Beam Inference for Multiple TRP Communications
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently selecting optimal downlink beams for multiple transmit-receive points (TRPs) due to the need for UE to decode all possible beams, leading to increased processing power and time consumption.
Innovation Solution
Implementing a machine learning component in base stations and user equipment to infer the optimal downlink beam based on measurement reports and the relative location of TRPs, allowing for the selection of the most suitable beam for communication, thereby reducing unnecessary decoding of reference signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the UE decodes all possible downlink beams to ensure optimal communication, then communication reliability is improved, but processing power consumption and time consumption increase
Solution Approach 1:
The base station performs preliminary beam inference using machine learning models before the UE needs to decode reference signals. The model predicts the optimal beam based on historical measurement reports and TRP location data, allowing the UE to focus decoding resources only on the inferred beam rather than exhaustively checking all possible beams.
Solution Approach 2:
A machine learning component acts as an intermediary between the base station and the UE. This intermediary processes historical data and spatial information to generate beam predictions, enabling the system to select optimal beams without requiring the UE to perform exhaustive decoding of all possible beams.
2Reliability
If the UE decodes all possible downlink beams to ensure optimal communication, then communication reliability is improved, but time consumption increases
Solution Approach 1:
The base station performs preliminary beam inference using machine learning models before the UE needs to decode reference signals. The model predicts the optimal beam based on historical measurement reports and TRP location data, allowing the UE to focus decoding resources only on the inferred beam rather than exhaustively checking all possible beams.
Solution Approach 2:
The patent extracts the beam selection function from the UE's decoding process and relocates it to the base station's machine learning component. This separation allows the UE to skip decoding unnecessary beams, reducing time consumption while maintaining communication reliability through intelligent prediction.
3Productivity
If the base station transmits communications using multiple TRPs with beam selection, then communication efficiency is improved, but device complexity increases
Solution Approach 1:
The machine learning component serves multiple functions: it processes historical measurement reports, analyzes TRP location data, generates beam predictions, and guides beam selection for multiple TRPs. This multi-functional approach consolidates complexity into a single intelligent system rather than requiring separate complex mechanisms for each TRP.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station may receive, from a user equipment (UE), a measurement report associated with at least one downlink beam corresponding to a first transmit receive point (TRP). The base station may transmit, to the UE and using a second TRP, a communication using a selected downlink beam corresponding to the second TRP, wherein the selected downlink beam is selected based at least in part on a beam inference generated using a machine learning component, and wherein the beam inference is based at least in part on the measurement report and a location of the second TRP relative to a location of the first TRP. Numerous other aspects are described.