Balanced Trees for Real-Time Channel and Spectrum Gap Mapping
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Solution Overview
Problem
Traditional methods for managing optical spectrum occupancy face inefficiencies due to fixed bandwidth granularity, leading to incoherent data structures, high computational overhead, and slow access times, especially when reallocating channels to minimize fragmentation.
Innovation Solution
A method using balanced tree data structures to represent active channels and spectrum gaps, allowing real-time updates and efficient management, with logarithmic time complexity for node access, thereby optimizing spectrum utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed bandwidth granularity is used to manage optical spectrum occupancy, then the management process is simplified, but spectral efficiency deteriorates due to fragmentation and inability to reallocate channels flexibly
Solution Approach 1:
The patent implements dynamic spectrum management by allowing channels to be reallocated, merged, or split based on demand. The system maintains a data structure that tracks both channels and gaps dynamically, enabling flexible reallocation without fixed granularity constraints. This resolves the contradiction by making the management system adaptive rather than static.
Solution Approach 2:
The patent segments the spectrum into both channels and gaps as distinct manageable units. By representing both occupied and unoccupied portions as first-class entities in the data structure, the system can perform operations on gaps independently, enabling efficient consolidation and reallocation to improve spectral efficiency while maintaining manageable complexity.
2Ease of manufacture
If traditional data structures are used to represent spectrum occupancy, then implementation is straightforward, but processing performance deteriorates due to high computational overhead and slow access times
Solution Approach 1:
The patent introduces a new dimensional approach by maintaining parallel data structures that simultaneously track both channels and gaps. This dual-dimensional representation allows the system to perform operations in logarithmic time by navigating both structures, significantly improving processing performance while keeping implementation manageable through structured organization.
3Productivity
If channels are reallocated to minimize fragmentation, then spectral efficiency improves, but computational overhead increases due to complex reallocation operations
Solution Approach 1:
The system implements feedback mechanisms where the data structures continuously reflect the actual spectrum occupancy state. When reallocation operations are performed, the feedback from the updated data structures enables the system to identify optimal reallocation paths and minimize fragmentation efficiently, balancing computational overhead with spectral efficiency improvements.
Data Source
AI summary
In some implementations, a device may map active channels within a band spectrum into a first balanced tree data structure wherein each node of the first balanced tree data structure represents a respective channel. The device may identify spectrum gaps within the band spectrum based on the nodes of the first balanced tree data structure and mapping the spectrum gaps into a second balanced tree data structure wherein each node of the second balanced tree data structure represents a respective spectrum gap. The device may update the first balanced tree data structure and the second balanced tree data structure in response to a change detected in an active channel by adding, deleting, or modifying the nodes of the first and second balanced tree data structures to reflect the change in real-time occupancy of the band spectrum.