In this paper, we develop a new framework called blockchain-based Radio Frequency (RF)-powered backscatter cognitive radio network. In the framework, IoT devices as secondary transmitters transmit their sensing data to a secondary gateway by using the RF-powered backscatter cognitive radio technology. The data collected at the gateway is then sent to a blockchain network for further verification, storage and processing. As such, the framework enables the IoT system to simultaneously optimize the spectrum usage and maximize the energy efficiency. Moreover, the framework ensures that the data collected from the IoT devices is verified, stored and processed in a decentralized but in a trusted manner. To achieve the goal, we formulate a stochastic optimization problem for the gateway under the dynamics of the primary channel, the uncertainty of the IoT devices, and the unpredictability of the blockchain environment. In the problem, the gateway jointly decides (i) the time scheduling, i.e., the energy harvesting time, backscatter time, and transmission time, among the IoT devices, (ii) the blockchain network, and (iii) the transaction fee rate to maximize the network throughput while minimizing the cost. To solve the stochastic optimization problem, we then propose to employ, evaluate, and assess the Deep Reinforcement Learning (DRL) with Dueling Double Deep Q-Networks (D3QN) to derive the optimal policy for the gateway. The simulation results clearly show that the proposed solution outperforms the conventional baseline approaches such as the conventional Q-Learning algorithm and non-learning algorithms in terms of network throughput and convergence speed. Furthermore, the proposed solution guarantees that the data is stored in the blockchain network at a reasonable cost.

中文翻译：

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基于区块链的射频驱动反向散射认知无线电网络中的联合时间调度和交易费用选择

在本文中，我们开发了一个称为基于区块链的射频 (RF) 驱动的反向散射认知无线电网络的新框架。在该框架中，作为辅助发射器的物联网设备使用射频驱动的反向散射认知无线电技术将其传感数据传输到辅助网关。然后将在网关收集的数据发送到区块链网络进行进一步验证、存储和处理。因此，该框架使物联网系统能够同时优化频谱使用并最大限度地提高能源效率。此外，该框架确保从物联网设备收集的数据以分散但受信任的方式进行验证、存储和处理。为了达到这个目标，我们在主通道的动态下为网关制定了一个随机优化问题，物联网设备的不确定性，以及区块链环境的不可预测性。在该问题中，网关共同决定（i）物联网设备之间的时间调度，即能量收集时间、反向散射时间和传输时间，（ii）区块链网络，以及（iii）交易费率最大化网络吞吐量，同时最小化成本。为了解决随机优化问题，我们建议使用、评估和评估深度强化学习 (DRL) 与决斗双深度 Q 网络 (D3QN) 以推导出网关的最佳策略。仿真结果清楚地表明，所提出的解决方案在网络吞吐量和收敛速度方面优于传统的基线方法，如传统的 Q-Learning 算法和非学习算法。此外，