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<p>The integration of space–air–ground wireless communications calls for energy-sustainable UAV operation and efficient multi-user service provisioning. To overcome the limited adaptability of conventional RIS/STAR-RIS static partitioning, this paper proposes a dynamic STAR-RIS-assisted UAV energy harvesting and heterogeneous user communication framework, where STAR-RIS resources are dynamically allocated to balance UAV energy replenishment and communication performance. We formulate a weighted sum-rate maximization problem subject to UAV energy self-sustainability constraints by jointly optimizing the UAV trajectory, the dynamic STAR-RIS partition ratio, and the phase-shift configuration. To efficiently solve the resulting non-convex problem, an alternating-optimization algorithm is developed, which integrates sequential convex programming for trajectory refinement, an adaptive perturbation update for the partition ratio, and gradient-ascent-based phase optimization. Numerical results under varying RF transmit power levels, STAR-RIS element numbers, and user population sizes verify that the proposed dynamic joint optimization consistently outperforms static/fixed-partition baselines; for example, at <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(K=40\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(M=64\)</EquationSource> </InlineEquation>, and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(P_{\textrm{RF}}=35\,\textrm{dBm}\)</EquationSource> </InlineEquation>, it increases the weighted sum rate from 3900 (static RIS) to 6200, achieving a <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(59.0\%\)</EquationSource> </InlineEquation> improvement. Moreover, the throughput advantage becomes increasingly significant as the number of users grows. These results demonstrate the effectiveness of dynamic STAR-RIS partitioning for energy-aware UAV-assisted communications and provide useful design insights for next-generation intelligent space–air–ground networks.</p>

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Joint design of UAV energy harvesting and heterogeneous user communications based on dynamic STAR-RIS partitioning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Jian Tang,
  • Mengxiang Chen,
  • Shufang Liu,
  • Fengmin Hu,
  • Zhisheng Hu

摘要

The integration of space–air–ground wireless communications calls for energy-sustainable UAV operation and efficient multi-user service provisioning. To overcome the limited adaptability of conventional RIS/STAR-RIS static partitioning, this paper proposes a dynamic STAR-RIS-assisted UAV energy harvesting and heterogeneous user communication framework, where STAR-RIS resources are dynamically allocated to balance UAV energy replenishment and communication performance. We formulate a weighted sum-rate maximization problem subject to UAV energy self-sustainability constraints by jointly optimizing the UAV trajectory, the dynamic STAR-RIS partition ratio, and the phase-shift configuration. To efficiently solve the resulting non-convex problem, an alternating-optimization algorithm is developed, which integrates sequential convex programming for trajectory refinement, an adaptive perturbation update for the partition ratio, and gradient-ascent-based phase optimization. Numerical results under varying RF transmit power levels, STAR-RIS element numbers, and user population sizes verify that the proposed dynamic joint optimization consistently outperforms static/fixed-partition baselines; for example, at \(K=40\) , \(M=64\) , and \(P_{\textrm{RF}}=35\,\textrm{dBm}\) , it increases the weighted sum rate from 3900 (static RIS) to 6200, achieving a \(59.0\%\) improvement. Moreover, the throughput advantage becomes increasingly significant as the number of users grows. These results demonstrate the effectiveness of dynamic STAR-RIS partitioning for energy-aware UAV-assisted communications and provide useful design insights for next-generation intelligent space–air–ground networks.