Flexible Payload Research Articles

Double-Timescale Multi-Agent Deep Reinforcement Learning for Flexible Payload in VHTS Systems

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.

Airbus flexible payload perspective

The satellite industry is evolving at a rate not seen in its previous 60 year history. The industry is attracting investment from new players resulting in billions of euros being invested in new space mega constellations. This massive investment in the space ecosystem is driving innovation in flexible payload technologies across LEO, MEO and GEO, supporting commercial governmental and military satellite applications. Airbus Defence and Space has long been a leading player and first to market innovator in the domain of flexible payloads building fully digitally beamformed flexible payloads for GEO mobile applications for Inmarsat and Thuraya, The Eutelsat Quantum fully flexible software defined satellite, and more recently the Onesat product for Inmarsat, Intelsat, Optus and JSAT. Airbus experience in high performance fully flexible payloads spans both the commercial and governmental market places. Indeed Airbus provided huge contributions ranging from full build launch and operating services, to payload subsystem provision for all major European Military SATCOM systems. Airbus experience in flexible payloads is not limited to GEO orbit, Airbus has worked closely with Oneweb and produced and launched to date many hundreds of LEO satellites having highly flexible payloads, leveraging novel new space methodologies and automated final assembly (FAL) techniques. Despite the huge success and achievements to date, innovation at Airbus never stands still. With this in mind Airbus has devoted millions of Euros of research and development funding with backing from ESA, ARTES and national institutions to study and shape the next generation of satellite communications. We have studied LEO MEO and GEO orbits, the projected use cases of each orbit, the required technology roadmaps and means to interconnect these orbits resulting in a system of systems able to leverage the pros of a given orbit, without being constrained by its cons. Within this paper Airbus will discuss our perspective on next generation payload technology supporting all orbits and many end use cases. We will discuss some of the basic physics behind the roadmaps and the key technology challenges and innovations needed to overcome these challenges. The overall scope of innovation can broadly be split into three main categories Airbus believes that these core technologies are applicable in differing scopes across the future of LEO, MEO and GEO systems.

Joint carrier allocation and precoding optimization for interference-limited GEO satellite

The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management, applying precoding on flexible payloads is essential to obtain high spectral efficiency. However, these cannot be achieved using a conventional resource allocation algorithm that does not consider the user demand. In this paper, we propose a demand-aware algorithm based on multiobjective optimization to jointly design the carrier allocation and precoding for better spectral efficiency and demand matching with proper management of the satellite resources. The optimization problem is non-convex, and we solve it using convex relaxation and successive convex approximation. Then, we evaluate the performance of the proposed algorithm through numerical results. It is shown that the proposed method outperforms the benchmark schemes in terms of resource utilization and demand satisfaction.

Eutelsat Quantum a fully flexible software defined satellite successfully operating on orbit

The satellite industry is evolving at a rate not seen in its previous 60 year history. The industry is attracting investment from new players resulting in billions of euros being invested in new space mega constellations. Despite rumours to the contrary, this investment in LEO / MEO systems does not indicate the demise of traditional GEO satellites but is in fact stimulating competition & new technology developments across the whole satellite ecosystem. Traditional GEO satellite operators and manufacturers are now able to capitalize on this growth in the overall industry along with the traditional strengths of the GEO orbit namely reach, broadcasting efficiency and terminal simplicity bringing to market a new generation of GEO flexible software defined satellites. Airbus has always been a leader and innovator in the domain of flexible satellite payload products, and have been designing and building fully flexible digital beam formed active antennas in L band for GEO mobile missions for more than 15 years, using Array Fed Reflector antenna architectures. Based on this heritage and capability Airbus was selected by Eutelsat to design, manufacture, & qualify the Eutelsat Quantum Satellite. Airbus has worked hand in hand with Eutelsat to bring this innovative software defined satellite vision to market and is proud to confirm that the satellite (along with all its supporting operational software) is operating in orbit in full compliance to its requirements and predicted capabilities. The Eutelsat Quantum satellite is the first (to our knowledge) of the new generation of fully flexible Ku band satellites deploying wideband active antennas in transmit and receive frequencies along with flexible channelization and connectivity. This suite of flexible payload technologies provide the satellite with all key aspects of payload flexibility namely coverage, RF power and frequency plan / connectivity. Furthermore, the active antennas also support value added functions such as GEO location, uplink interference management and beam hopping. As such the Eutelsat Quantum Satellite is a trail blazer in being the first to market product in the domain of fully flexible Ku band satellites and is the first to have been launched and successfully tested in orbit. This paper will detail the overall design approach to the Eutelsat Quantum satellite, its game changing overall capabilities, its technical solution and industrial footprint.

High-throughput satellite flexibility design and modeling

The paper presents a high-throughput satellite flexibility modeling and analysis method. The described method gives the design basis for choosing flexible satellite payloads such as transparent digital processors (DTP) for high-throughput satellites. The algorithm in the paper takes different time demands within the high-throughput satellite beams and different area demands between beams as input analyzes the capacity matching rate under the onboard resource constraint in different application scenarios. It also gives the conclusion of whether the satellite chooses a flexible design or not. When the variance of inter-beam demand and design capacity in the coverage area exceeds a given limit, the flexible payload is selected to achieve a higher capacity matching ratio and lower satellite design and usage cost. When the variance of demand and beam design capacity is less than the given limit, the variance of inter-beam demand in the coverage area is smaller, and the satellite system without flexible design is selected considering the full satellite capacity and cost.

Collaborative Antiswing Hoisting Control for Dual Rotary Cranes With Motion Constraints

With large load capacity and flexible payload attitude adjustment capability, dual rotary cranes (DRCs) play crucial roles in infrastructure construction with heavy hoisting demands. However, as a kind of collaborative control systems, DRCs have complex collaborative constraints, and large-scale payloads cannot be simply regarded as mass points; moreover, due to the lack of control inputs, some state variables can only be indirectly controlled through complicated nonlinear coupling relationships, which make the controller design and corresponding analysis particularly difficult. Additionally, in practical applications of DRCs, many factors (such as boom motion overshoots, inaccurate gravity (torque) compensation, etc.) are prone to result in unexpected steady errors, large payload swing, and boom collisions, which may result in inaccurate assembly, and even lead to safety accidents. To this end, this article proposes an adaptive nonlinear proportional-integral-derivative-like collaborative control method for DRCs, which can realize accurate and efficient antiswing hoisting. To our knowledge, this is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> controller embedded with integral terms <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> any linearization during the controller design or stability analysis, which can effectively reduce steady errors, and keep boom motions within safety ranges by adaptive gravity (torque) compensation and elaborately designed constraint terms. Theoretically, the closed-loop stability and convergence are proven through strict mathematical analysis by using Lyapunov techniques and LaSalle’s invariance theorem. Finally, several groups of hardware experimental results are presented for effectiveness and robustness verification.

Non-orthogonal transmission under flexible illumination patterns for advanced satellite payloads

This works proposes a flexible illumination pattern with nonorthogonal transmissions, which reuse the same carrier frequency in different beams, to enhance the service quality for a region of interest within the satellite footprint. In particular, we assume an advanced satellite communication payload architecture, allowing for a flexible allocation of power and bandwidth across beams together with active antenna subsystems to relocate beams. To take a full advantage of the flexible payload capabilities, a non-orthogonal transmission scheme, known as Space-Time Rate Splitting, is utilized that only requires a minimum amount of channel state information to be known at the transmitter. Performance improvements, in terms of the minimum user throughput up to 50% with respect to single beam orthogonal transmission have been identified, as long as user terminals can apply a single-state successive interference cancellation. Lower gain values are observed when non-orthogonal transmission and conventional single user detection techniques are deployed as the receiver.

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.

Nonlinear disturbance observer-based direct joint control for manipulation of a flexible payload with output constraints

This paper discusses a nonlinear direct joint control scheme for manipulation of a flexible payload by a single-arm system with disturbances, parametric uncertainty and output constraints. The flexible payload can be considered as an Euler–Bernoulli beam. The equations of the coupled rigid-flexible motion are established on the basis of the ordinary differential equations (ODEs) and partial differential equation (PDE), which can avoid system spillover instability. A novel nonlinear disturbance observer-based control approach is proposed to enhance its disturbance attenuation ability. To handle the system parametric uncertainty, we apply adaptive direct joint control to approximate the unknown parameter, which does not require endpoint boundary control. Then a Barrier Lyapunov Function (BLF) is adopted to eliminate the impact of output restriction. The asymptotic stability of the closed-loop system is rigorously proven by semi-group theory and LaSalle's Invariance Principle extended to an infinite-dimensional system. Finally, the performance of the direct joint control is validated via numerical simulations.

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).

Filter Distortions in Ultra High-Throughput Satellites: Models, Parameters and Multicarrier Optimization

Ultra high-throughput satellite systems are expected to play an essential role in future beyond 5G and 6G networks. These systems must remain as flexible as possible to adapt to heterogeneous traffic demands, while also delivering the highest possible rate for dedicated services. Satellites flexible payloads are increasingly employing wideband output multiplexers. In this context, it is now more important than ever to evaluate frequency-dependent degradations on multicarrier signals. In particular, it is critical to characterize the distortions entailed by the output multiplexers filters. In this paper, models are presented and novel formulas are derived to determine the carrier-to-interference ratio resulting from these distortions. Derivations are oriented towards the applicability of either high-accuracy (e.g., for link budget) or low-complexity calculations (e.g., for real-time carrier allocation). The influence of key parameters such as the optimal decision instant, symbol rate and roll-off factor is thoroughly analyzed. Furthermore, formulas are evaluated in a practical scenario: the dynamic carrier allocation optimization. They are combined with efficient optimization algorithms to obtain the best performance based on user fairness. Relevant metrics such as accuracy, complexity and allocation gain are also investigated. In the end, the application of the proposed formulas and algorithms leads to a significant allocation gain that is increasing with the number of carriers. The feasibility of real-time dynamic carrier allocation to further increase the capacity of the next generation of satellite systems is emphasized.

Supervised machine learning for power and bandwidth management in very high throughput satellite systems

SummaryIn the near future, very high throughput satellite (VHTS) systems are expected to have a high increase in traffic demand. However, this increase will not be uniform over the service area and will be also dynamic. A solution to this problem is given by flexible payload architectures; however, they require that resource management is performed autonomously and with low latency. In this paper, we propose the use of supervised machine learning, in particular a classification algorithm using a neural network, to manage the resources available in flexible payload architectures. Use cases are presented to demonstrate the effectiveness of the proposed approach, and a discussion is made on all the challenges that are presented.

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.

A Tunable Quarter-Wavelength Coaxial Filter With Constant Absolute Bandwidth Using a Single Tuning Element

This letter presents a novel configuration high- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> coaxial tunable filter that employs a single rotational mechanism to tune the filter while using fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> /4 resonators. A prototype filter is designed for the proof of concept, which has a tuning range of 11.6% from 685 to 770 MHz, over which bandwidth (BW) variation is within 10.5 MHz ± 0.7 MHz. In addition, the proposed design methodology can be scaled to realize higher order filters. The proposed filter promises to be useful in a wide range of telecommunication applications, including flexible payload in aerospace applications.

Machine Learning for Satellite Communications Operations

This article introduces the application of machine learning (ML)-based procedures in real-world satellite communication operations. While the application of ML in image processing has led to unprecedented advantages in new services and products, the application of ML in wireless systems is still in its infancy. In particular, this article focuses on the introduction of ML-based mechanisms in satellite network operation centers such as interference detection, flexible payload configuration, and congestion prediction. Three different use cases are described, and the proposed ML models are introduced. All the models have been constructed using real data and considering current operations. As reported in the numerical results, the proposed ML-based techniques show good numerical performance: the interference detector presents a false detection probability decrease of 44 percent, the flexible payload optimizer reduces the unmet capacity by 32 percent, and the traffic predictor reduces the prediction error by 10 percent compared to other approaches. In light of these results, the proposed techniques are useful in the process of automating satellite communication systems.

Generalized Modulation Scheme of GBOC for Global Navigation Satellite System

The Generalized Binary Offset Carrier (GBOC) modulations can offer additional degrees of freedom for shaping the spectrum of navigation signal and provide superior performance in code tracking, multipath and compatibility than traditional Binary Phase Shift Keying (BPSK) and Binary Offset Carrier (BOC) modulations. This paper proposes a generalized modulation scheme for GBOC modulation design. Firstly, the generalized waveform of GBOC modulations is proposed. Then, the proposed construction method of GBOC modulations is presented, and the consistency with traditional GBOC modulations is investigated. Finally, the generalized autocorrelation functions of GBOC modulations are derived based on the inverse Fourier transform method. The results show the new modulation scheme has good consistency with traditional method and can provide the opportunity for flexible satellite payload design.

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.

Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals

Information dissemination in mobile networks turns out to be a problem when the network is sparse. Mobile networks begin to establish a separate cluster attributable to the limited communication range of terminals. The multi-beam satellite communication systems can play a significant role in providing direct-to-user satellite mobile services and connecting the separated clusters. This paper focuses on how to efficiently schedule limited satellite-based radio resources to enhance transmission efficiency and meet the requested traffic with low complexity. Taking the inter-beam interference and resource utilization variance into consideration, we build a game-theoretic based model for bandwidth allocation in the forward link. As the size of satellite beams increases, the size of the action space for deep reinforcement learning based on a single agent becomes large, resulting in high time complexity. Thus, we extend the single-agent deep reinforcement learning to the multi-agent context and then propose a cooperative multi-agent deep reinforcement learning method to achieve the optimal bandwidth allocation strategy. Each beam works as a player who is willing to satisfy the request traffic with flexible payloads. We built a multi-beam satellite platform using real historical data. The experimental results show that this approach is capable of enhancing transmission efficiency and can be flexible to achieve the desired goal with low complexity.

Point-to-point motion control for flexible crane systems working in the deep sea

At present, marine resources, especially the deep-sea resources, are becoming more and more important in resource exploitation globally, and hence, widely used deep-sea cranes are playing essential roles. For such systems, the bridge frames and trolleys are set up above the water, while payloads are transported under the water. In this underwater situation, there exist hydrodynamic forces such as complicated disturbances to the crane systems, making the payload vibration and rope flexibility more obvious. For the sake of improving working efficiency, considering the constraints of all the state variables, an anti-vibration trajectory is designed for the trolley motion, which can not only ensure trolley positioning but also suppress the flexible payload’s vibrations. Then, the state variables are constrained within preset safety ranges. Finally, numerical simulation results prove the satisfactory performance of the designed method.

High-Performance Self-Synchronous Blind Audio Watermarking in a Unified FFT Framework

This paper presents a blind audio watermarking method that uses two different schemes to hide binary bits and auxiliary information within separate ranges of a fast Fourier transform (FFT) sequence. An adaptive vector norm modulation (AVNM) scheme is introduced to achieve a satisfactory balance of imperceptibility, robustness, and payload capacity. An improved spread spectrum (ISS) scheme is developed to produce a striking correlation peak, which facilitates the detection of synchronization codes in the FFT domain. The combination of robust audio segment extraction and recursive FFT makes it possible to execute these two FFT-based schemes in tandem on a sample-by-sample basis. The experiment results confirm that watermark embedding causes merely a negligible degradation in perceptual quality. A detectability test proved the effectiveness of the ISS scheme in self-synchronization as well as hiding auxiliary data. Three versions of AVNM with capacities ranging from 344.53 to 1033.59 bits per second were demonstrated. Compared with six recently developed schemes, AVNM exhibited advantages in terms of negligible quality distortion, flexible payload capacity, and excellent robustness against a variety of common signal processing attacks.