Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

Lei, Lei; Wang, Anyue; Lagunas, Eva; Hu, Xin; Zhang, Zhengquan; Wei, Zhiqiang; Chatzinotas, Symeon

doi:10.1109/jsac.2024.3383445

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the processing domain of the received signal, the adaptive wideband beamforming algorithms can be categorized into spatial-temporal and spatial-frequency beamforming, respectively [10][11][12]. Frost beamformer (FB) is a well-known wideband space-time beamformer that employs a series of tapped delay lines (TDLs) for frequency-dependent weighting [13].…”

Section: Introductionmentioning

confidence: 99%

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Guo,

Shen,

Zhou

et al. 2024

Electronics

View full text Add to dashboard Cite

Wideband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems to compensate for severe path loss through beamforming gain. However, traditional adaptive wideband digital beamforming (AWDBF) algorithms suffer from serious performance degradation when there are insufficient signal snapshots, and the training process of the existing neural network-based wideband beamforming network is slow and unstable. To address the above issues, an AWDBF method based on the convolutional neural network (CNN) structure, the improved wideband beamforming prediction network (IWBPNet), is proposed. The proposed method increases the network’s feature extraction capability for array signals through deep convolutional layers, thus alleviating the problem of insufficient network feature extraction capabilities. In addition, the pooling layers are introduced into the IWBPNet to solve the problem that the fully connected layer of the existing neural network-based wideband beamforming algorithm is too large, resulting in slow network training, and the pooling operation increases the generalization ability of the network. Furthermore, the IWBPNet has good wideband beamforming performance with low signal snapshots, including beam pattern performance and output signal-to-interference-plus-noise ratio (SINR) performance. The simulation results show that the proposed algorithm has superior performance compared with the traditional wideband beamformer with low signal snapshots. Compared with the wideband beamforming algorithm based on the neural network, the training time of IWBPNet is only 10.6% of the original neural network-based wideband beamformer, while the beamforming performance is slightly improved. Simulations and numerical analyses demonstrate the effectiveness and superiority of the proposed wideband beamformer.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Guo,

Shen,

Zhou

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites

Chen,

Jiang,

Yan

2024

Sensors

View full text Add to dashboard Cite

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

show abstract

Spatial–Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

Cited by 2 publications

References 26 publications

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

Improved Convolutional Neural Network for Wideband Space-Time Beamforming

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites

Contact Info

Product

Resources

About