Power-law model fitting for mobile network capacity expansion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing traffic blocking theory, based on Erlang's model, is inadequate for accurately determining the capacity expansion needs in modern data service-focused communications networks, especially in LTE and 5G systems, due to the unpredictable and explosive growth of data services which do not follow Poisson and exponential distributions.
Innovation Solution
A mobile communications system expansion method that collects data characteristic information, fits it using a power-law model, and determines user data transmission rates to assess whether to expand the capacity of base stations or RRU units, considering latency satisfaction degrees to reflect user experience and adjust resource allocation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Erlang's traffic blocking theory is applied to capacity expansion, then capacity expansion decisions can be made using simple Poisson and exponential distribution models, but the accuracy of capacity expansion decisions deteriorates due to unpredictable and explosive growth of data services
Solution Approach 1:
The patent changes the fundamental parameters of the traffic model from Poisson and exponential distributions to power-law distribution, which better captures the unpredictable and explosive growth characteristics of data services. This parameter change resolves the contradiction by maintaining model simplicity while improving accuracy through more realistic traffic pattern representation
Solution Approach 2:
The patent introduces dynamic user data transmission rates that vary over time and follow power-law distribution, replacing the static assumptions of traditional models. This allows the system to adapt to changing traffic patterns and explosive growth, improving measurement precision while keeping the decision-making process relatively simple through standardized evaluation metrics
2Adaptability or versatility
If conventional voice service models are used for data service networks, then the existing Erlang table and traffic blocking theory can be applied directly, but the reliability of capacity expansion decisions deteriorates due to fundamental differences in service characteristics
Solution Approach 1:
The patent fundamentally changes the distribution parameters from Poisson/exponential (voice services) to power-law (data services), creating a new model that reflects the actual characteristics of data services including unpredictable growth and long-duration connections. This resolves the reliability issue while maintaining adaptability through a systematic approach that can be applied across different network scenarios
Solution Approach 2:
The patent segments the capacity expansion decision process into distinct components: data characteristic information collection, power-law distribution fitting, user data transmission rate determination, and latency satisfaction degree evaluation. This segmentation allows the system to handle data service complexities reliably while maintaining ease of implementation through modular, reusable evaluation steps
3Measurement precision
If power-law model fitting is performed on data characteristic information, then the accuracy of user data transmission rate determination is improved, but the complexity of the capacity expansion evaluation process increases
Solution Approach 1:
The patent enables the system to automatically collect data characteristic information, fit power-law distributions, and determine user data transmission rates without requiring complex manual analysis. The standardized evaluation metrics and automated fitting process reduce operational complexity while maintaining high measurement precision through systematic data-driven approaches
Solution Approach 2:
The patent incorporates latency satisfaction degree as a feedback mechanism that evaluates whether the determined user data transmission rates meet user experience requirements. This feedback loop allows the system to automatically adjust and validate capacity expansion decisions, reducing complexity by providing clear acceptance criteria and iterative optimization
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A mobile communications system expansion method, a related device, a storage medium, and a program product are disclosed, to relatively accurately expand a capacity of a current data service-focused communications network. In embodiments of this application, a distribution status of collected data characteristic information is fitted by using a power-law model, to obtain a user data transmission rate in a coverage area of a transmit unit; a theoretical quantity of users supported by a mobile communications system is determined based on the user data transmission rate and an obtained reference system transmission rate of the mobile communications system; and whether to expand a capacity of the transmit unit is determined based on the theoretical quantity of users and an actual quantity of users that access the transmit unit in the preset time segment. A distribution of a quantity of arrived data packets of a data service and a distribution of lengths of the data packets accord with the power-law model. Therefore, whether a capacity of a current data service-focused communications network needs to be expanded can be determined more accurately by fitting the distribution status of the data characteristic information by using the power-law model.