Optimal Selection Mechanism of Short Message Terminal for "Beidou-3"

Xia, Kaiwen; Feng, Jing; Yan, Chao

doi:10.1109/icsip49896.2020.9339413

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…To verify the effectiveness and convergence of the DRL-SAR, we used the throughput and load balance index of the SMSCS, the path transmission loss of the short-message terminal, and the terminal service satisfaction index as the algorithm evaluation criteria. At the same time, the DRL-SAR was compared with the DQN, GA, and TS-IHA [24]. An Intel(R) Xeon(R) W-2104 CPU @3.20 Hz, 16 GB RAM computer was used for the simulation experiments conducted in this work.…”

Section: Simulation and Performance Analysismentioning

confidence: 99%

“…There has been little research on short-message satellite resource allocation. Yang et al [23] proposed a task-oriented satellite network resource allocation algorithm (SAGA) based on the GA. Xia et al [24] combined this with the BDS3-SMC to form a short-message transmission mechanism and solved the problem of short-message satellite resource allocation by improving the Hungarian algorithm. However, for scenarios with a large number of terminals, the applicability of the algorithm is poor.…”

Section: Introductionmentioning

confidence: 99%

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BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

Xia

Feng

Yan

et al. 2021

Entropy

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The comprehensively completed BDS-3 short-message communication system, known as the short-message satellite communication system (SMSCS), will be widely used in traditional blind communication areas in the future. However, short-message processing resources for short-message satellites are relatively scarce. To improve the resource utilization of satellite systems and ensure the service quality of the short-message terminal is adequate, it is necessary to allocate and schedule short-message satellite processing resources in a multi-satellite coverage area. In order to solve the above problems, a short-message satellite resource allocation algorithm based on deep reinforcement learning (DRL-SRA) is proposed. First of all, using the characteristics of the SMSCS, a multi-objective joint optimization satellite resource allocation model is established to reduce short-message terminal path transmission loss, and achieve satellite load balancing and an adequate quality of service. Then, the number of input data dimensions is reduced using the region division strategy and a feature extraction network. The continuous spatial state is parameterized with a deep reinforcement learning algorithm based on the deep deterministic policy gradient (DDPG) framework. The simulation results show that the proposed algorithm can reduce the transmission loss of the short-message terminal path, improve the quality of service, and increase the resource utilization efficiency of the short-message satellite system while ensuring an appropriate satellite load balance.

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Section: Simulation and Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

Xia

Feng

Yan

et al. 2021

Entropy

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“…The aim of China's continuous construction of BDS is to ensure that various industries obtain navigation, positioning, and timing services with specific characteristics. These characteristics include strong resistance to interference from electromagnetic and environmental factors, prevention of deception by other signals on satellite navigation signals, and the reception of continuous and stable satellite navigation signals by users [1].…”

Section: Introductionmentioning

confidence: 99%

Fusion Navigation of BeiDou-3 and Remote Sensing Information in the Case of Satellite Signal Loss

Chen,

Zhao,

et al. 2024

Advances in Transdisciplinary Engineering

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This article presents a method to address the issue of decreased navigation accuracy caused by partial or complete signal loss of the BeiDou-3 satellite navigation system in complex environments. For cases where the number of satellites is insufficient but not completely depleted, the article introduces a method that combines odometry scene matching with BeiDou navigation. The fusion navigation framework of BeiDou-3 and remote sensing information is analyzed and studied, and a corresponding mathematical model is designed. For scenarios where satellite navigation signals are completely lost, the article proposes a solution based on the BP neural network. It utilizes the original satellite measurement information and remote sensing information when the BeiDou-3 signal is available for training and prediction, enabling compensation and correction of the positioning error based on remote sensing information. Simulation experiments demonstrate the effectiveness of these methods in improving the navigation accuracy of the BeiDou-3 satellite navigation system in complex environments.

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A Comprehensive High Accuracy Positioning Method Based on BD3 System

Zeng

et al. 2022

2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)

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Optimal Selection Mechanism of Short Message Terminal for "Beidou-3"

Cited by 3 publications

References 7 publications

BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

Fusion Navigation of BeiDou-3 and Remote Sensing Information in the Case of Satellite Signal Loss

A Comprehensive High Accuracy Positioning Method Based on BD3 System

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