结合时间窗口选择的无线多摄像头感知的潜在年龄与资源联合最小化
Joint Age-of-Latent and Resource Minimization for Wireless Multi-Camera Perception With Temporal Window Selection
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中文总结 AI 辅助
针对无线多摄像头感知的基站资源受限问题，提出CoLA算法，通过李雅普诺夫优化与近端策略优化，实现潜在年龄与资源的联合最小化，在仓库数据集上表现最优。
中文摘要 AI 辅助
多摄像头无线感知需要基站（BS）在有限的上行链路资源下维持来自分布式摄像头的及时且可靠的潜在信念。传统的信息年龄（AoI）衡量最新接收更新的年龄，但未考虑与任务相关的潜在内容。本文引入潜在年龄（AoL）以量化每个摄像头最新解码的潜在表示的新鲜度。有限的时间积分窗口（TWI）决定任务承诺时间和可用上行链路时隙，在更新机会、预测时长、承诺时间AoL、预测可靠性及累积资源成本之间形成权衡。在该时间范围内，冗余或重叠视图可实现相关预测，减少对高成本通信和编码配置的依赖，以维持基站侧的潜在信念。我们构建联合AoL-资源最小化问题，将任务级TWI选择与时隙级编码器选择、调度及非正交多址（NOMA）功率分配耦合，同时满足预测可靠性约束。本文提出用于AoL最小化的感知相关潜在预测（CoLA）算法，其基于AoL-资源成本和预测不确定性，采用李雅普诺夫优化进行任务级TWI选择，采用近端策略优化进行时隙级资源控制。在仓库多摄像头射频数据集上的结果表明，CoLA可使TWI适应摄像头更新可用性，在维持预测可靠性的同时，在基准方法中实现最优的AoL-资源权衡，尤其在摄像头长时间严重中断的情况下表现突出。
英文摘要
Multi-camera wireless perception requires a base station (BS) to maintain timely and reliable latent beliefs from distributed cameras under limited uplink resources. Conventional Age-of-Information (AoI) measures the age of the latest received update but not task-relevant latent content. We introduce Age-of-Latent (AoL) to quantify the freshness of each camera's latest decoded latent representation. A finite temporal window of integration (TWI) determines the task-commitment time and available uplink slots, creating a tradeoff among update opportunities, prediction duration, commit-time AoL, prediction reliability, and accumulated resource cost. Within this horizon, redundant or overlapping views enable correlated prediction, reducing reliance on the highest-cost communication and encoding configuration to maintain BS-side latent beliefs. We formulate a joint AoL-resource minimization problem coupling task-level TWI selection with slot-level encoder selection, scheduling, and NOMA power allocation under prediction-reliability constraints. We propose correlation-aware latent prediction for AoL minimization (CoLA), which uses Lyapunov optimization for task-level TWI selection based on AoL-resource cost and prediction uncertainty, and proximal policy optimization for slot-level resource control. Results on a warehouse multi-camera RF dataset show that CoLA adapts the TWI to camera-update availability and achieves the most favorable AoL-resource tradeoff among the benchmarks while maintaining prediction reliability, particularly under prolonged and severe camera outages.
