4.7 Article

Sensing-Mining-Access Tradeoff in Blockchain-Enabled Dynamic Spectrum Access

期刊

IEEE WIRELESS COMMUNICATIONS LETTERS
卷 10, 期 4, 页码 820-824

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2020.3045776

关键词

Sensors; Blockchain; Optimization; Resource management; Throughput; Probability; Wireless communication; Dynamic spectrum access; blockchain; cognitive radio network

资金

  1. National Natural Science Foundation of China [61631005, U1801261]
  2. National Key Research and Development Program of China [2018YFB1801105]
  3. Key Areas of Research and Development Program of Guangdong Province, China [2018B010114001]
  4. Fundamental Research Funds for the Central Universities [ZYGX2019Z022]
  5. Programme of Introducing Talents of Discipline to Universities [B20064]
  6. Ignition Grant of SIT [RMOE-E103-F006]

向作者/读者索取更多资源

The study proposes the application of blockchain to DSA and aims to optimize the frame structure regarding sensing time and mining time to maximize achievable throughput. It proves the existence of a unique maximum point for both sub-optimization problems and proposes an alternating algorithm for optimization, illustrating the tradeoff and effectiveness through simulations.
Dynamic spectrum access (DSA) is crucial to improve the utilization efficiency of the limited and precious radio spectrum resources. Recently, the application of blockchain is proposed to improve the security, distribution and transparency of DSA. However, in opportunistic spectrum access (OSA), the implementation of blockchain consumes considerable amount time in each time slot so that the time left for spectrum sensing and access will be decreased. Therefore, in this letter, we aim to optimize the frame structure regarding the sensing time and mining time so that the average achievable throughput is maximized. We first decouple the original optimization problem into two sub-optimization problems with respect to sensing time and mining time, respectively, and then prove that there exists a unique maximum point for both the two sub-optimization problems. After that, an alternating algorithm is proposed for the optimization. Using the simulations, the sensing-mining-access tradeoff and effectiveness of our proposed algorithm to optimize such a tradeoff are illustrated.

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