Modern sensing systems generate heterogeneous updates ranging from small status packets to large data objects. We study a single-hop wireless uplink network where sensors generate updates at will, each consisting of a sensor dependent number of packets. Under a strict medium-access constraint and non-preemptive (no-switching) transmissions, decision stages become action-dependent and stochastic. We formulate the problem as a restless multi-armed bandit (RMAB) with semi-Markov decision process (SMDP) dynamics and develop a Lagrange index based heuristic for minimizing weighted average AoI cost. For the weighted AoI setting, we utilize the structural properties of the heuristic to enable efficient index computation. Numerical results demonstrate consistent performance gains over existing non-preemptive scheduling policies, providing a practical solution for heterogeneous freshness-aware systems.

翻译：现代传感系统生成从状态小包到大型数据对象的异构更新。我们研究单跳无线上行网络，其中传感器按需生成更新，每个更新包含取决于传感器的数据包数量。在严格的介质访问约束和非抢占式（无切换）传输条件下，决策阶段变得依赖于动作且具有随机性。我们将该问题建模为具有半马尔可夫决策过程（SMDP）动力学的休止多臂赌博机（RMAB），并开发了基于拉格朗日指数的启发式算法以最小化加权平均信息时效（AoI）成本。针对加权AoI场景，我们利用该启发式算法的结构特性实现高效的指数计算。数值结果表明，该算法相较于现有非抢占式调度策略具有一致性的性能优势，为异构时效感知系统提供了实用解决方案。