CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION (20240236693). SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS simplified abstract
Contents
- 1 SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Semi-Distributed Spectrum Sensing in Cognitive IoT Networks
- 1.11 Original Abstract Submitted
SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS
Organization Name
CHUNG ANG UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION
Inventor(s)
Sung Rae Cho of Seongnam-si (KR)
SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240236693 titled 'SEMI-DISTRIBUTED SPECTRUM SENSING METHOD AND APPARATUS IN COGNITIVE INTERNET OF THINGS NETWORKS
Simplified Explanation
The patent application describes a method for semi-distributed spectrum sensing in cognitive IoT networks, along with the apparatus used for this method.
- Grouping secondary terminals based on local information into local clusters.
- Generating overlapping point information to determine directional antenna beams of secondary terminals.
- Calculating and adjusting the overlapping range between local clusters.
Key Features and Innovation
- Semi-distributed spectrum sensing method in cognitive IoT networks.
- Grouping secondary terminals into local clusters based on local information.
- Determining directional antenna beams of secondary terminals using overlapping point information.
- Calculating and adjusting overlapping ranges between local clusters.
Potential Applications
This technology can be applied in various industries such as telecommunications, smart cities, industrial IoT, and healthcare for efficient spectrum sensing and utilization.
Problems Solved
The technology addresses the challenges of spectrum congestion, interference, and inefficient spectrum utilization in cognitive IoT networks.
Benefits
- Improved spectrum efficiency.
- Enhanced network performance.
- Reduced interference.
- Optimal spectrum utilization.
Commercial Applications
- Telecommunications industry for better spectrum management.
- Smart cities for efficient IoT connectivity.
- Industrial IoT for improved automation and connectivity.
- Healthcare for reliable wireless communication.
Questions about Semi-Distributed Spectrum Sensing in Cognitive IoT Networks
How does the method of grouping secondary terminals into local clusters improve spectrum sensing efficiency?
Grouping secondary terminals into local clusters allows for more focused and coordinated spectrum sensing, leading to better utilization of available spectrum resources.
What are the potential implications of calculating and adjusting overlapping ranges between local clusters in cognitive IoT networks?
Calculating and adjusting overlapping ranges between local clusters can help in reducing interference and optimizing spectrum allocation, leading to improved network performance and reliability.
Original Abstract Submitted
disclosed are a semi-distributed spectrum sensing method in cognitive iot networks, and an apparatus thereof. the semi-distributed spectrum sensing method in cognitive iot networks includes: (a) grouping, based on local information of pre-shared secondary terminals, each secondary terminal into each local cluster; (b) generating overlapping point information by calculating an overlapping range between directional antenna beams of the respective secondary terminals in the each local cluster, and determining a beam determination binary indicator of the each secondary terminal by using the overlapping point information; and (c) calculating and adjusting the overlapping range between the respective local clusters.
