Joint Time Scheduling and Transaction Fee Selection in Blockchain-based RF-Powered Backscatter Cognitive Radio Network
Tran The Anh, Nguyen Cong Luong, Zehui Xiong, Dusit Niyato, and Dong, In Kim

TL;DR
This paper introduces a blockchain-based RF-powered backscatter cognitive radio network framework that optimizes spectrum and energy efficiency while ensuring secure data storage, using deep reinforcement learning for joint scheduling and fee selection.
Contribution
It presents a novel integrated framework combining RF-powered backscatter, blockchain verification, and DRL-based optimization for IoT networks, addressing spectrum, energy, and security challenges.
Findings
Outperforms baseline algorithms in network throughput.
Achieves faster convergence with D3QN.
Ensures cost-effective blockchain data storage.
Abstract
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…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Cognitive Radio Networks and Spectrum Sensing · Age of Information Optimization
