Genetic Programming Evolved Algorithms for Base Station Power Control
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Solution Overview
Problem
Telecommunications networks face challenges in efficiently deploying self-x algorithms for base stations due to diverse environments, leading to performance degradation, as existing algorithms are often designed with unrealistic assumptions and lack specialization for specific conditions, making them less effective across varying operating environments.
Innovation Solution
A method using genetic programming to generate evolved algorithms that adjust base station transmission power based on specific operating conditions, utilizing a predetermined list of functions and terminals, and a fitness function to determine the suitability of algorithms in achieving desired operating characteristics, thereby selecting and refining algorithms suited to particular environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manually designed algorithms are applied across all network nodes, then implementation simplicity is improved, but performance is degraded due to lack of specialization for diverse operating environments
Solution Approach 1:
The patent applies local quality by generating specialized algorithms tailored to specific operating environments rather than using a single universal algorithm. Each network node receives an algorithm optimized for its local conditions (traffic patterns, geography, user density), thereby achieving both specialization for performance and systematic implementation through automated generation
Solution Approach 2:
The patent uses parameter changes by varying algorithm parameters based on operating environment characteristics. The system adjusts algorithm behavior according to environmental parameters such as traffic demand patterns, geographical features, and user density, allowing the same algorithm framework to adapt to diverse conditions while maintaining implementation consistency
2Reliability
If specialized algorithms are designed for each operating environment, then performance is improved, but device complexity and development cost increase
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate specialized algorithms for each operating environment without manual intervention. The automated algorithm generation process eliminates the need for engineers to manually design and provision algorithms for each environment, reducing complexity while maintaining performance specialization
Solution Approach 2:
The patent achieves universality through a single algorithm generation system that can produce multiple specialized algorithms for different environments. The same generation process and base framework serve multiple purposes across diverse operating conditions, reducing overall system complexity while providing environment-specific optimization
3Ease of manufacture
If manually designed algorithms with specific assumptions are used, then design simplicity is improved, but adaptability to real-world diverse environments deteriorates
Solution Approach 1:
The patent applies dynamics by transforming static manually designed algorithms into dynamic, environment-adaptive algorithms. The system continuously generates and updates algorithms based on current operating conditions, allowing network nodes to adapt to changing environments while maintaining simple automated generation processes
Solution Approach 2:
The patent uses feedback mechanisms where algorithm performance is evaluated against actual operating conditions, and this information feeds back into the algorithm generation process. This closed-loop system enables automatic adaptation to real-world environments while keeping the design process simple and automated
Data Source
AI summary
A telecommunications network node and methods are disclosed. The method is for generating, by genetic programming, evolved algorithms for adjusting base station transmission power to control coverage of a cell to assist in providing desired base station operating characteristics. The method comprises the steps of: generating, using predetermined functions and terminals defined in a functions and terminals list, a plurality of evolved algorithms each of which determine whether, for any particular base station operating conditions, to adjust said base station transmission power; determining a fitness level indicative of each evolved algorithm's ability to adjust base station transmission power to control coverage to achieve said desired base station operating characteristics under expected operating conditions; and iteratively performing said steps of generating and determining to generate further evolved algorithms using at least one of said evolved algorithms determined to have achieved a particular fitness level. In this way, it can be seen that algorithms can be assembled and then tested to see how suitable they are at controlling base station power to achieve particular operating characteristics under particular operating conditions. Those algorithms which are determined to be the best at achieving those characteristics under those particular operating conditions may then be used to generate further algorithms which, in turn, are also then assessed. Hence, those individual algorithms which are suited to the operating characteristics and the operating conditions can rapidly be generated thereby avoiding the need to manually design new algorithms which are specialised to particular environments.