Mixed Integer Programming for Heterogeneous Network Resource Placement
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Solution Overview
Problem
The challenge lies in optimally planning and placing radio access network nodes in large-scale heterogeneous networks, particularly for 5G and 6G, due to limited resources, rising demands, and interference among cells, with conventional methodologies being time-consuming and sub-optimal.
Innovation Solution
The use of a mixed integer programming (MIP) model to determine optimal placement of resources, considering constraints such as resources, demand, and interference, and providing a design template for Cloud Radio Access Network (cRAN) and 5G mmWave deployment, which can be automated and deployed nationwide.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methodologies are used for node placement planning, then implementation simplicity is maintained, but planning time increases and placement optimality deteriorates
Solution Approach 1:
The patent replaces conventional manual or heuristic methodologies with a mathematical programming-based automated system. The mixed integer programming model mathematically formulates node placement optimization, substituting traditional mechanical planning processes with computational algorithms that solve for optimal placements efficiently.
Solution Approach 2:
The system changes the approach from qualitative, experience-based placement decisions to quantitative, parameter-driven optimization. By defining objective functions and constraints in terms of measurable parameters (coverage area, capacity, interference levels), the system transforms the planning problem into a solvable mathematical optimization task.
2Productivity
If more resources are placed in the network to meet rising demands, then network capacity and coverage improve, but costs increase and interference among cells worsens
Solution Approach 1:
The patent applies local quality by allowing different regions of the network to have different node densities and configurations based on local demand and interference conditions. The optimization model determines specific placement locations where nodes provide maximum benefit without causing excessive interference to neighboring cells, creating non-uniform but optimized resource distribution.
Solution Approach 2:
The system incorporates feedback mechanisms by evaluating the impact of each potential node placement on overall network performance metrics including interference levels. The objective function and constraints continuously assess how adding a node affects capacity, coverage, and interference, adjusting placements to maintain optimal balance.
3Ease of manufacture
If node placement is optimized to minimize costs, then resource efficiency improves, but placement precision requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating optimal node placements using the mixed integer programming model before actual deployment. The system solves the optimization problem in advance, determining the precise locations that minimize costs while meeting performance requirements, thereby guiding subsequent deployment activities with predetermined optimal solutions.
Solution Approach 2:
The system segments the large-scale network planning problem into manageable optimization subproblems. By dividing the network into regions or time periods, or by breaking down the objective function into separate components (coverage, capacity, interference), the model makes the optimization tractable while maintaining overall precision.
Data Source
AI summary
Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.