LDM-satellite: A new scheme for packet loss classification over LEO satellite network

A Loss Differentiation Method Based on Heterogeneous Ensemble Learning Model for Low Earth Orbit Satellite Networks

“…In Section 4, we propose a heterogeneous ensemble learning model for discriminating packet loss in satellite networks. In Section 5, the loss differentiation method based on heterogeneous ensemble learning (LDM-HEL), loss detection method (LDM)-satellite [11], and LLD method [12] are used as comparative experiments for simulation, which proves the superiority of the algorithm proposed in this paper. Finally, Section 6 concludes the paper.…”

“…Additionally, optimal parameters for relevant algorithms were determined and their rationality in terms of parameter settings is verified. Finally, in order to assess the performance advantages of our proposed method, we compared it with the LDM-satellite [11] and LLD [12] loss differentiation models under identical conditions.…”

“…Additionally, considering algorithm complexity and false alarm rate, the BP neural network does not perform optimally in discriminating data packet loss; hence, it was excluded from the final selection scheme for individual classifiers, leaving four types: LSSVM, DT, LR, and kNN. To validate the performance enhancement of our proposed integrated detection method LDM-HEL compared to existing methods such as LDM-satellite [11], LLD [12], Spike method, Zigzag method, and ZBS method [4][5][6]; we plotted average performance results for four detection indicators (accuracy, precision, recall, and F1-score) across five different detection methods as shown in Figure 4. Furthermore, it can be observed from Table 8 that the Q statistic value of the BP neural network is 0.9, ranking second-highest.…”

“…To validate the performance enhancement of our proposed integrated detection method LDM-HEL compared to existing methods such as LDM-satellite [11], LLD [12], Spike method, Zigzag method, and ZBS method [4][5][6]; we plotted average performance results for four detection indicators (accuracy, precision, recall, and F1-score) across five different detection methods as shown in Figure 4. Firstly, as depicted in Figure 4, the proposed LDM-HEL, the LDM-Satellite, and the LLD method have shown significant performance enhancements compared to conven- Firstly, as depicted in Figure 4, the proposed LDM-HEL, the LDM-Satellite, and the LLD method have shown significant performance enhancements compared to conventional classifiers.…”