Cooperative Spectrum Sensing Sensor Partitioning
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Solution Overview
Problem
Current cooperative spectrum sensing methods are inefficient due to the high resource strain and unreliable sensor selection, which affects the detection of spectrum opportunities and introduces interference, while existing research has not adequately addressed the optimal selection of sensors for reliable sensing with minimal resource consumption.
Innovation Solution
A method is introduced to partition a candidate set of sensors into active and passive sets using a cost formula and optimization procedure, where sensors are selected based on their distance to the active set, ensuring a balanced reliability and resource efficiency in cooperative spectrum sensing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all candidate sensors are used in cooperative spectrum sensing, then sensing reliability is improved, but system resource strain increases
Solution Approach 1:
The patent divides the candidate sensors into two distinct sets: an active set for current sensing operations and a passive set for future potential use. This segmentation allows the system to use only a subset of sensors (active set) for current operations, reducing immediate resource strain while maintaining sensing reliability through the availability of additional sensors (passive set) when needed. The ordered statistics approach enables reliable sensing with fewer active sensors by optimally selecting which sensors to activate based on their statistical characteristics.
2Ease of operation
If sensors are selected randomly for cooperative spectrum sensing, then implementation simplicity is maintained, but sensing reliability deteriorates
Solution Approach 1:
The patent changes the selection criterion from random selection to ordered statistics-based selection. Instead of randomly choosing sensors, the system ranks candidate sensors based on their statistical parameters (such as signal strength, noise characteristics, or detection performance metrics) and selects the optimal subset for the active set. This parameter-based selection improves sensing reliability by ensuring that the most capable sensors are actively used, while still maintaining relatively simple implementation through automated ranking and selection algorithms.
3Measurement precision
If the number of active sensors is increased, then primary user detection accuracy is improved, but false alarm rate increases
Solution Approach 1:
The patent implements a feedback mechanism through the ordered statistics framework, where the system continuously evaluates the performance and characteristics of sensors in the active set and adjusts the composition of the active and passive sets accordingly. By monitoring detection outcomes and sensor performance metrics, the system can identify when additional sensors are causing increased false alarms and dynamically reconfigure the sets to maintain optimal detection accuracy while minimizing false positives. This feedback-driven adjustment resolves the contradiction between detection accuracy and false alarm rate.
Data Source
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AI summary
The invention presents methods and corresponding device relating to cooperative spectrum sensing. First a candidate set of sensors that can participate in an occasion of cooperative spectrum sensing is obtained. A cost formula for calculating a cost associated with using sensors from the candidate set in the cooperative spectrum sensing is then defined (75; 107, 97). The candidate set is partitioned (29; 14, 145) into an active set and a passive set. The active set contains any sensor (s) from the candidate that is to participate in the cooperative spectrum sensing. The passive set includes any sensor (s) that is not to participate in the cooperative spectrum sensing. The partitioning of the candidate set is done by applying an optimization procedure (79, 81, 83; 111, 113, 115) which performs a constrained optimization of the cost in accordance with the defined cost formula.