  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.
