To evaluate the timeliness and freshness of information from energy-constrained devices, we minimize the weighted average age of information (AoI) of the ambient backscatter-assisted energy harvesting cognitive radio network (AB-EH-CRN), where secondary transmitters (STs) follow the non-orthogonal multiple access (NOMA) strategy to transmit status updates. The secondary receiver (SR) schedules the selected actions for STs to perform spectrum access, and receives status updates from them. We formulate the AoI minimization problem where the action selection and transmission power of STs are considered as variables. To exploit spectrum resources in the AB-EH-CRN with unknown signal-to-noise ratios (SNRs) at the STs, we propose the deep neural network (DNN)-based cooperative spectrum sensing (CSS), and adopt an energy threshold approach for STs to determine the energy allocation between spectrum sensing (SS) and packet transmission. Moreover, we address the AoI minimization problem by the proposed hybrid action and energy penalty deep deterministic policy gradient (HAEP-DDPG) algorithm, which adapts to the hybrid continuous and discrete action spaces. Simulation results validate the advantage of the proposed HAEP-DDPG algorithm compared with comparison schemes in terms of AoI.

中文翻译：

---

具有协作频谱感知的环境反向散射辅助 EH-CRN 的 AoI 最小化

为了评估来自能量受限设备的信息的及时性和新鲜度，我们最小化了环境反向散射辅助能量收集认知无线电网络（AB-EH-CRN）的信息加权平均年龄（AoI），其中辅助发射机（ST）遵循非正交多址（NOMA）策略来传输状态更新。辅助接收器 (SR) 为 ST 安排选定的操作来执行频谱接入，并从它们接收状态更新。我们制定了 AoI 最小化问题，其中 ST 的动作选择和传输功率被视为变量。为了利用 ST 处信噪比（SNR）未知的 AB-EH-CRN 中的频谱资源，我们提出了基于深度神经网络（DNN）的协作频谱感知（CSS），并采用能量阈值方法供 ST 确定频谱感知 (SS) 和数据包传输之间的能量分配。此外，我们通过提出的混合动作和能量惩罚深度确定性策略梯度（HAEP-DDPG）算法解决了 AoI 最小化问题，该算法适应混合连续和离散动作空间。仿真结果验证了所提出的 HAEP-DDPG 算法与 AoI 方面的比较方案相比的优势。