Sequential Detection with Feedback Information for Two-way Cooperative
Spectrum Sensing in Cognitive Internet of Things
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.
, 
