Neural Network Beam Alignment for mmWave
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Solution Overview
Problem
Existing beam-alignment techniques in mmWave communications face challenges due to high propagation losses and susceptibility to blockages, leading to complex and overhead-intensive beam sweeping processes.
Innovation Solution
A method using a neural network to process input data including user equipment location, number of user equipments, and desired receive signal strength, generating beam-pair indices and reducing search complexity through adaptive beam alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive beam sweeping is performed to establish good beam alignment, then beam alignment accuracy is improved, but search complexity and overhead increase significantly
Solution Approach 1:
The system performs preliminary channel estimation before beam alignment to obtain AoA-AoD pairs, which are then used to pre-determine candidate beam pairs. This preliminary action reduces the search space from all possible beam pairs to a subset of promising candidates, significantly lowering search complexity while maintaining alignment accuracy.
Solution Approach 2:
The beam alignment process is segmented into multiple stages: channel estimation phase to obtain AoA-AoD pairs, candidate beam pair selection phase, and final beam alignment phase. This segmentation allows the system to process information in manageable steps rather than performing exhaustive search all at once.
2Loss of information
If channel estimation is performed prior to beam alignment, then AoA-AoD information is obtained, but beamforming gain is not utilized effectively
Solution Approach 1:
The system uses feedback from channel estimation results (AoA-AoD pairs) to guide subsequent beam alignment operations. The estimated channel information feeds into the candidate beam pair selection process, creating a feedback loop that ensures beamforming gain is applied in the most effective directions identified by channel estimation.
Solution Approach 2:
Channel estimation is performed as a preliminary action to gather AoA-AoD information before beam alignment, but the system then uses this information to guide beamforming operations rather than completing alignment without utilizing the gained insights.
3Device complexity
If hierarchical beam alignment with multi-resolution codebooks is used, then search complexity is reduced, but search complexity is not reduced significantly
Solution Approach 1:
The system changes the parameter used for beam selection from purely codebook-based hierarchical search to channel estimation-based AoA-AoD pair selection. This parameter change allows the system to leverage actual channel conditions rather than relying on geometric hierarchical decomposition, achieving better efficiency.
4Device complexity
If blind beam steering based on location information is used, then search complexity is reduced, but reliability decreases when LOS path is blocked
Solution Approach 1:
The system introduces channel estimation as an intermediary process between location-based beam steering and final beam alignment. This intermediary provides additional AoA-AoD information that complements location data, allowing the system to maintain reliability even when LOS paths are blocked by using NLOS channel information.
Solution Approach 2:
The system dynamically adapts beam selection based on actual channel conditions observed through estimation rather than relying solely on static location information. This dynamic approach allows the system to respond to changing environmental conditions such as blocked LOS paths.
Data Source
AI summary
Some embodiments of a method may include: obtaining input data comprising a user equipment location, a number of user equipments, and a desired receive signal strength; processing the input data with a neural network having weights determined from a training phase to generate a set of one or more beam-pair indices; performing a beam search over at least a subset of the set of beam-pair indices; and receiving at least one beam-pair index from a vehicle that provides a desired received signal strength.