HetNet Planning System for Small Cell Capacity Prediction
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Solution Overview
Problem
Conventional wireless technologies face challenges in meeting the exponential growth in demand for wireless access due to limitations in macrocell deployment and spectrum availability, leading to 'spectrum exhaust' and high costs.
Innovation Solution
A HetNet planning system predicts small cell capacity and coverage to facilitate macrocell offloading by selecting candidate locations for small cells using machine learning models, determining estimated traffic capacity and signal strengths, and optimizing hyperparameters for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If macrocell tower cost and size restrictions are considered, then deployment of additional macrocells is limited, but wireless coverage and capacity demand continues to grow
Solution Approach 1:
The patent segments the wireless network into macrocells and small cells (femtocells, picocells, microcells). Instead of deploying only macrocells, the system introduces smaller, lower-cost cell units that can be deployed in diverse locations to provide localized coverage, thereby meeting capacity demands without proportionally increasing macrocell deployment costs.
Solution Approach 2:
The patent transitions from a single-scale macrocell architecture to a multi-scale heterogeneous network architecture. By adding the dimension of small cell deployment at lower elevations and in varied locations, the system achieves enhanced capacity and coverage without being constrained by macrocell tower cost and size restrictions.
2Quantity of substance
If existing macrocell RF spectrum is used to accommodate increased demand, then spectrum utilization is maximized, but spectrum exhaust occurs reducing available bandwidth
Solution Approach 1:
The patent segments the spectrum utilization across multiple small cells operating in the same macrocell area. Each small cell uses a portion of the available spectrum, allowing the system to serve more users simultaneously without requiring additional macrocell spectrum. This segmentation of service areas enables better spectrum utilization and delays spectrum exhaust.
Solution Approach 2:
The patent introduces small cells as intermediary nodes between the macrocell and the end users. These small cells act as spectrum-sharing mediators that can dynamically allocate bandwidth resources, enabling efficient spectrum utilization and extending the available bandwidth capacity without requiring new macrocell spectrum acquisitions.
3Quantity of substance
If additional spectrum is procured to meet demand, then wireless capacity increases, but spectrum acquisition becomes undobrable due to lack of availability and cost
Solution Approach 1:
The patent enables the network to serve itself by deploying small cells that utilize existing macrocell spectrum resources. Instead of requiring external spectrum procurement, the system internally generates additional capacity through small cell deployment, effectively serving its own capacity needs without external spectrum acquisition.
Solution Approach 2:
The patent changes the deployment parameters from macrocell-only to heterogeneous network with small cells. By adjusting the network architecture parameters to include multiple cell types with different transmission powers and coverage areas, the system achieves increased capacity without changing the spectrum acquisition parameters, thereby avoiding costly spectrum procurement.
4Quantity of substance
If small cells are deployed to offload macrocell traffic, then wireless access capacity improves, but network complexity increases requiring prediction systems for capacity and coverage
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict small cell capacity and coverage before actual deployment. The system pre-calculates optimal locations, transmission powers, and spectrum allocations based on historical data and simulated conditions, thereby reducing operational complexity during deployment and enabling data-driven network planning.
Solution Approach 2:
The patent implements feedback mechanisms where the predicted capacity and coverage data from machine learning models are continuously refined using actual network performance measurements. This feedback loop allows the system to improve its prediction accuracy over time, reducing planning complexity as the models become more accurate and the network behavior more predictable.
Data Source
AI summary
Predicting small cell capacity and coverage to facilitate offloading of macrocell capacity is presented herein. A system selects a group of candidate locations for placement of respective small cells to facilitate offloading, via the respective small cells, of traffic from respective macrocells corresponding to the candidate locations—the respective small cells including first transmission powers that are less than second transmission powers of the respective macrocells. Further, for each candidate location of the group of candidate locations, the system determines an estimated amount of traffic capacity of a small cell of the respective small cells that has been presumed to have been placed at the candidate location, and determines estimated signal strengths of respective signals that have been predicted to have been received from the small cell at respective portions of a grid of a defined signal coverage area corresponding to the candidate location.