mlsanityData to 14 Oct 2026

Joint Detection Algorithm for Multiple Cognitive Users in Spectrum Sensing

arXiv 2311.18599 ↗First posted 30 Nov 2023eess.SP cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Spectrum sensing technology is a crucial aspect of modern communication technology, serving as one of the essential techniques for efficiently utilizing scarce information resources in tight frequency bands. This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor. Building upon hard decisions, the paper further introduces a method for multi-user spectrum sensing based on soft decisions. Then the paper simulates the false alarm probability and detection probability curves corresponding to the three criteria. The simulated results of multi-user collaborative sensing indicate that the simulation process significantly reduces false alarm probability and enhances detection probability. This approach effectively detects spectrum resources unoccupied during idle periods, leveraging the concept of time-division multiplexing and rationalizing the redistribution of information resources. The entire computation process relies on the calculation principles of power spectral density in communication theory, involving threshold decision detection for noise power and the sum of noise and signal power. It provides a secondary decision detection, reflecting the perceptual decision performance of logical detection methods with relative accuracy.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 30 Nov 2023.

Builds on

4 of 15 references.

References · 4

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-levelcited by 5
  4. 2023Sentiment analysis with adaptive multi-head attention in Transformercited by 5

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.