Beamforming Control System for Cellular Network Optimization
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Solution Overview
Problem
Beamforming antennas in cellular networks face challenges in identifying the most appropriate beam due to uncertainties in wireless propagation environments and user locations and activities, which are difficult to predict accurately.
Innovation Solution
A closed-loop beamforming control method using real-time network performance and location data, employing a two-stage methodology: identifying promising beam candidates based on historical data and then using statistical performance testing to select the best beams through cycling operations and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beamforming antennas are used to improve signal strength and reduce interference, then wireless performance is improved, but the complexity of identifying the most appropriate beam increases due to uncertainties in propagation environment and user locations
Solution Approach 1:
The patent segments the beam identification process into two distinct stages: (1) candidate beam identification based on historical data, and (2) statistical performance testing to select the best beam. This segmentation reduces complexity by breaking down the uncertain decision-making process into manageable, systematic steps that can be executed independently.
Solution Approach 2:
The patent performs preliminary actions by using historical long-term data to pre-identify candidate beams before actual transmission. This preliminary identification narrows down the search space from all possible beams to a manageable set of candidates, reducing the real-time computational complexity while maintaining reliability.
2Measurement precision
If statistical performance testing is used to narrow down beam candidates, then measurement precision is improved, but the time required for beam selection increases due to cycling through multiple beam sets
Solution Approach 1:
The patent applies partial action by testing only a selected subset of beam candidates rather than evaluating all possible beams. The candidate identification stage filters out clearly suboptimal beams, so the statistical testing phase only needs to compare a limited number of promising candidates, reducing overall time while maintaining measurement precision.
Solution Approach 2:
The patent implements periodic action through cycling operations where beam sets are activated in alternating phases over multiple periods. This periodic testing allows statistical analysis to be performed systematically across different time slots, enabling precise measurement while distributing the time requirement across multiple cycles rather than requiring all measurements simultaneously.
3Productivity
If real-time beamforming adaptation is implemented to improve capacity and coverage, then network performance is enhanced, but the system complexity increases due to closed-loop control requirements
Solution Approach 1:
The patent implements feedback mechanisms where performance measurements from transmitted beams are collected and fed back into the statistical analysis system. This feedback loop enables the system to adaptively select the best beam based on actual measured performance rather than relying solely on predictions, enhancing network capacity while managing complexity through systematic feedback processing.
Solution Approach 2:
The system performs self-service by automatically identifying candidate beams, conducting statistical tests, and selecting the optimal beam without requiring manual intervention. The closed-loop control operates autonomously, with the system serving itself by using its own performance measurements to guide future beam selections, thereby managing complexity through automation rather than external control.
Data Source
AI summary
Beamforming antennas are used in cellular network to improve performance by enhancing signal strength or reducing interference for given users. The beam forming patterns are cyclic in nature; for example, the beams used on Monday's between 8 and 9 am have similarities to the beams of the previous Monday in the same time period. At least one method of testing and identifying the most appropriate beam for a given antenna at a given time from a list of promising beam candidates is provided. While the cellular network is providing services to its users, the network is simultaneously testing each of the promising beam candidates and extracting data. The extracted data is used to determine the best beams out of the list of beam candidates selected for use during a particular time period.