The envisioned robotic aerial base station (RABS) concept is expected to bring further flexibility to integrated sensing and communication (ISAC) systems. In this letter, characterizing the spatial traffic distribution on a grid-based model, the RABS-assisted ISAC system is formulated as a robust optimization problem to maximize the minimum satisfaction rate (SR) under a cardinality constrained uncertainty set. The problem is reformulated as a mixed-integer linear programming (MILP) and solved approximately by the iterative linear programming rounding algorithm. Numerical investigations show that the minimum SR can be improved by 28.61% on average compared to fixed small cells.

翻译：本文所提出的机器人空中基站（RABS）概念有望为集成感知与通信（ISAC）系统带来更高的灵活性。基于网格模型刻画空间流量分布，本文将RABS辅助的ISAC系统建模为鲁棒优化问题，旨在基数约束不确定性集下最大化最小满意率（SR）。该问题被重构为混合整数线性规划（MILP），并通过迭代线性规划舍入算法近似求解。数值结果表明，与固定小蜂窝相比，最小SR平均可提升28.61%。