. For instance, convolutional neural networks (CNNs) have been widely used for automatic modulation classification (AMC) by leveraging input features like constellation diagrams [11] and long-symbol rate signals [12]. [PERSON] et al. [13] generated hybrid images using scalograms and IQ plots to reduce training time through transfer learning. Similarly, CNNs have been applied for channel state information (CSI) estimation by treating the time-frequency response of fast-fading channels as 2D images [14], while advanced architectures have integrated CNNs with long short-term memory (LSTM) networks for 5G wireless systems [15]. Attention-based mechanisms have also been employed for CSI estimation, using transformer encoder architectures to focus on critical features and improve performance [16]. Beyond CSI estimation, [PERSON] et al. [17] demonstrated the use of deep neural networks (DNNs) for end-to-end channel estimation and symbol detection in orthogonal frequency-division multiplexing (OFDM) systems. Additionally, [PERSON] et al. [18] trained an LSTM-based recurrent neural network for radio anomaly detection by predicting time-series data and comparing the expected error distribution with a well-characterized signal for non-anomalous behavior. [PERSON] et al. [19] proposed an attention-based deep separation network for co-frequency modulated communication signals. Another area where deep learning has shown promise is in regression-based applications, where deep neural network (DNN) regression models are used to estimate relationships between input and output variables. In particular, regression analysis is useful for predicting continuous-valued estimation targets based on a given D-dimensional vector \(\mathbf{x}\) of independent input variables [20]. [PERSON] et al. [21] demonstrated that a DNN can  
Figure 3: Time domain representation of a composite signal. Its peak amplitude, \(u\), is key to selecting the DAC’s input gain, \(G\), so that the waveform uses the DAC’s full range. approximate signal processing algorithms by treating the input-output relationship as an unknown non-linear mapping, with training samples generated through optimization algorithms. This technique has also been applied in mobile terminal usage pattern estimation [22], as well as for CSI estimation [23]. The predictive power of regression analysis in estimating continuous variables makes it particularly useful for the task of estimating the statistical distribution parameters of composite signal amplitude levels in this study [3]. This article is an extension of [24], where the authors demonstrate a method to estimate DAC's gain using statistical moments. This article demonstrates that a composite signal generated by summing signals with different modulation techniques, power levels, data rates, and normalized frequencies has voltage levels that adhere to the GGD [25]. We simulate a dataset of 100,000 composite signals, calculating their respective GGD parameters. This dataset is utilized to train a DNN for regression, where the inputs are modulation technique, power level, data rate, the normalized frequency of each component signal, and the count of signals combined. The DNN's output targets are the parameter values of the composite signal's GGD. The remainder of this paper is organized as follows: Section 2 discusses the generation of composite signals and their statistical distribution. Section 3 outlines the application of DNN regression for estimating the statistical distribution parameters of the summed signals. Section 4 presents simulation results for the proposed multi-output DNN regression technique and compares these with other regression methods. Finally, Section 5 concludes the paper.