Uplink Power Control via Evolutionary Gene Pool Optimization
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Solution Overview
Problem
Current uplink power control solutions in wireless communication systems face challenges in achieving globally optimal settings while minimizing computational complexity and network interference, with existing methods either failing to account for neighboring base stations' effects or consuming excessive resources.
Innovation Solution
A method and apparatus that utilize evolutionary algorithms to optimize uplink power control settings by generating and evolving 'genes' representing power control solutions, evaluating their fitness, and broadcasting the optimized global solution to base stations, thereby determining a globally optimized power control solution without performing an exhaustive search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a centralized exhaustive search approach is used to compute globally optimized uplink power control solutions, then the quality of power control solution is improved, but the computational complexity and network resource consumption increase significantly
Solution Approach 1:
The patent segments the network into multiple clusters, each managed by a separate controller that independently computes power control solutions for its cluster. This division reduces the computational burden on any single controller compared to a centralized exhaustive search, while still achieving globally optimized solutions through coordinated cluster management.
Solution Approach 2:
The patent implements distributed control where each base station or cluster controller computes local power control solutions based on local conditions and feedback. This local computation approach reduces overall computational complexity while maintaining solution quality through iterative optimization and coordination between clusters.
2Device complexity
If each base station computes its own uplink power control solution unilaterally, then the computational complexity is reduced, but the solution quality deteriorates due to failure to account for neighboring base stations
Solution Approach 1:
The patent implements feedback mechanisms where base stations and controllers exchange information about power control parameters, interference levels, and performance metrics. This feedback enables iterative optimization where each base station adjusts its power control settings based on responses from neighboring base stations, improving solution quality without requiring complex centralized computation.
Solution Approach 2:
The patent merges local computations at base stations with coordinated optimization at cluster level. Each base station performs local power control computations, while cluster controllers coordinate between neighboring base stations to account for inter-cell interference, combining the benefits of distributed simplicity with centralized optimization where needed.
Data Source
AI summary
A method for optimizing uplink power control settings in a wireless network, the method comprising generating a first gene pool comprising a set of parent genes, wherein each parent gene comprises a set of first generation power control solutions for a set of base stations in the wireless network. The method may further include performing natural selection on the first gene pool to generate a second gene pool comprising selected ones of the set of parent genes, wherein the selected parent genes are chosen by probabilistically selecting some of the parent genes based on fitness values assigned to the parent genes. The method may further include evolving the second gene pool into a descendent gene, wherein the descendent gene comprises a set of local power control solutions for the set of base station in the wireless network.