With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

Beam-hopping (BH) technology, integral to multi-beam satellite systems, adapts beam activation to the variable communication demands of terrestrial users. The optimization of power allocation and beam illumination scheduling constitutes the core design challenge in BH systems, especially under the constraint on a limited number of simultaneously active beams due to restricted radio frequency chain availability. This paper proposes a two-stage BH design solution, which minimizes energy consumption in BH satellite communications while accommodating the heterogeneous demands of users. The first stage addresses the coupling variables of power and beam status by recasting the allocation and scheduling problem through a statistical lens, thus breaking down the intricate relationship between variables. To manage the resulting non-convex challenge, we propose an iterative method that capitalizes on the optimality conditions inherent to this problem. This method is designed to procure a statistically-informed solution that aligns with our reformulated interpretation. Subsequently, the second stage maps this solution into a concrete beam illumination schedule, employing binary quadratic programming techniques. A penalty-based iterative method is applied, ensuring convergence to a locally optimal solution. Through numerical simulations, the proposed framework has been validated for its efficacy in improving energy efficiency and accurately matching demands.

The low earth orbit (LEO) satellite system is one of the promising solutions to provide broadband services to a wide-coverage area for future integrated LEO-6G networks, where users' demands vary with time and geographical locations. Conventional satellites with fixed beam pattern and footprint planning may not be capable of meeting such dynamic requests and irregular traffic distributions. As the development of flexible satellite payload with beamforming capabilities, spot beams with flexible size and shape are considered potential solutions to this issue. As an early investigation, in this paper, we consider the scenarios where satellite payloads are equipped with multiple beam patterns and study the optimal beam pattern selection. We exploit the potential synergies of joint resource optimization between adaptive beam patterns and non-orthogonal multiple access (NOMA) in a LEO satellite system, where NOMA is employed to reduce intra-beam interference and flexible beam pattern is adopted to mitigate inter-satellite interference. The formulated problem is to minimize the capacity-demand gap of terminals, which falls into mixed-integer nonconvex pro-gramming (MINCP). To tackle the discrete variables and non-convexity, we design a joint approach to allocate power and select beam patterns. Numerical results show that the proposed scheme achieves capacity-demand gap reduction of 37.8% over conventional orthogonal multiple access (OMA) and 42.5% over the fixed-beam-pattern scheme.

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.

In this paper, we apply non-orthogonal multiple access (NOMA) in satellite systems to assist data transmission for services with latency constraints. We investigate a problem to minimize the transmission time by jointly optimizing power allocation and terminal-timeslot assignment for accomplishing a transmission task in NOMA-enabled satellite systems. The problem appears non-linear/non-convex with integer variables and can be equivalently reformulated in the format of mixed-integer convex programming (MICP). Conventional iterative methods may apply but at the expenses of high computational complexity in approaching the optimum or near-optimum. We propose a combined learning and optimization scheme to tackle the problem, where the primal MICP is decomposed into two learning-suited classification tasks and a power allocation problem. In the proposed scheme, the first learning task is to predict the integer variables while the second task is to guarantee the feasibility of the solutions. Numerical results show that the proposed algorithm outperforms benchmarks in terms of average computational time, transmission time performance, and feasibility guarantee.

In this paper, we investigate potential synergies of non-orthogonal multiple access (NOMA) and beam hopping (BH) for multi-beam satellite systems. The coexistence of BH and NOMA provides time-power-domain flexibilities in mitigating a practical mismatch effect between offered capacity and requested traffic per beam. We formulate the joint BH scheduling and NOMA-based power allocation problem as mixed-integer non-convex programming. We reveal the exponential-conic structure for the original problem, and reformulate the problem to the format of mixed-integer conic programming (MICP), where the optimum can be obtained by exponential-complexity algorithms. A greedy scheme is proposed to solve the problem on a timeslot-by-timeslot basis with polynomial-time complexity. Numerical results show the effectiveness of the proposed efficient suboptimal algorithm in reducing the matching error by 62.57% in average over the OMA scheme and achieving a good trade-off between computational complexity and performance compared to the optimal solution.

In a multi-beam satellite communication system, traffic requests are typically asymmetric across beams and highly heterogeneous among terminals. In practical operations, it is important to achieve a good match between the offered and requested traffic, i.e., to improve the performance of Offered Capacity to requested Traffic Ratio (OCTR). Due to satellites’ payload constraints and limited flexibilities, it is a challenging task for resource optimization. In this paper, we tackle this issue by formulating a max-min resource allocation problem, taking fairness into account such that the lowest OCTR can be maximized. To exploit the potential synergies, we introduce Non-Orthogonal Multiple Access (NOMA) to enable aggressive frequency reuse and mitigate intra-beam interference. Although NOMA has proven its capabilities in improving throughput and fairness in 5G terrestrial networks, for multi-beam satellite systems it is unclear if NOMA can help to enhance the OCTR performance, and hence is worth quantifying how much gain it can bring. To solve the problem, we design a suboptimal algorithm to firstly decompose the original problem into multiple convex subproblems by fixing power allocation for each beam, and secondly adjust beam power to improve the minimum OCTR in iterations. Numerical results show the convergence of the proposed algorithm and the superiority of the proposed NOMA scheme in max-min OCTR.

In order to obtain the worthwhile gain in coverage and the capacity required by future mobile services, heterogeneous networks (HetNets) are being considered as one of the most promising solutions. However, the ultra-dense deployment of small base stations (ud-SBSs) would significantly increase energy consumption (EC), which becomes particularly severe when meeting explosive growth of multimedia services. Proper user association (UA) and power allocation (PA) are both crucial to achieve desirable energy-saving performance in HetNets. In view of these, this paper investigates the joint UA and PA optimization problem for multimedia services in cache-enabled HetNets. The aim is to minimize the total power consumption under certain quality-of-service (QoS) requirement and maximum power limit. A non-convex mixed integer programming optimization problem is formulated. To solve the problem, a heuristic algorithm based on matching game is proposed. Numerical results demonstrate that the proposed algorithm yields a performance improvement in terms of the power consumption.

Dynamic Adaptive Streaming over HTTP (DASH), as one of the main video transmission technologies, has been widely concerned. However, due to the unstability of wireless environment and the restrictions of mobile networks, it is challenging to deliver DASH-based videos in LTE systems and enhance the performance of Quality of Experience (QoE). In this paper, a problem is formulated to maximize the sum of weighted rates of all DASH users constrained by resource blocks (RB) and transmit power. To solve this problem, we propose ulti-Cell DASH-based Video Transmission (MDVT) scheme, containing two phases: RBs allocation with fixed transmit power and power allocation with fixed RBs assignment. Simulation results reveal that the performance of MDVT outperforms the state-of-art schemes.