School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2022 Apr 24;22(9):3262. doi: 10.3390/s22093262.
The combination of ambient backscatter (AB) communications (ABCs) and RF-powered cognitive radio networks (CRNs) deals with challenges of both energy supply and spectrum shortage, and improves the network performances. With the expansion of wireless networks, many applications raise requirements for both high-throughput and timely data. Driven by these facts, we study the long-term throughput optimization of the secondary network in the AB-assisted overlay CRN (ABO-CRN), ABCs, and CRNs with the age of information (AoI) constraint, which is a novel metric for measuring the freshness of data received by receivers. Due to the dynamic environment, complete knowledge of the environment could not be obtained. Then, the deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) method that addresses decision issues in both continuous and discrete spaces, is deployed to address the throughput optimization. We consider the impacts of time and energy allocation on the reward when the AoI constraint can not be satisfied, and develop the corresponding reward functions. Furthermore, we analyze the impacts of the minimum throughput requirement and maximum allowable AoI on the throughput and AoI of the secondary networks in the ABO-CRN, ABCs, and CRNs. We compare the throughput optimization scheme under the AoI constraint with two baseline schemes (i.e., throughput-optimal (T-O) and AoI-optimal (A-O) baseline schemes), and the simulation results show that the throughput of the ABO-CRN is close to the optimal throughput of the T-O baseline scheme, and the AoI of the ABO-CRN is close to the optimal AoI of the A-O baseline scheme.
环境反向散射 (AB) 通信 (ABCs) 和射频供电认知无线电网络 (CRNs) 的组合解决了能源供应和频谱短缺的挑战，并提高了网络性能。随着无线网络的扩展，许多应用程序对高吞吐量和及时数据提出了要求。受这些事实的驱动，我们研究了辅助覆盖认知无线电网络 (ABO-CRN)、ABCs 和具有信息年龄 (AoI) 约束的 CRNs 中的次网络的长期吞吐量优化，AoI 是一种用于衡量接收者接收到的数据新鲜度的新指标。由于动态环境，无法获得环境的完整知识。然后，部署深度确定性策略梯度 (DDPG) 作为一种深度强化学习 (DRL) 方法，用于解决连续和离散空间中的决策问题，以解决吞吐量优化问题。当 AoI 约束不能满足时，我们考虑时间和能量分配对奖励的影响，并开发相应的奖励函数。此外，我们分析了最小吞吐量要求和最大允许 AoI 对 ABO-CRN、ABCs 和 CRNs 中次网络的吞吐量和 AoI 的影响。我们将 AoI 约束下的吞吐量优化方案与两种基线方案（即吞吐量最优 (T-O) 和 AoI 最优 (A-O) 基线方案）进行了比较，仿真结果表明，ABO-CRN 的吞吐量接近 T-O 基线方案的最优吞吐量，ABO-CRN 的 AoI 接近 A-O 基线方案的最优 AoI。
