Beam Grid Optimization for Fair 5G Uplink Power Distribution
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Solution Overview
Problem
Existing wireless communication networks, particularly 5G NR networks, face challenges in optimizing beam configurations for improved performance, especially in Massive MIMO-based air interface technologies, which affect uplink throughput and fairness across client devices.
Innovation Solution
A network node device determines a grid of beams using a fairness function to maximize estimated received power, employing α-fairness functions and traffic density distribution, and applies a greedy or policy improvement algorithm to optimize beam selection for client devices, ensuring high received power and fair distribution across beams and devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If beam configuration is optimized to maximize received power for each client device, then uplink throughput is improved, but fairness across multiple client devices deteriorates
Solution Approach 1:
The patent changes the optimization parameter from individual client received power maximization to a fairness function that considers received power distribution across multiple clients. The fairness function uses parameters like α-weighting to balance between maximizing total throughput and ensuring minimum service levels for all clients, thereby resolving the contradiction between productivity and fairness.
Solution Approach 2:
The patent applies prior cushioning by pre-establishing fairness constraints and minimum received power thresholds before optimizing beam configurations. The fairness function is designed in advance to prevent extreme disparities in received power across clients, ensuring that optimization for individual clients does not compromise overall system fairness.
2Reliability
If a dense grid of beams is used to cover all spatial directions, then coverage and reliability are improved, but system complexity increases
Solution Approach 1:
The patent applies partial action by selecting only the necessary subset of beams from a potential dense grid, rather than activating all possible beams. The fairness-based optimization identifies and activates only those beams that contribute most to both coverage and fairness, reducing complexity while maintaining reliability.
Solution Approach 2:
The patent makes the beam grid configuration dynamic by adapting the selected beams based on current traffic density distribution and client locations. The fairness function guides dynamic selection of beam subsets, allowing the system to maintain adequate coverage with fewer active beams under varying conditions, thereby reducing complexity while preserving reliability.
3Reliability
If beam optimization considers traffic density distribution across multiple clients, then fairness is improved, but computational complexity increases
Solution Approach 1:
The patent segments the optimization problem by dividing the fairness function into manageable components that consider traffic density distribution across different spatial regions and client groups. This segmentation allows the complex optimization to be solved through iterative or distributed methods, reducing computational complexity while maintaining fairness optimization.
Data Source
AI summary
Beam configuration optimization is disclosed. A network node device may determine a grid of beams. The grid of beams is determined to maximize a fairness function of an estimated received power of at least two of the beams of the grid of beams that are optimal for each client device of a plurality of client devices.