Deep-Learning-Based ModCod Predictor for Satellite Channels

Abstract

One of the significant challenges for satellite communications is to serve the ever-increasing demand for the use of finite resources. One option is to increase channel utilization, i.e., to transmit as much data as possible in a given frequency range. Since the channel is highly variable, primarily due to the ionosphere and troposphere, this goal can only be achieved by adaptively varying modulation and coding schemes. Most procedures and algorithms estimate the channel characteristics and descriptive quantities (e.g., signal-to-noise ratio). Ultimately, these procedures solve a regression problem. The resulting quantity is used as the basis for a decision process. Since valuation can also be subject to error, the decision mechanisms based on it must compensate and mitigate this error. The main element of the current research is to combine these two steps and solve them together using deep neural networks. The theoretical advantages of the method include that a better result can be achieved by having a joint estimation and decision process with a standard algorithm and cost function. The theoretical approach was tested with an actual protocol -- Digital Video Broadcasting - Satellite - Second Generation -- where we observed a significant improvement in channel utilization on previously recorded Alphasat satellite data.

Keywords

• satellite links
• time series prediction
• imbalanced classification
• supervised machine learning
• fading
• acm
• ai
• dl
• dnn
• dvb-s2
• alphasat
• satellite channel
• q band
• lstm
• dnn
• satellite-earth communication
• wireless signal propagation