With the rapid development of satellite communications, low Earth orbit satellite networks have attracted considerable attention because of their high data delivery capability and low propagation delay. However, the increasing scarcity of frequency resources has become a major obstacle to their large-scale deployment. To address this issue, this paper proposes a resource optimization framework that combines cooperative single-layer distributed rate-splitting multiple access with cognitive radio to improve spectrum utilization in satellite systems. A coexistence communication model is established for a secondary low Earth orbit satellite network and a primary geostationary Earth orbit satellite network. Based on this model, the maximum achievable sum rate of the low Earth orbit system is obtained by optimizing the transmit-power allocation and common-rate allocation variables under minimum mean square error-based precoding. The resulting optimization problem is efficiently addressed by a greedy-and-swap user-association strategy combined with the successive convex approximation algorithm. Numerical simulation results verify that the framework proposed in this paper features fast convergence. Comparative analyses against ablation experiment frameworks and multiple access benchmark frameworks demonstrate that the proposed joint resource allocation distributed rate-splitting multiple access framework can improve the performance of low Earth orbit satellite communication systems while satisfying multiple constraint conditions.
Xianpeng Wang, Xi Han, Mingqi Gao et al.· IEEE Access· 0 citations
Mobile manipulation requires a robot to coordinate base and arm motion under continuously changing viewpoints and contact conditions, within an action space far larger than that of fixed-base manipulation. Existing Vision-Language-Action (VLA) policies are limited in two respects. (i)They map observations directly to whole-body action chunks, searching this large action space without an explicit task-space motion plan, which makes coordinated base--arm prediction imprecise. (ii)They execute the predicted chunk open-loop, without checking whether the actions can realize the motion the policy intended, so control errors and unmodeled contacts accumulate into a gap between planned and realized motion. We present DreamTrajectory, a trajectory-guided framework for language-conditioned mobile manipulation that introduces one component for each limitation. Addressing(i), DreamTrajectory jointly predicts an intention-level end-effector trajectory and a whole-body action chunk in a single action expert, so that the trajectory explicitly guides base--arm action generation instead of remaining implicit. Addressing(ii), a lightweight trajectory world model predicts the trajectory that a candidate action chunk would induce, and a test-time search--predict--score procedure selects the candidate best aligned with the planned trajectory. On MS-HAB, trajectory guidance raises average success from 32.3% to 47.5% and test-time refinement further to 54.8%, with the largest gains on contact-rich articulated-object tasks. On three real-world mobile manipulation tasks, the corresponding average success rates are 63.3%, 81.7%, and 90.0%.
Zheng Yang, Wenjie Zhang, Xiangyu Chen et al.· 0 citations