AI Beam Management for Large-Antenna Beam Alignment
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Solution Overview
Problem
High-frequency radio communication signals suffer from high propagation losses, necessitating beamforming techniques to compensate, but existing beam management operations are inefficient, especially in scenarios with large antenna arrays.
Innovation Solution
Implementing artificial intelligence and machine learning (AI/ML) based beam management operations to optimize beamforming weights and patterns dynamically, using reinforcement learning models for improved alignment and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Strength
If beamforming techniques are employed to compensate for propagation losses in high-frequency radio communication, then signal strength is improved, but device complexity increases due to the need for beam management operations with large antenna arrays
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting beamforming weights and beam management parameters based on channel conditions and device locations. The system changes parameters such as beamforming weight vectors, reference signal configurations, and beam alignment parameters to optimize signal strength while managing complexity through adaptive parameter tuning rather than fixed complex structures
Solution Approach 2:
The patent implements self-service through autonomous beam management where devices automatically perform beam alignment, selection, and optimization without manual intervention. The system uses self-organizing mechanisms where devices independently determine optimal beamforming parameters based on measured channel state information, reducing the operational complexity burden on network controllers
2Area of stationary object
If traditional beam management operations are used with large antenna arrays, then coverage area is improved, but operation time increases due to inefficient beam alignment procedures
Solution Approach 1:
The patent applies preliminary action by pre-configuring beamforming weight vectors and reference signal resources before actual beam management operations. The system prepares multiple candidate beamforming vectors and associated reference signals in advance, allowing devices to quickly select and activate appropriate beams without performing time-consuming real-time calculations during critical communication phases
Solution Approach 2:
The patent implements periodic action through scheduled beam management operations where beam alignment and optimization are performed at regular intervals rather than continuously. The system uses periodic reference signal transmissions and measurements to maintain beam alignment, reducing operational time by avoiding constant beam management while still providing timely updates to accommodate device mobility and channel changes
3Reliability
If beamforming weights are adjusted to improve signal alignment, then communication reliability is improved, but computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the beamforming weight adjustment process into discrete, manageable segments corresponding to different device groups, frequency resources, and spatial domains. The system processes beamforming parameters in segmented blocks rather than as a monolithic computation, allowing parallel processing and reducing peak computational power requirements while maintaining alignment accuracy across all devices
Solution Approach 2:
The patent implements this principle by using simplified, approximate beamforming weight calculations that are computationally inexpensive and can be quickly discarded and regenerated. The system employs lightweight algorithms that provide sufficient alignment accuracy for practical purposes without requiring complex, power-intensive computations, effectively using disposable computational approximations rather than persistent complex calculations
Data Source
AI summary
Devices and methods for performing a beam management operation are provided in this disclosure. A radio communication device may include a transceiver that is configured to transmit a plurality of reference signals, each reference signal being beamformed based on a set of beamforming weights that is different from a set of beamforming weights of at least one other reference signal, and receive a plurality of measurement results representing measurements for at least some of the plurality of reference signals. The radio communication device may further include a processor that is configured to provide the plurality of measurement results to a machine learning model configured to determine a parameter for a beam management operation using a predefined codebook and perform the beam management operation according to the predefined codebook based on the determined parameter.