Predictive Beamforming Scheduling for Delayed CSI Reporting
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Solution Overview
Problem
The existing 5G NR system faces challenges with excessive resource overhead and time delays in CSI reporting, leading to inefficient beamforming due to the inability to reflect real-time channel states in wireless communication for self-driving vehicles like AGVs.
Innovation Solution
A scheduling method for beamforming that predicts future locations and channel states using machine learning models, enabling the determination of precoders to direct beams effectively without relying on immediate CSI reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CSI is reported through UE to gNB, then channel state information is obtained, but time delay occurs and real-time channel state cannot be reflected
Solution Approach 1:
The system performs preliminary actions by predicting future channel states and locations before actual communication occurs. The gNB uses machine learning models to forecast UE location and channel conditions at future time points, enabling proactive beamforming adjustments without waiting for delayed CSI reports.
Solution Approach 2:
A machine learning-based prediction system acts as an intermediary between the UE and gNB. Instead of directly relying on delayed CSI reports, the system introduces prediction models that estimate future channel states based on historical data, serving as a mediator that provides timely channel state information.
2Reliability
If pilot symbols are sent for CSI estimation, then channel state can be estimated, but radio resource overhead becomes excessive
Solution Approach 1:
The system extracts only the essential information needed for prediction (historical location and channel state data) rather than continuously transmitting full CSI reports. By using ML models to process this minimal extracted data, the system achieves accurate channel state estimation without the overhead of traditional pilot symbols and CSI reporting mechanisms.
Solution Approach 2:
Instead of using actual pilot symbols to estimate channel state, the system creates a copy or prediction of future channel states based on historical patterns. The ML models generate predicted channel state information that mirrors what would be obtained through traditional estimation, but without requiring additional radio resources for pilot transmission.
3Reliability
If beamforming direction is adjusted based on current CSI, then communication quality is optimized, but the system cannot adapt to moving UEs effectively
Solution Approach 1:
The system transitions from static beamforming based on current CSI to dynamic beamforming using machine learning predictions. The ML models continuously adapt to UE movement patterns by learning from historical location and channel state data, enabling the beamforming direction to dynamically adjust to future UE positions rather than relying on static current state information.
Solution Approach 2:
The system performs preliminary beamforming adjustments by predicting future UE locations and channel states. Instead of reacting to current channel conditions, the gNB proactively configures beamforming parameters based on predicted future states, allowing effective communication with moving UEs before they actually move to new positions.
Data Source
AI summary
A scheduling method for beamforming and a network entity are provided. In the method, a future location is predicted according to one or more past locations of a user equipment (UE). A precoder is determined according to the future location. A direction of a beam of a base station is determined according to the precoder. The past locations are locations of the UE at one or more past time points, and the future location is a location of the UE at a future time point. The precoder reflects a downlink channel state at the future time point. Accordingly, the communication quality can be improved.