Network Demand Packing by Transceiver Type for Route Efficiency
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Solution Overview
Problem
Existing communication network configurations inefficiently assign multiple demands with the same source and target nodes to separate routes, leading to wasted resources and reduced network capacity.
Innovation Solution
Packing sets of demands into packages associated with specific transceiver types using heuristic or dynamic programming methods, optimizing resource allocation by combining demands into a single route while adhering to hardware and software constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate resources are assigned to each demand with the same source and target nodes, then each demand can be satisfied independently, but network resources are used inefficiently
Solution Approach 1:
The patent combines multiple demands with the same source and target nodes into a single package that shares common route and resources. Instead of assigning separate resources to each demand, the system packages multiple demands together and assigns them a common route, thereby reducing resource duplication and improving network resource efficiency while still satisfying all individual demands.
2Loss of energy
If multiple demands are packed into a single package, then network resource efficiency improves, but the complexity of determining optimal packages increases
Solution Approach 1:
The patent segments the set of demands into multiple packages based on common characteristics such as source nodes, target nodes, or route compatibility. By dividing the overall packing problem into smaller sub-problems where demands are grouped into discrete packages, the system reduces the computational complexity of determining optimal packages while still achieving efficient resource utilization.
Solution Approach 2:
The patent changes the parameters used for package determination by introducing package cost metrics and using heuristic or dynamic programming methods. Instead of evaluating all possible package combinations, the system uses cost-based parameters and optimization algorithms to efficiently determine near-optimal packings, thereby reducing computational complexity while maintaining resource efficiency.
3Productivity
If heuristic or dynamic programming methods are used for packing demands, then optimal resource allocation is achieved, but computational time increases
Solution Approach 1:
The patent applies partial optimization by using heuristic methods that find good-enough solutions without guaranteeing absolute optimality. Instead of exhaustively searching all possible packings, the heuristic approaches perform partial evaluation of package combinations and select solutions that achieve sufficient resource allocation optimization within acceptable computational time limits.
Solution Approach 2:
The patent performs preliminary actions by pre-processing demands to identify common characteristics, grouping compatible demands together before applying optimization algorithms. This preliminary segmentation and characterization of demands reduces the search space for subsequent dynamic programming or heuristic methods, thereby achieving optimal or near-optimal resource allocation with reduced computational time.
Data Source
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AI summary
A set of demands on a communication network between a source node and a terminal node can be packed into a set of packages. Each package can be associated with a corresponding transceiver type. The packing of the set of demands into the set of packages can be performed using a heuristic method or a dynamic programming method. The packing of the set of demands can depend on the costs associated with each package. Multiple packings can be generated using different packing conditions. One of the multiple packings can then be selected. The communication network can be configured to satisfy each package using a transceiver of the corresponding transceiver type.