The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

Multisource fusion localization is a mainstream scheme for acquiring accurate locations in complex indoor scenes. To overcome the interference of indoor structures on radio and illumination variation on visual features, the semantic maps provide an effective way for multisource fusion localization. However, due to the lack of visual depth information, solutions of indoor semantic maps suffer from large semantic segmentation errors for similar objects, which leads to the unstable performance of localization systems. To overcome the issue in semantic and fusion localization, we develop a localization system to demonstrate the use of restudy semantic map and self-adapting fusion localization would achieve centimeter-level positioning accuracy, termed VISEL. VISEL uses the proposed spatial attention-aware semantic model to enhance the discrimination of semantic features for capturing accurate semantic maps. On the basis of high-precision semantic maps, VISEL completes an enhanced particle filter fusion localization module with adaptive reassign weight to different localization modules, which successfully improves accuracy through complementary advantages between different signals while overcoming the drawbacks of each signal and interference of complex environment. The extensive experimental results show that VISEL outperforms current state-of-the-art positioning systems and achieves an average positioning accuracy of 0.4 m. VISEL utilizes semantic maps with depth features and enhanced particle filter to reduce the fusion localization error by 38%, which suggests the high-precision semantic maps with depth features could provide a robust solution for the fusion localization system for indoor complex scenes.