Pan, Qianqian, Wu, Jun, Bashir, Ali Kashif, Li, Jianhua, Vashisht, Sahil and NAWAZ, Raheel (2022) Blockchain and AI Enabled Configurable Reflection Resource Allocation for IRS-Aided Coexisting Drone-Terrestrial Networks. IEEE Wireless Communications, 29 (6). pp. 46-54. ISSN 1536-1284
Full text not available from this repository. (Request a copy)Abstract or description
With the capability of establishing line-of-sight (LoS) links for devices, drones are generally utilized as aerial base stations to construct coexisting drone-terrestrial networks (CDTNs) for wireless communication. However, the established LoS links are easily blocked, thereby severely decreasing transmission performance. The intelligent reflecting surface (IRS) is a promising technology to improve data transmission in the CDTN by programming propagation channels. However, secure IRS reflection resource allocation is still an open issue. Existing IRS resource allocation methods are mainly based on a centralized third party and are vulnerable to the single point of failure. Furthermore, intelligent allocation of IRS reflection resources is also a key issue. To solve these problems, we propose a blockchain and artificial intelligence (AI) enabled configurable reflection resource allocation approach for the IRS-aided CDTN. First, we establish the IRS-aided communication framework for the CDTN, where a drone-mounted IRS is introduced to improve spatial freedom for data transmission. Second, the blockchain-based reflection resource management mechanism is proposed. In this mechanism, we design allocation transactions, the hierarchical blockchain structure, and smart-contract-enabled resource trading. Third, the AI-based reflection resource allocation mechanism is proposed, including the intelligent reflection elements assignment and deep-reinforcement-learning-driven reflection coefficient configuration. Furthermore, experimental results verify the effectiveness of our proposed approach. Finally, open issues and key challenges of the proposed approach are discussed. © 2002-2012 IEEE.
Item Type: | Article |
---|---|
Faculty: | Executive |
Depositing User: | Raheel NAWAZ |
Date Deposited: | 13 Sep 2024 13:39 |
Last Modified: | 13 Sep 2024 13:39 |
URI: | https://eprints.staffs.ac.uk/id/eprint/8465 |