WTRU mmWave Cell Discovery Using AI Location Prediction
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Solution Overview
Problem
Millimeter wave (mmWave) cell discovery in emerging mobile networks faces challenges due to high signal directivity, short coverage range, and sensitivity to environmental variations, making beam alignment crucial for successful base station discovery.
Innovation Solution
A wireless transmit/receive unit (WTRU) performs cell selection and beam association using a two-phase procedure, leveraging contextual information like location to refine beam association, and employing AI techniques to predict WTRU-base station association with wide beams, followed by conventional beam sweeping for finer tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive periodic beam sweeping is used for cell discovery, then beam alignment can be achieved, but latency increases significantly
Solution Approach 1:
The patent applies preliminary action by using AI/ML models to predict suitable beam pairs before actual beam sweeping occurs. The model is trained on historical data including WTRU location, mobility patterns, and channel conditions to pre-identify promising beam pairs, allowing the system to skip exhaustive sweeping and directly test predicted beams, thereby reducing latency while maintaining alignment success
Solution Approach 2:
The patent segments the beam sweeping process into two phases: an AI-driven prediction phase that identifies candidate beam pairs, and a refined beam sweeping phase that only tests the predicted candidates. This segmentation eliminates the need to sweep all possible beams, reducing the time-consuming exhaustive search while ensuring reliable beam alignment through targeted testing of predicted beams
2Loss of time
If hierarchical beam sweeping is used to reduce latency, then cell discovery speed improves, but beam-pairing quality may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where the AI/ML model continuously learns from measurement results of beam pairs. After initial beam sweeping and measurements, the system feeds back the actual channel conditions and beam performance data to retrain and refine the model, improving its prediction accuracy for subsequent cell discovery events, thus maintaining high beam-pairing quality while keeping latency low
Solution Approach 2:
The patent applies dynamics by making the beam sweeping process adaptive rather than static. The AI model dynamically adjusts which beam pairs to test based on real-time inputs such as WTRU location, mobility state, and environmental conditions. This dynamic approach ensures that the system focuses resources on the most promising beam pairs for current conditions, maintaining high pairing quality while reducing unnecessary sweeping latency
3Loss of time
If AI techniques are used to predict WTRU-base station association, then latency is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an AI/ML model as an intermediary component that sits between the WTRU and the beam sweeping mechanism. This intermediary processes inputs like location and mobility data to generate predictions about suitable beam pairs, effectively decoupling the complexity of AI processing from the core beam management functions. The model can be implemented as a separate module or even in the cloud, reducing the complexity burden on the wireless device itself while still achieving low-latency cell discovery
4Length of stationary object
If mmWave systems use directional antennas for short coverage compensation, then coverage range is extended, but cell discovery difficulty increases
Solution Approach 1:
The patent applies preliminary action by using AI predictions to identify the most likely serving cell and its beam directions before actual cell discovery begins. This pre-identification narrows down the search space from all possible directional beams to a small set of predicted candidate beams, making cell discovery feasible despite the directional nature of mmWave antennas and their limited coverage ranges
Data Source
AI summary
A wireless transmit/receive unit (WTRU) may perform cell selection and/or beam association as described herein. The WTRU may use contextual information, such as a location, to enable cell selection and/or beam association. The WTRU may be configured with a resource on which to report WTRU contextual information. The WTRU may receive, in response to the contextual information, assistance information associated with one or more cells for enabling cell selection and/or beam association. For example, the WTRU may receive a defined subset of measurement resources in response to the transmission of the location on the resource. The WTRU may determine a base station (BS) and/or a beam pair (BP) based on the assistance information. The WTRU may perform a transmission to the BS using at least one beam of the BP.