Abstract
Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.
Highlights
V ERY high throughput satellite (VHTS) systems have a key role in supporting future 5G and broadcast terrestrial networks [1], [2]
Liu et al [16] suggest a novel dynamic channel allocation algorithm based on deep reinforcement learning (DRL-DCA) in multibeam satellite systems, where the results showed that this algorithm can achieve a lower blocking probability, compared to traditional algorithms; the joint channel and the power allocation algorithm is not taken into consideration
Other machine learning (ML) algorithms are capable of providing solutions for time-variant data; a convolutional neural networks (CNNs) architecture has been chosen because the distribution of traffic demand in the service area can be represented by a spatial dependence and CNN networks have demonstrated good performance in exploiting the features of spatial distributions [33], [34]
Summary
V ERY high throughput satellite (VHTS) systems have a key role in supporting future 5G and broadcast terrestrial networks [1], [2]. While it may seem feasible to achieve a solution to this problem through optimization techniques, on a larger scale, the number of resources to be managed, the constraints coming from the system and the infinite number of traffic demand situations may lead to a problem that cannot be solved by conventional techniques [12] To solve this problem, Liu et al [13] suggested an assignment game-based dynamic power allocation (AG-DPA) to achieve suboptimal low complexity in multibeam satellite systems. The management of the payload resources is performed in an autonomous way; the main advantage of this methodology is that the management is performed with a low computational cost, since the neural network training is performed offline This methodology has several challenges; one of them is the exponential dependence of number of classes on the number of beams in addition to possible variations of power, bandwidth, and/or beamwidth, resulting in unsolvable problems due to flexibility increase. In the Appendix, the CNN design for the considered DRM problem is outlined
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