Cognitive Radio Spectrum Allocation via Matrix Aggregation
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Solution Overview
Problem
Existing cognitive radio network technologies face challenges in effectively utilizing discrete spectrum fragments and supporting bandwidth requirements for secondary users due to incomplete constraints and complex algorithms in multi-user spectrum allocation, leading to inefficiencies in spectrum usage.
Innovation Solution
A method and apparatus for allocating cognitive radio network spectrum based on aggregation, which involves obtaining information about unoccupied spectrum fragments and unit capacity, constructing a solution matrix, calculating a reference index, and allocating resources to maximize system capacity by minimizing the number of spectrum fragments used.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the Nash Bargaining method is used for multi-user spectrum allocation, then the allocation can consider multiple users' requirements, but the algorithm becomes complex and difficult to solve
Solution Approach 1:
The patent segments the spectrum resources into discrete fragments and formulates the allocation problem as a matrix optimization problem. By dividing the complex multi-user allocation into smaller sub-problems (matrix elements representing individual spectrum fragment assignments), the system maintains multi-user adaptability while reducing algorithmic complexity through structured mathematical formulation.
Solution Approach 2:
The patent transforms the spectrum allocation problem from a continuous resource distribution challenge into a discrete matrix optimization problem with defined parameters (solution matrix, reference index, resource allocation vector). This parameter transformation enables the use of efficient mathematical algorithms while maintaining the ability to handle multiple users' requirements.
2Adaptability or versatility
If discrete spectrum fragments are used for cognitive radio communication, then spectrum utilization flexibility is improved, but the system cannot meet bandwidth requirements for services
Solution Approach 1:
The patent merges multiple discrete spectrum fragments into aggregated bandwidth resources through matrix-based allocation. By combining adjacent or non-adjacent spectrum fragments that are not occupied by primary users, the system maintains flexibility in utilizing available spectrum while achieving sufficient bandwidth for service requirements through resource aggregation.
Solution Approach 2:
The patent introduces a new dimension of spectrum aggregation by combining spectrum fragments across different frequency dimensions. The matrix formulation enables allocation that spans multiple spectrum dimensions, transforming the limitation of discrete fragments into an opportunity for multi-dimensional resource consolidation to meet bandwidth demands.
3Quantity of substance
If spectrum aggregation technology is applied, then the bandwidth requirement is satisfied, but the system design becomes more complex
Solution Approach 1:
The patent implements a universal matrix-based allocation framework that handles both spectrum aggregation and distribution functions simultaneously. The same mathematical formulation (solution matrix construction, reference index calculation, resource allocation vector determination) serves multiple purposes: identifying available fragments, aggregating them into usable bandwidth, and allocating to multiple users, thereby reducing overall system design complexity.
Solution Approach 2:
The patent uses a standardized matrix model that can be replicated and applied to different spectrum allocation scenarios. The solution matrix structure, reference index calculation method, and resource allocation vector formulation serve as reusable templates that simplify system design by providing a proven, repeatable framework rather than requiring custom solutions for each aggregation scenario.
Data Source
AI summary
A method and an apparatus for allocating a cognitive radio network spectrum based on aggregation. The method includes: obtaining information about unoccupied spectrum fragments and unit capacity information of a secondary users on different spectrum fragments, and constructing a solution matrix; calculating a reference index corresponding to the solution matrix, determining a resource allocation vector according to the reference index, and allocating resources after selecting a resource allocation element from the resource allocation vector; and removing a resource allocation element that is used in the resource allocation from the solution matrix, and then constructing a new solution matrix to continue allocating resources until unoccupied spectrum resources corresponding to the information about unoccupied spectrum fragments are allocated completely.