A Multi-Beam Satellite Cooperative Transmission Scheme Based on Resources Optimization and Packets Segmentation

Abstract

Multi-beam satellite communication systems are promising architectures in the future. A packet is transmitted by multi-satellite and multi-beam cooperatively, which can provide efficient spectrum utilization, improve system throughput, and guarantee Quality of Services (QoS). In multi-beam satellite communication systems, multi-layer and multi-dimensional radio resources change dynamically, which leads to the discontinuity of optimal resources and the lack of mapping balance between packets and radio resources. To deal with these problems, we propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and an adaptive packet segmentation scheme based on the irregular gradient algorithm. The cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm simulates cross-layer and cross-dimension optimization for the high-dynamic and multi-dimensional radio resources by considering the channel state information (CSI) and QoS in the multi-beam satellite communication system. The optimal resources are taken as the weight of irregular gradient algorithm to segment packets and map packets to radio resources, which can realize the mapping balance between packets and radio resources and ensure the efficiency and reliability of communication. The simulations show that the new transmission scheme improves the normalized system throughput and user satisfaction index by 18.7% and 6.2%, respectively.

Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks

“…From (25), we can observe that the maximum data storage of each user satellite increases with the maximum data sampling rate. As defined in the optimization objective (MTASU), a larger sampling rate brings a larger system utility, which indicates that increasing the system utility requires more storage capacity to ensure the system stability.…”

“…In the scheme, a single task is split into multiple sub-tasks and rationally arranged in multiple transmission time windows. Some existing efforts have studied resource optimization strategies in data transmission [23][24][25]. To schedule stochastic arrived data, wang et al proposed a queue stability model and a dynamic contact capacity optimization scheme [23].…”

“…An et al [24] considered different strategies, to obtain optimal power and adaptive transmission rate in a relay satellite network. In [25], the authors designed a resource optimization model to realize the continuity of radio resources for mapping with packets. In addition, several studies explore reinforcement learning methods to address the resource allocation problem.…”

State-of-the-Art in Satellite Communication Networks

“…Deng et al [6] studied a cross-layer and cross-dimension radio resource optimization model based on a firefly algorithm that takes into account the channel state information (CSI) and QoS of the satellite communications in a multi-satellite and multi-beam scenario. Their study tries to improve the resource management in the overall system and then obtain high spectrum efficiency throughout a packet segmentation scheme able to map different packets to radio resources.…”

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning

“…Lin et al [8] adopted the alternating optimization scheme and the Taylor expansion penalty function to optimize the beamforming weight vector and phase shift, aiming to minimize the total transmission power of the satellite and the base station, while meeting the user rate requirements. Deng et al [9] proposed an adaptive packet splitting scheme based on a discrete firefly algorithm for cross-layer and cross-dimension radio resources optimization and an irregular gradient algorithm to ensure communication efficiency and reliability. Zhang et al [10] proposed an uplink cooperative user scheduling and power allocation method based on game theory in an uplink multi-beam satellite Internet of Things (S-IoT).…”