Optimization in VHTS Satellite System Design with Irregular Beam Coverage for Non-Uniform Traffic Distribution

Very High Throughput Satellites (VHTS) have a pivotal role in complementing terrestrial networks to increase traffic demand. VHTS systems currently assume a uniform distribution of traffic in the service area, but in a real system, traffic demands are not uniform and are dynamic. A possible solution is to use flexible payloads, but the cost of the design increases considerably. On the other hand, a fixed payload that uses irregular beam coverage depending on traffic demand allows maintaining the cost of a fixed payload while minimizing the error between offered and required capacity. This paper presents a proposal for optimizing irregular beams coverage and beam pattern, minimizing the costs per Gbps in orbit, the Normalized Coverage Error, and Offered Capacity Error per beam. We present the analysis and performance for the case study and compare it with a previous algorithm for a uniform coverage area

Enhanced Communications on Satellite-Based IoT Systems to Support Maritime Transportation Services

Maritime transport has become important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport to decarbonize its impact on the environment. In order to improve the economy and people’s lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from Global Positioning System (GPS), and security through the Automatic Identification System (AIS). This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo and maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented

Forward Link Optimization for the Design of VHTS Satellite Networks

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse, in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity, and the figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs, including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams, and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas

Multi-Criteria Ground Segment Dimensioning for Non-Geostationary Satellite Constellations

Non-Geostationary Orbit (NGSO) satellite constellations are becoming increasingly popular as an alternative to terrestrial networks to deliver ubiquitous broadband services. With satellites travelling at high speeds in low altitudes, a more complex ground segment composed of multiple ground stations is required. Determining the appropriate number and geographical location of such ground stations is a challenging problem. In this paper, we propose a ground segment dimensioning technique that takes into account multiple factors such as rain attenuation, elevation angle, visibility, and geographical constraints as well as user traffic demands. In particular, we propose a methodology to merge all constraints into a single map-grid, which is later used to determine both the number and the location of the ground stations. We present a detailed analysis for a particular constellation combining multiple criteria whose results can serve as benchmarks for future optimization algorithms

Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems

Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to the non-uniform distribution of users and changes in traffic demand during the day. This problem is addressed by using flexible payload architectures, which allow the allocation of payload resources flexibly to meet the traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to VHTS systems, so in this paper we discuss the use of one reinforcement learning (RL) algorithm and two deep reinforcement learning (DRL) algorithms to manage the resources available in flexible payload architectures for DRM. These algorithms are Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) which are compared based on their performance, complexity and added latency. On the other hand, this work demonstrates the superiority a cooperative multiagent (CMA) decentralized distribution has over a single agent (SA)

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

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

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach

Optimization of cost and capacity of broadband satellite system and resources management using Machine Learning techniques

Very High Throughput Satellite (VHTS) systems have an important role to play as a complement to future 5G terrestrial networks to meet the growing traffic demand. In the near future, VHTS systems are expected to reach a transmission capacity of 1 Tbps based on frequency reuse/polarization and multibeam coverage schemes. However, the traffic demand in the service area is not uniform and is also changing throughout the day. This means that with a traditional payload, some beams have insufficient resources and others have wasted resources. One solution to this problem is flexible payloads that allow satellite resources to be modified according to traffic demand. According to operators, the main challenges in Satellite Communications (SatComs) is to achieve new generation VHTS systems capable of satisfying traffic demand and to know how to manage resources in an optimal and autonomous way, thus emerging the problem of Dynamic Resource Management (DRM). With this in mind, this thesis studies the optimization for the design of new generation VHTS systems. The study is divided into two parts, satellites with fixed payload and satellites with flexible payload. For the first part, an optimization method is developed that minimizes the cost per Gbps in orbit and maximizes the capacity per beam, as a function of the number of beams, user G/T and annual availability. As an intermediate step between flexibility and a fixed system, the possibility of having a payload that provides coverage with irregularly sized beams depending on traffic demand is studied. While, for flexible systems, new optimization techniques belonging to Machine Learning are studied to manage resources dynamically and autonomously in the system. The results of this thesis provide new contributions for the design of new generations of VHTS broadband satellites and open a possibility for new research line

Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems

Future generation SatCom multibeam architectures will extensively exploit full-frequency reuse schemes together with interference management techniques, such as precoding, to dramatically increase spectral efficiency performance. Precoding is very sensitive to user scheduling, suggesting a joint precoding and user scheduling design to achieve optimal performance. However, the joint design requires solving a highly complex optimization problem which is unreasonable for practical systems. Even for suboptimal disjoint scheduling designs, the complexity is still significant. To achieve a good compromise between performance and complexity, we investigate the applicability of Machine Learning (ML) for the aforementioned problem. We propose three clustering algorithms based on Unsupervised Learning (UL) that facilitate the user scheduling decisions while maximizing the system performance in terms of throughput. Numerical simulations compare the three proposed algorithms (K-means, Hierarchical clustering, and Self-Organization) with the conventional geographic scheduling and identify the main trade-offs