## 1 Introduction  
Aerospace vehicles often require multiple connectivity applications for navigation, telemetry, communications, and a host of other applications [1]. As shown in Figure 1, these links may be through air-to-ground, satellite, and other high-altitude platforms and networks [1]. Legacy systems that use dedicated hardware devices for each link can be difficult to integrate and maintain. Interconnecting these systems can require wiring harnesses that substantially increase the weight and cost of the vehicle [2]. These costs, coupled with the complexities of modifying, integrating, troubleshooting, and maintaining new applications within hardware-dependent systems--especially those with extensive scaling--pose significant operational challenges [3, 4]. [PERSON] et al. [5] demonstrated that an SDR-based integrated modular avionics architecture could significantly reduce the size, weight, power usage, and cost in an aircraft. Software-defined radios (SDRs) allow designers to support multiple communication protocols on a single hardware platform. An SDR can generate multiple baseband waveforms for the various links they support and then, in a single operation, up-convert and transmit them using a single hardware transmitter [6]. By summing signals in the digital domain, no additive noise is introduced prior to the digital-to-analog converter, ensuring a clean composite waveform. The flexibility of reprogramming an SDR without hardware changescan give designers greater flexibility and assist in the troubleshooting and maintenance processes [3]. Multiple radio waveforms can be generated in a single SDR as illustrated in Figure 2. In this system, the SDR generates waveforms for each application and then sums them into a single digital waveform [3]. A digital-to-analog converter (DAC) produces an outgoing composite analog signal, which is up-converted and transmitted. To minimize quantization error while still avoiding the nonlinear clipping of large signals, it is important to carefully select the input gain of the DAC. The composite signal's peak amplitude is a key determinant of the optimal gain setting (\(G=\frac{\alpha}{u}\)), where \(\alpha\)V represents the DAC's range and \(u\)V denotes the peak voltage of the composite signal, as further illustrated in Figure 3.  
Estimating the DAC's gain for an individual signal is relatively straightforward, as the peak amplitude is generally well understood, even considering distortion from a pulse-shaping filter [3]. The estimate can become challenging for composite signals due to the unpredictable superposition that occurs when an SDR must sum signals from multiple applications to form a composite signal. One method for gain estimation involves a closed-loop system, adjusting the gain after the signal has been observed for some time. The signal  
Figure 1: Typical communication links of an aircraft [3]. Figure 2: SDR-based implementation of an aircraft’s communication links [3]. An SDR generates and sums multiple baseband waveforms, and the resulting composite signal is then amplified and up-converted for transmission. may suffer from high quantization noise, or clipping, during the time required for the loop to reach the optimal level. The SDR could perform this operation in advance of having to transmit data, but that could substantially increase the SDR's computational load. To overcome this challenge, we propose a deep learning (DL)-based technique to estimate the statistical distribution of composite signal amplitudes. Our method relies exclusively on the parameters used to generate the component signals, such as modulation type, power levels, data rates, normalized frequency, and the number of applications, thereby eliminating the need to generate or observe the composite signal to estimate DAC's gain [3]. This approach dynamically adapts the gain of the DAC to rapid changes in SDR configuration, ensuring use of the DAC's full range across varying scenarios.