Essential Site Maintenance: Authorea-powered sites will be updated circa 15:00-17:00 Eastern on Tuesday 5 November.
There should be no interruption to normal services, but please contact us at [email protected] in case you face any issues.

loading page

Sequential Detection with Feedback Information for Two-way Cooperative Spectrum Sensing in Cognitive Internet of Things
  • +5
  • Jun Wu,
  • Mingkun Su,
  • Jianrong Bao,
  • Lei Qiao,
  • Xiaorong Xu,
  • Hao Wang,
  • Gefei Zhu,
  • Weiwei Cao
Jun Wu
Hangzhou Dianzi University School of Communication Engineering

Corresponding Author:[email protected]

Author Profile
Mingkun Su
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Jianrong Bao
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Lei Qiao
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Xiaorong Xu
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Hao Wang
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Gefei Zhu
Hangzhou Dianzi University School of Communication Engineering
Author Profile
Weiwei Cao
Civil Aviation Flight University of China
Author Profile

Abstract

With the rapid growth of internet of thing (IoT) wireless devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large-scale cognitive IoTs (CIoTs). To address these challenges, we present a sequential detection with feedback information (SD-FI) approach in this paper. To achieve this objective, we propose a two-way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD-FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of our proposed approach in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD-FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in cognitive IoTs.
Submitted to Transactions on Emerging Telecommunications Technologies
12 Mar 2024Reviewer(s) Assigned
07 May 2024Editorial Decision: Revise Major
20 May 20241st Revision Received
20 May 2024Review(s) Completed, Editorial Evaluation Pending