Supporting Adaptive Coding and Modulation Techniques for Satellite Radio Channel

Abstract

For satellite-Earth communications, higher frequency bands, especially with millimeter wavelength frequencies are very sensitive to precipitation and other atmospheric changes. The time variation of these effects is extremely variable, eventually increasing the current attenuation of the channel [1]. Taken together, these phenomena may significantly reduce the momentary capacity of the channel. The primary goal is to transfer as much data as possible correctly on a given link, which means that the best possible settings for the protocol used should be used at each time step. To change the parameters, some information about the expected conditions must be available (this change process is called Adaptive Coding and Modulation or ACM). In terms of implementation, there are at most three ways to categorise the possible solutions [2]. One of the classical solutions is to estimate a channel characteristic, typically SNR, from the measured signal levels, on the basis of which the change is made. Such methods, with sufficiently rapid sampling, may be sufficient for channel variations. The former can be seen as a further development of procedures that already estimate these parameters for the future. Obviously, estimation is generally a more difficult operation to perform, but it also allows the system to react in advance to future changes [3]–[6]. A more complex and not yet fully researched area is when, instead of all these procedures, we predict the corresponding ACM settings as a kind of state estimation (where state represents a pair of encoding and modulation settings) [2]. Solutions of this kind are based on some kind of artificial intelligence or machine learning. Thus, in order to implement them, a large amount of data is needed to perform an acceptable training process. Compared to the first two solutions, it has the advantage of combining two logical steps: estimation of the quantity and prediction based on the estimated quantity. Summing up the advantages and disadvantages, if the resulting algorithm is given enough resources to run in real time, it could theoretically be faster and more accurate than previous solutions. The current research is built around two main pillars: on the one hand, it takes stock of existing methods for such problems ( [2], [7]), and; on the other hand, we investigated how to implement state estimation for real interconnections. This was based on actual measurements from the Alphasat satellite. The ModCod settings for DVB-2 transmission are estimated from the beacon signal transmitted by the spacecraft. For the implementation, we used a deep neural network with memory, the results of which can be used as a basis for future tests to solve similar problems.