# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications
### Use Cases
#### 3.1.3 Link Adaption/ACM Optimization  
ACM techniques are among the most successful fade mitigation techniques for wireless links. The receiver estimates the instantaneous signal-to-noise ratio (SNR), and signals this value back to the transmitter, requiring a dead time to react to any changes. The channel conditions in SatCom links, such as rain, delay, and scintillation, make it difficult obtain the estimation signal required for feedback to choose the most suitable MODCOD at all times. In addition, another problem is added, the number of channel models currently considered for NGSO shows no consensus to determine the unique channel model [25]. In conventional approaches, ACM is easy for a single link without interference, where a pilot signal can be used to measure the channel and report back to the receiver. However, the innate delay of satellite links makes the problem substantially challenging. The ML algorithms are expected to improve the channel state information (CSI) prediction in the satellite network to help achieve ACM, hence optimizing system capacity.  
The work presented in [26] focuses on applying ML algorithms to CSI prediction in the satellite network, and using the improved prediction results to optimize system capacity. By testing different ML algorithms, the improvement in system performance and  
Figure 4: Recurrent neural network for forward error correction in regenerative payloads.  
the feasibility of deploying an ML-based prediction framework are demonstrated. The ML-based CSI prediction model provides an average capacity increase of up to 10.9% with acceptable overhead. Faced with the complexity of channel estimation techniques, non-coherent schemes do not need CSI to arise. In SatCom, these schemes are challenging for many use cases; for instance, they require abundant computational resources, as presented in [27] for SatCom. Onboard processing ML techniques can reduce the ACM capability required by non-coherent receivers.  
Machine learning techniques are proposed to increase the system's availability when link adaptation is required. Implementation based on deep learning, such as a long short-term memory network (LSTM), allows us to predict the SINR through time series data. This prediction strategy will enable us to develop a more efficient ACM mechanism.  
An LSTM layer is an RNN that supports time series and sequential data. To predict the values of future time steps of a sequence, we can train a sequence-by-sequence regression LSTM network. The responses are the training sequences with the value shifted by a one-time step, i.e., at each time step of the input sequence, the LSTM network learns to predict the value of the next step, \(t+1\).  
Figure 5 shows the proposed architecture for SNIR prediction. The SINR time series vector for each \(n^{th}\) channel, \(\gamma_{n}\), is preprocessed using feature scaling, also known as normalization, which is expressed as \(\gamma_{n}\). The LSTM network comprises at least one LSTM layer and a complete connection layer. The input of the LSTM network is \(\gamma_{n}^{{}^{\prime}}\), with which the SNIR prediction for time step \(t+1\), \(\tilde{\gamma}_{n}\), is performed. With this prediction, the predicted value of the spectral efficiency can be calculated, and the MODCOD can be adapted.