Beam Hopping Research Articles

Multi-Satellite Beam Hopping Based on Load Balancing and Interference Avoidance for NGSO Satellite Communication Systems

Due to the non-uniform distribution of the ground traffic demand and the high mobility of non-geostationary orbit (NGSO) satellites, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for NGSO communication systems. In order to achieve efficient spectrum utilization, the combination of full frequency multiplexing and beam hopping is a major trend in future satellite communication systems. However, conventional beam hopping methods are mainly based on geostationary satellites, which do not take into account the interference between satellites. This paper proposes a multi-satellite beam hopping algorithm based on load balancing and interference avoidance, which takes advantage of the multiple coverage features in the NGSO constellation and avoids intra-satellite interference and inter-satellite interference by designing beam-hopping patterns with spatial isolation characteristics. In particular, we decompose the multi-satellite beam hopping problem into three sub-problems, which are the multi-satellite load balancing problem, the single-satellite beam hopping pattern design problem, and the multi-satellite interference avoidance problem. Simulation results demonstrate that the proposed method reduces the load gap among satellites by about 72.5% and the average traffic satisfaction rate can reach 81.4%. Besides, our method has the lowest unmet capacity compared with other benchmarks, achieving better offered-requested data match.

NGSO Satellites Beam Hopping Strategy Based on Load Balancing and Interference Avoidance for Coexistence With GSO Systems

Non-geostationary orbit (NGSO) satellite communication system plays an important role in achieving seamless global coverage and addressing the digital divide. The uneven distribution of terrestrial users and the high mobility of NGSO satellites put forward higher requirements for resource management. In this letter, we propose a multi-satellite beam hopping scheme based on load balancing and interference avoidance, where the complicated multi-satellite joint beam hopping problem is decomposed into two sub-problems, namely the satellite-cell assignment problem and joint multi-satellite beam hopping pattern design problem. On the one hand, we rationally assign cells to different satellites for balancing the traffic load among satellites and reducing the interference to geostationary earth orbit (GSO) ground stations. On the other hand, an iterative multi-satellite beam hopping pattern design scheme based on minimizing co-channel interference criterion is adopted to achieve on-demand service. Simulation results indicate that the performance of load balancing and interference avoidance are improved compared to the maximum elevation angle access strategy. Besides, the proposed scheme outperforms other benchmarks in terms of the traffic satisfaction rate, and the optimality gap is only 0.1%.

Joint Design of Beam Hopping and Multiple Access Based on Cognitive Radio for Integrated Satellite-Terrestrial Network

Joint Design of Beam Hopping and Multiple Access Based on Cognitive Radio for Integrated Satellite-Terrestrial Network

Sequential dynamic resource allocation in multi-beam satellite systems: A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources. One of the primary challenges accompanying with Multi-Beam Satellites (MBS) is an efficient Dynamic Resource Allocation (DRA) strategy. This paper presents a learning-based Hybrid-Action Deep Q-Network (HADQN) algorithm to address the sequential decision-making optimization problem in DRA. By using a parameterized hybrid action space, HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly. To pursue multiple long-term QoS requirements, HADQN adopts a multi-objective optimization method to decrease system transmission delay, loss ratio of data packets and power consumption load simultaneously. Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

Joint Optimization of Beam-Hopping Design and NOMA-Assisted Transmission for Flexible Satellite Systems

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks.

Dynamic Beam Hopping for DVB-S2X GEO Satellite: A DRL-Powered GA Approach

Dynamic beam hopping (DBH) is a crucial technology for adapting to the flexibility of service configurations in multi-beam satellite (MBS) communications. To solve the problem that the traditional beam-hopping method is difficult to adapt to the dynamic satellite environment, Deep Reinforcement Learning (DRL) was proposed. DRL can use the previous information to make decisions at the current moment and thus has low time complexity. However, because the generalization ability of the existing DRL methods cannot fully meet dynamic changes in multi-beam satellite communications scenarios, it is difficult to obtain the optimal decision. To address this issue, this letter explores a new framework in which DRL-Powered genetic algorithm (GA) approach. The proposed method uses DRL to assist the decision of DBH. Comparing the proposed method with DRL and traditional GA, simulation results show that the proposed method has a better performance in throughput and fairness when adapting to changes in the satellite environment.

Dynamic Beam Pattern and Bandwidth Allocation Based on Multi-Agent Deep Reinforcement Learning for Beam Hopping Satellite Systems

Due to the non-uniform geographic distribution and time-varying characteristics of the ground traffic request, how to make full use of the limited beam resources to serve users flexibly and efficiently is a brand-new challenge for beam hopping satellite systems. The conventional greedy-based beam hopping methods do not consider the long-term reward, which is difficult to deal with the time-varying traffic demand. Meanwhile, the heuristic algorithms such as genetic algorithm have a slow convergence time, which can not achieve real-time scheduling. Furthermore, existing methods based on deep reinforcement learning (DRL) only make decisions on beam patterns, lack of the freedom of bandwidth. This paper proposes a dynamic beam pattern and bandwidth allocation scheme based on DRL, which flexibly uses three degrees of freedom of time, space and frequency. Considering that the joint allocation of bandwidth and beam pattern will lead to an explosion of action space, a cooperative multi-agents deep reinforcement learning (MADRL) framework is presented in this paper, where each agent is only responsible for the illumination allocation or bandwidth allocation of one beam. The agents can learn to collaborate by sharing the same reward to achieve the common goal, which refers to maximize the throughput and minimize the delay fairness between cells. Simulation results demonstrate that the offline trained MADRL model can achieve real-time beam pattern and bandwidth allocation to match the non-uniform and time-varying traffic request. Furthermore, when the traffic demand increases, our model has a good generalization ability.

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.

Joint Beam Hopping and Carrier Aggregation in High Throughput Multibeam Satellite Systems

Beam hopping (BH) and carrier aggregation (CA) are two promising technologies for the next generation satellite communication systems to achieve several orders of magnitude increase in system capacity and to significantly improve the spectral efficiency. While BH allows a great flexibility in adapting the offered capacity to the heterogeneous demand, CA further enhances the user quality-of-service (QoS) by allowing it to pool resources from multiple adjacent beams. In this paper, we consider a multi-beam high throughput satellite (HTS) system that employs BH in conjunction with CA to capitalize on the mutual interplay between both techniques. Particularly, an innovative joint BH-CA scheme is proposed and analyzed in this work to utilize their individual competencies. This includes designing an efficient joint time-space beam illumination pattern for BH and multi-user aggregation strategy for CA. Through this, user-carrier assignment, transponder filling-rates, beams hopping pattern, and illumination duration are all simultaneously optimized by formulating a joint optimization problem as a multi-objective mixed integer linear programming problem (MINLP). Simulation results are provided to corroborate our analysis, demonstrate the design tradeoffs, and point out the potentials of the proposed joint BH-CA concept. Advantages of our BH-CA scheme versus the conventional BH method without employing CA are investigated and presented under the same system circumstances.

Dynamic Beam Hopping Time Slots Allocation Based on Genetic Algorithm of Satellite Communication under Time-Varying Rain Attenuation

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.

Precoded Cluster Hopping for Multibeam GEO Satellite Communication Systems

Beam hopping (BH) and precoding are two trending technologies for high-throughput satellite (HTS) systems. While BH enables the flexible adaptation of the offered capacity to the heterogeneous demand, precoding aims at boosting the spectral efficiency. In this study, we consider an HTS system that employs BH in conjunction with precoding in an attempt to bring the benefits of both in one. In particular, we propose the concept of cluster hopping (CH), where a set of adjacent beams are simultaneously illuminated with the same frequency resource. On this line, we propose an efficient time–space illumination pattern design, where we determine the set of clusters that shall be illuminated simultaneously at each hopping event along with the dwelling time. The CH time–space illumination pattern design formulation is shown to be theoretically intractable due to the combinatorial nature of the problem and the impact of the actual illumination design on the resulting interference. For this, we make some design decisions on the beam–cluster design that open the door to a less complex still well-performing solution. Supporting results based on numerical simulations are provided which validate the effectiveness of the proposed CH concept and a time–space illumination pattern design with respect to benchmark schemes.

A Secure Structure for UAV-Aided IoT Networks: Space-Time Key

The Internet of Things (IoT) has been extensively investigated as an enabling technology for situation awareness and data collection. Since power-limited or battery-free IoT devices may have limited transmission coverage, unmanned aerial vehicles (UAVs) can be employed as mobile edge computing (MEC) servers to collect and process data packets from multiple IoT devices due to their high mobility and low operating cost. However, data transmission becomes vulnerable when eavesdroppers exist, as those IoT devices may have limited computing power for encryption. Moreover, it is noteworthy that the identity and location information of IoT devices themselves are also crucial to particular implementations (e.g., regional situation awareness), especially when massive IoT devices are involved using random access schemes. In this article, a secure structure is proposed as a promising strategy to support UAV-aided IoT networks, where the identity and location information of multiple IoT devices are utilized as the key to encrypt data packets collected from IoT networks. In the new structure, the trajectory planning of UAVs is developed to minimize the energy consumption among multiple clusters of a UAV, while the deployment optimization is used to maximize the secrecy capacity in each cluster, and the beam hopping is carried out to encrypt multi-access IoT devices within a coverage of a UAV. After introducing the structure, some key technologies are presented, and then future works for the secure UAV-aided IoT network are discussed.

Benefits analysis of beam hopping in satellite mobile system with unevenly distributed traffic

Satellite mobile system and space-air- ground integrated network have a prominent superiority in global coverage which plays a critical role in remote and non-land regions, as well as emergency communications. However, due to the gradual angle attenuations of the satellite antennas, it is difficult to achieve full frequency multiplex among different beams as terrestrial 5G network. Multi-color frequency reuse is widely adopted in both academic and industry. Beam hopping scheme has attracted the attention of researchers recently due to the allocation flexibility. In this paper, we focus on analyzing the performance benefits of beam hopping compared with multi-color frequency reuse scheme in non-uniform user and traffic distributions in satellite system. Aerial networks are also introduced to form a space-air- ground integrated network for coverage enhancement, and the capacity improvement is analyzed. Besides, additional improved techniques are provided to make comprehensive analysis and comparisons. Theoretical analysis and simulation results indicate that the beam hopping scheme has a prominent superiority in the system capacity compared with the traditional multicolor frequency reuse scheme in both satellite mobile system and future space-air-ground integrated network.

Joint precoding schemes for flexible resource allocation in high throughput satellite systems based on beam hopping

Beam hopping technology provides a foundation for the flexible allocation and efficient utilization of satellite resources, which is considered as a key technology for the next generation of high throughput satellite systems. To alleviate the contradiction between resource utilization and co-frequency interference in beam hopping technology, this paper firstly studies dynamic clustering to balance traffic between clusters and proposes cluster hopping pool optimization method to avoid inter-cluster interference. Then based on the optimization results, a novel joint beam hopping and precoding algorithm is provided to combine resource allocation and intra-cluster interference suppression, which can make efficient utilization of system resources and achieve reliable and near-optimal transmission capacity. The simulation results show that, compared with traditional methods, the proposed algorithms can dynamically adjust to balance demand traffic between clusters and meet the service requirements of each beam, also eliminate the co-channel interference to improve the performance of satellite network.

A Secure Architecture of Relay-Aided Space Information Networks

In recent years, the Internet of Things (IoT) has been extensively investigated as an enabling technology to support massive devices or sensors. With increasing demands of seamless connectivity of IoT, space information networks (SINs) are adopted to provide reliable communications for IoT devices at any time and anywhere. However, the conventional SIN suffers from different malicious attacks (e.g., jamming attack, eavesdropping attack) due to its highly exposed satellite-ground links with fixed spot beams. Those attacks may cause severe security problems to SINs. To overcome these drawbacks, in this article, a secure architecture is proposed as a promising strategy to improve the security of SINs, where relays are deployed together with hopped beams to relieve the jamming attack of the uplink and combat the eavesdropping attack of the downlink. In the proposed new structure, by utilizing spatial diversity, inter-satellite beam hopping is developed to build jamming-free uplink channels, while relay selection is carried out to form eavesdropping-free downlink channels. Meanwhile, relay deployment optimization is studied to maximize the system efficiency, and data aggregation is adopted to encrypt the data packets of IoT devices at relays. As a result, the security of SINs can be strongly improved in the existence of jamming and eavesdropping threats. Furthermore, future works for the secure SIN are also discussed.

Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

Optimization Method of Dynamic Beam Position for LEO Beam-Hopping Satellite Communication Systems

The beam-hopping (BH) technology applied to low earth orbit (LEO) satellite communication networks is a superior choice, but the long transmission delay partly caused by data packets waiting in the queue of satellite transponders will seriously affect the user experience. To shorten the packet queueing delay, in this paper, we propose an optimization method of dynamic beam position division for LEO BH satellite communication systems. Firstly, we analyze the packet queueing delay problem in BH satellites to find out the factors related to the queueing delay, and we find that the number of beam positions is negatively correlated with the queueing delay. Then, we turn the beam position division problem into a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -center problem to try to cover all users with the least number of beam positions. The beam positions among the footprint of LEO satellites are determined dynamically by the user distribution and the traffic distribution. Finally, the performance evaluation of the proposed optimization method is carried out in real-time and the simulation shows that the beam position division optimized system we proposed can shorten the queueing delay up to 40% compare to the benchmark system without sacrificing throughput.

Dynamic Beam Hopping Method Based on Multi-Objective Deep Reinforcement Learning for Next Generation Satellite Broadband Systems

When regarding the inherent uncertainty of differentiated services requirements as well as the non-uniform spatial distribution of capacity requests, it is essential to flexibility adjust resources of the satellite to satisfy the different conditions. How to match the system capacity demand with efficient utilization of beam is a brand-new challenge. The convention beam hopping methods ignores the intrinsic correlation between decisions, do not consider the long-term reward, and only achieve the optimal solution at the current time. Therefore, system complexity increases significantly as the increase of the demand for differentiated services or beam number. This paper investigates the optimal policy for beam hopping in DVB-S2X satellite with multiple purposes of assuring the fairness of each beam services, minimizing the delay of real-time services transmission, and maximizing the throughput of non-instant services transmission. Since wireless channel conditions, differentiated services arrival rates have stochastic properties, and the multi-beam satellite environment’s dynamics are unknown, the model-free multi-objective deep reinforcement learning approach is used to learn the optimal policy through interactions with the situation. To solve the problem with action dimensional disaster, a novel multi-action selection method based on a Double-Loop Learning (DLL) is proposed. Moreover, the multi-dimensional state is reformulated and obtained by the deep neural network. Under realistic conditions achieving evaluation results demonstrate that the proposed method can pursue multiple objectives simultaneously, and it can also allocate resource intelligently adapting to the user requirements and channel conditions.

A methodology to benchmark flexible payload architectures in a megaconstellation use case

SummaryThis paper proposes a methodology to benchmark satellite payload architectures and find the optimal trade‐offs between high flexibility and low complexity. High flexibility would enable the satellite to adapt to various distributions of user terminals on the ground and fulfill the data rate demand of these users. Besides, low complexity is required to keep satellite networks competitive in the context of emerging 5G networks. To estimate the flexibility of a payload, an indicator to characterize the non‐uniformity of user distributions is proposed. Each benchmarked payload may be characterized by a graph relating the throughput to this parameter further denoted . The payload provides the same throughput trends for different scenarios of user distributions with the same parameter. As a consequence, the average capacity of the system may be estimated by (a) calculating the probability distribution of over the orbit and (b) integrating the throughput based on this payload response. It thus results in a straightforward way for benchmarking payloads directly on an estimation of the averaged capacity, accounting for the user distribution over the earth. A simulation platform has been developed to characterize the payload throughput including the implementation of a resource allocation algorithm that accounts for constraints of various payloads. Using this definition and the developed tool, we benchmark a bent‐pipe architecture, a beam hopping architecture and a hybrid beam‐steering architecture for a LEO megaconstellation use case. The methodology showcases the interest for investigating different payload architectures depending on realistic traffic scenario analysis.

Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping

Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular and time-varying traffic requests in future multi-beam satellite systems. In BH optimization, adopting conventional iterative heuristics may have their own limitations in providing timely solutions, and directly using data-driven technique to approximate optimization variables may lead to constraint violation and degraded performance. In this paper, we explore a combined learning-and-optimization (LO 2) Features to be learned in BH design; 3) How to address the feasibility issue incurred by machine learning. We provide numerical results and analysis to show that the learning component in L&O significantly accelerates the procedure of identifying promising BH patterns, resulting in reduced computing time from seconds/minutes to milliseconds level. The identified learning feature enables high accuracy in predictions. In addition, the optimization component in L&O guarantees the solution’s feasibility and improves the overall performance with around 5% gap to the optimum.