基于区块链与深度学习的空间分集协作频谱感知系统

肖仲杉; 王春琦; 冯大权

doi:10.11959/j.issn.1000-0801.2023193

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基于区块链与深度学习的空间分集协作频谱感知系统

研究与开发 | 更新时间：2024-06-05

- 基于区块链与深度学习的空间分集协作频谱感知系统
- A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning
- 电信科学 2023年39卷第10期页码：49-63
- 作者机构：
  
  1. 深圳大学，广东深圳 518060
  2. 国家无线电监测中心，北京 100037
- 作者简介：
  
  [ "肖仲杉（1999- ），男，深圳大学硕士生，主要研究方向为频谱共享共存" ]
  [ "王春琦（1991- ），男，国家无线电监测中心工程师，主要研究方向为联盟链监管和频谱共享共存" ]
  [ "冯大权（1986- ），男，博士，深圳大学副教授，主要研究方向为 D2D 通信、频谱共享共存、车联网等" ]
- 基金信息：
  
  国家重点研发计划项目;The National Key Research and Development Program of China(2020YFB1807600)
- DOI：10.11959/j.issn.1000-0801.2023193
  中图分类号： TP393
- 网络出版日期：2023-10，
  
  纸质出版日期：2023-10-20
- 稿件说明：
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肖仲杉, 王春琦, 冯大权. 基于区块链与深度学习的空间分集协作频谱感知系统[J]. 电信科学, 2023,39(10):49-63.

Zhongshan XIAO, Chunqi WANG, Daquan FENG. A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning[J]. Telecommunications science, 2023, 39(10): 49-63.
肖仲杉, 王春琦, 冯大权. 基于区块链与深度学习的空间分集协作频谱感知系统[J]. 电信科学, 2023,39(10):49-63. DOI： 10.11959/j.issn.1000-0801.2023193.

Zhongshan XIAO, Chunqi WANG, Daquan FENG. A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning[J]. Telecommunications science, 2023, 39(10): 49-63. DOI： 10.11959/j.issn.1000-0801.2023193.

摘要

协作频谱感知是认知无线电中的关键技术。针对协作频谱感知中存在的安全性、隐私性、激励性和隐藏终端等问题，提出了一种运行在智能合约上的数据驱动的智能化空间分集的协作频谱感知系统。具体地，利用区块链的去中心化、数据难以篡改等特性，设计了一种激励性的协作频谱感知系统，并采用深度学习的方法来识别系统中的恶意用户。此外，针对如何更为高效地在该系统中招募感知节点以达到较高的感知准确率，设计了基于性能权重和空间分集的硬判决协作频谱感知融合算法。实验结果表明，所提算法在安全性、隐私性、激励性、感知准确率方面优于传统的协作频谱感知算法。

Abstract

Cooperative spectrum sensing is a key technology in cognitive radio.A data-driven intelligent cooperative spectrum sensing system was proposed with spatial diversity running on a smart contract to address issues of security

privacy

incentive and hidden terminals in cooperative spectrum sensing.Specifically

a motivated spectrum sensing system was designed by taking advantage of the decentralization of blockchain technology and the immutability of data.Secondly

a deep learning-based approach was proposed to identify malicious users in the system.In addition

to achieve higher accuracy in recruiting sensing nodes more efficiently in the system

a hard decision cooperative spectrum sensing fusion algorithm based on performance weights and spatial diversity was designed.The experimental results indicate that the proposed solution outperforms traditional cooperative spectrum sensing algorithms in terms of security

privacy

motivation

and sensing accuracy.

关键词

Keywords

references

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