Semi-distributed Spectrum Sensing in Cognitive IoT Networks
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Solution Overview
Problem
Conventional centralized spectrum sensing technologies in cognitive IoT networks are vulnerable to system incapacitation due to high load and security attacks, and they do not efficiently utilize frequency resources.
Innovation Solution
A semi-distributed spectrum sensing method that clusters secondary terminals into local clusters, calculates overlapping ranges between directional antenna beams, and adjusts beam determination indicators to optimize spectrum sensing, allowing for inter-cluster mediation and flexibility in leader replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized spectrum sensing technology is used, then sensing performance is improved, but system reliability deteriorates due to high load and security attacks
Solution Approach 1:
The patent divides the centralized sensing system into multiple distributed secondary terminals that each perform local spectrum sensing independently. This segmentation eliminates the single point of failure in centralized systems while maintaining sensing effectiveness through cooperative decision-making among distributed terminals.
Solution Approach 2:
The patent introduces a coordinator terminal as an intermediary that collects sensing results from multiple secondary terminals and facilitates cooperative spectrum sensing. This intermediary structure distributes the computational load and avoids the vulnerabilities of a fully centralized system while preserving sensing performance.
2Reliability
If distributed spectrum sensing is used, then system reliability is improved, but sensing performance deteriorates compared to centralized sensing
Solution Approach 1:
The patent merges the sensing capabilities of multiple distributed secondary terminals through cooperative spectrum sensing. By combining local sensing results and coordinating beamforming strategies, the distributed system achieves sensing performance comparable to centralized approaches while maintaining the reliability benefits of distribution.
Solution Approach 2:
The patent implements feedback mechanisms where secondary terminals exchange sensing information and adjust their beamforming strategies based on received feedback. This iterative coordination enables distributed terminals to achieve centralized-level sensing performance through distributed decision-making.
3Productivity
If directional antenna beams are used for spectrum sensing, then frequency resource efficiency is improved, but device complexity increases due to beam overlap calculations
Solution Approach 1:
The patent performs preliminary calculations of beam overlapping ranges and determines beam determination binary indicators in advance before actual spectrum sensing operations. This pre-computation reduces the complexity of real-time sensing operations while maintaining the frequency resource efficiency benefits of directional beamforming.
Solution Approach 2:
The patent transforms the complex continuous beamforming parameter optimization problem into a discrete binary indicator selection problem. By changing the parameter space from continuous beam directions to discrete beam selection indicators, the system achieves frequency resource efficiency with reduced computational complexity.
Data Source
AI summary
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.