AI 中文总结
针对流体天线系统的端口切换成本与时隙级性能问题，提出在线保形贝叶斯优化算法，构建感知成本多目标切换框架，仿真显示其长期ISAC性能优于现有基线。
AI 中文摘要
流体天线系统（Fluid Antenna Systems, FAS）为空地网络的集成感知与通信（Integrated Sensing and Communication, ISAC）引入了额外的空间自由度。然而，现有研究常忽略或简化FAS的物理开销与切换成本，实际中端口切换会产生不可忽略的时间，此期间通信与感知仍可进行但时隙级性能可能下降，这带来两大关键挑战：一是难以表征时隙级、感知成本的ISAC指标；二是庞大的端口空间及伴随的突发环境变化需要更可靠的在线决策。为应对这些挑战，本文构建了考虑时隙级ISAC性能与切换能量的感知成本多目标FAS切换问题，提出在线保形贝叶斯优化（Online Conformal Bayesian Optimization, OCBO）算法以学习未知的灰盒ISAC目标并校准代理不确定性，从而做出鲁棒的保留或切换决策。仿真结果表明，所提感知成本优化框架与现有基线相比，实现了显著提升的长期ISAC性能。
英文摘要
Fluid antenna systems (FAS) introduce additional spatial degrees of freedom to enable integrated sensing and communication (ISAC) in air-ground networks. However, conventional studies often overlook or simplify the physical overheads and switching costs of FAS. In practice, port switching incurs non-negligible time, during which communication and sensing may continue but with potentially degraded slot-level performance. This leads to two key challenges: (1) the characterization of a slot-level, cost-aware ISAC metric is difficult, and (2) the large port space and accompanying abrupt environmental variations demand more reliable online decision-making. To address these challenges, a cost-aware multi-objective FAS switching problem is formulated, jointly considering slot-level ISAC performance and switching energy. The online conformal Bayesian optimization (OCBO) algorithm is then proposed to learn the unknown gray-box ISAC objectives and calibrate surrogate uncertainty for robust stay-or-switch decisions. Simulation results demonstrate that the proposed cost-aware optimization framework achieves substantially improved long-term ISAC performance compared to existing baselines.
Comments6 pages, 5 figures, conference
