Beam Pair Selection in Millimeter-Wave MIMO Systems
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Solution Overview
Problem
Existing methods for finding accurate beam alignment in Massive Millimeter-Wave MIMO systems require exhaustive searches, resulting in prohibitively high signalling overhead due to the large size of transmitter and receiver codebooks.
Innovation Solution
A method that sounds only a subset of beam pairs, uses a learning model to measure and predict the Signal to Noise Ratio (SNR) of unsounded pairs, and selects the beam pair with the maximum SNR to form the channel, thereby reducing computational complexity and signalling overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search is used to find the best beam pair, then the beam alignment accuracy is improved, but the signalling overhead increases prohibitively
Solution Approach 1:
The patent segments the exhaustive search process into two parts: a training phase where a subset of beam pairs is sounded to build a predictive model, and a prediction phase where the model estimates SNR for unsounded beam pairs. This segmentation reduces signalling overhead while maintaining alignment accuracy by avoiding the need to sound all possible beam pairs.
Solution Approach 2:
The patent creates a predictive model (copy) that replicates the relationship between beam pairs and SNR based on training data. This model copy allows the system to predict SNR values for unsounded beam pairs without actually measuring them, significantly reducing the number of sounding operations needed while maintaining accurate beam pair selection.
2Productivity
If the size of transmitter and receiver codebooks is increased to support massive antennas, then the system capacity is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary action by training a predictive model on a subset of beam pairs before the actual beam selection process. This preliminary training phase captures the essential relationships in the channel, allowing the system to quickly predict SNR for all codebook combinations without performing exhaustive measurements, thus reducing computational complexity during operation.
Solution Approach 2:
The patent changes the approach from directly measuring all SNR values to using a predictive model that estimates SNR based on learned parameters from training data. This parameter-based prediction approach reduces the computational burden of evaluating large codebooks while maintaining the ability to identify the best beam pair for massive MIMO systems.
Data Source
AI summary
A method for selecting beams in a Millimeter-Wave MIMO system, using at least a transmitter antennas codebook indexing a plurality of beam patterns and a receiver antennas codebook indexing a plurality of beam patterns, at least one beam pattern being selected among said transmitter antennas codebook and at least one beam pattern being selected among said receiver antennas codebook, said selected beam patterns forming one or more pair of beams, said pair forming a channel for transmitting information between the transmitter and receiver antennas, with at least the following steps: sounding only a subset of beam pair,measuring a transmission quality parameter of said subset beam pairs to be indexed in an matric, using a learning model to select the beam pair having the maximum value of transmission quality parameter to form the channel for transmitting information between the transmitter and receiver antennas.