Cell Parameter Optimization via Heuristic Network Analysis

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Solution Overview

Problem

Current cell planning in mobile phone networks is sub-optimal due to reliance on pre-deployment measurements and lack of real-time feedback, leading to inefficient resource utilization and increased handovers, which affect network performance.

Innovation Solution

A heuristic approach that collects and analyzes statistics from mobile device usage to predict future locations and adjust cell coverage parameters such as handover thresholds and transmit power, using neural networks and statistical analysis to optimize network configuration dynamically.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If traditional a priori measurements are used for cell planning, then deployment is simplified, but resource utilization becomes sub-optimal

Engineering Contradiction:

Improvedeployment simplicityVSAvoidresource utilization efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system performs preliminary cell planning using a priori measurements to enable simplified deployment, then subsequently refines the planning based on collected statistics and feedback to optimize resource utilization. This two-stage approach allows both ease of deployment and optimal resource utilization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system collects statistics from mobile devices and network performance data, then uses this feedback to iteratively refine cell planning parameters and adjust coverage areas. This closed-loop feedback mechanism transforms static a priori planning into dynamic optimization, resolving the contradiction between deployment simplicity and resource efficiency.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If cell parameters are adjusted offline with human intervention, then control precision is maintained, but response time increases

Engineering Contradiction:

Improveparameter adjustment precisionVSAvoidresponse time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables self-service by automatically collecting statistics, analyzing performance data, and adjusting cell parameters without requiring continuous human intervention. The automated system maintains control precision through algorithmic decision-making while dramatically reducing response time by operating in near real-time.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes cell parameters such as coverage areas, handover thresholds, and transmit power levels based on collected statistics and predictive analytics. This automated parameter adjustment maintains precision through data-driven decisions while eliminating the time delays associated with manual intervention.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If tracking area size is reduced to improve load balancing, then resource distribution improves, but handover frequency increases

Engineering Contradiction:

Improveload balancing efficiencyVSAvoidhandover frequency

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The system dynamically adjusts tracking area sizes based on collected statistics and predictive analytics, allowing areas to expand or contract according to actual network conditions and traffic patterns. This dynamic approach optimizes load balancing while minimizing unnecessary handovers by adapting to changing conditions rather than using static size reductions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses predictive analytics to anticipate traffic patterns and proactively adjusts tracking area configurations before peak loads occur. This preliminary action allows for smoother load distribution and reduces sudden handover spikes by preparing the network in advance for expected changes.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If predictive analytics are implemented to reduce handovers, then network stability improves, but system complexity increases

Engineering Contradiction:

Improvenetwork stabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The cloud coordination server performs multiple functions including data collection, statistical analysis, predictive analytics, and parameter optimization within a single unified system. This multi-functional approach improves network stability through predictive capabilities while containing complexity by consolidating functions rather than adding separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11229087B2Parameter optimization and event prediction based on cell heuristics

Publication Date: 2022.01.18 PARALLEL WIRELESS INC

AI summary

A heuristic approach to configuration and/or planning for wireless networks is disclosed herein. In one embodiment, statistics relating to mobile device cell usage are collected and monitored. The statistics may include UE measurements (RSRP/RSRQ), UE location, number of connection requests, duration of connectivity, average traffic load associated with the users, channel utilization, and other statistics. Based on statistical analysis of the data collected, neural network analysis, data fitting, or other analysis, adjustments to cell coverage parameters such as handover thresholds, inactivity timer values, contention window size, inter-frame duration, transmit power, DRX cycle duration, or other parameters may be identified.