# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications
### Use Cases
#### 3.1.2 FEC in Regenerative Payload  
Forward error correction (FEC) [22] decoding algorithms such as low-density parity check (LDPC), Reed-Solomon, or polar codes are crucial elements of modern digital communications based on 3 GPP and DVB standards. These decoding schemes require high implementation complexity and power consumption to be integrated into full regenerative satellite payloads. Therefore, this use case is motivated by AI acceleration through ML algorithms to reduce the complexity and, thus, the power consumption of FEC decoding algorithms on board satellites. A conventional FEC approach is based on maximum a posteriori decoding in which ML application is restricted to shortcodes due to the exponential training complexity [23]. In addition, sometimes, an unknown distribution of the channel noise makes training difficult. The most popular algorithm applied to conventional FEC decoding with ML is the belief propagation (BP) algorithm [23], trained as a neural network to improve error correction performance. However, its application on regenerative payloads and SatCom links has not been analyzed. In this scenario, a complexity reduction in FEC decoding for small power-limited regenerative satellites with a reduced number of datalinks is expected. Then, this approach may also be scaled up to future regenerative ultra-high-throughput satellites (UHTSs) with thousands of datalinks requiring simultaneous FEC decoding. A significant decrease in the power consumption of such a case will also lead to an increase in the available power budget for other satellite subsystems or to a decrease in the mass, and hence, launching costs. Typical decoding relies on maximum a posteriori decoding, which consists of computing the probability that a specific bit is 0 or 1 and selecting the hypothesis with a higher probability. The two main drawbacks of typical decoding are (i) the computational complexity and (ii) the sometimes unknown distribution of the channel noise. Therefore, it makes sense to consider the ML alternative to learning from data automatically. The application of ML to FEC decoding is generally restricted to shortcodes due to the exponential training complexity. For instance, a message with \(k\) bits gives a total of \(2^{k}\) possible codewords. Fortunately, the ACM codes of DVB-S2X are generally limited, favoring such a scenario. Two ML technique options exist, artificial neural networks (ANNs) and recurrent neural networks (RNNs), as candidates to include onboard processing. The first has a fixed input layer size, which can be inefficient when applying ACM techniques to maintain avail  
Figure 3: Autoencoder for interference detection. ability in the system. The second can process a sequence of arbitrary length by recursively applying a transition function to the internal hidden state vector of the input sequence. The RNN properties suggest that it can correct errors in the time series data flow by referring to the contexts obtained by supervised training. Hence, RNNs have been shown to perform well for FEC decoding on receivers that may not previously know the channel coding types, such as cognitive radio. In this sense, a technique based on RNNs is preferable for error correction based on FEC for a regenerative satellite. An RNN can process a sequence of arbitrary length by recursively applying a transition function to the internal hidden state vector of the input sequence, in addition to the fact that RNNs have been shown to perform well for FEC coding on receivers that may not know the channel coding types previously, such as cognitive radio [24]. In this sense, a technique based on RNNs is chosen for error correction in a regenerative satellite. The RNN is a direct adaptation of the standard feedforward neural network used to learn the features of time series data. Figure 4 shows the structure of the RNN for the FEC of a regenerative satellite.