Transmitter Beamforming with Dynamic Training Stream Segmentation
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Solution Overview
Problem
In wireless communication systems, when the number of antennas at the transmitter (beamformer) is less than that at the receiver (beamformee), existing methods struggle to perform effective beamforming without increasing processing capability or circuit size, leading to inefficient channel estimation and matrix computation.
Innovation Solution
The system notifies the receiver of the channel estimation maximum dimension of the transmitter, allowing the receiver to suppress the number of streams in the training series to match the transmitter's processing capability, enabling the transmitter to construct a backward channel matrix and perform beamforming within its limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the receiver transmits training series with number of streams equal to its number of antennas (M), then the channel estimation completeness is improved, but the transmitter's processing load and circuit size increase beyond its capability
Solution Approach 1:
The patent segments the training series transmission by dividing it into two phases: first transmitting training series with M streams (full dimension), then transmitting additional training series with (N-M) streams (supplementary dimension). This segmentation allows the transmitter to process channel estimation in manageable parts rather than attempting to handle all M streams simultaneously, reducing peak processing load while maintaining complete channel estimation.
Solution Approach 2:
The patent applies preliminary action by having the receiver first transmit training series with M streams before the actual data transmission. This preliminary transmission allows the transmitter to pre-compute the channel estimation matrix for the M dimensions, and then use this pre-computed information to efficiently process the remaining (N-M) dimensions during the actual transmission, reducing real-time processing requirements.
2Reliability
If the transmitter performs beamforming using full channel matrix (MxN), then the beamforming quality is improved, but the circuit size and processing capability requirements increase
Solution Approach 1:
The patent segments the beamforming computation into two parts: first computing the beamforming matrix for the MxM channel subset, then computing the beamforming matrix for the Mx(N-M) channel subset. This segmentation allows the transmitter to perform simpler matrix operations sequentially rather than computing the full MxN matrix simultaneously, reducing circuit complexity while maintaining beamforming quality.
Solution Approach 2:
The patent applies dynamics by making the training series dimension dynamic - adjusting the number of streams transmitted based on the relationship between M and N. When M < N, the system dynamically increases the training series dimension to MxN; when M >= N, it uses the conventional MxM approach. This dynamic adaptation optimizes beamforming quality while matching the actual processing capabilities of the transmitter.
3Adaptability or versatility
If the number of antennas at transmitter is less than receiver (N < M), then the system adapts to asymmetric configuration, but the conventional beamforming methods become inefficient
Solution Approach 1:
The patent applies dynamics by making the training series dimension dynamic - adjusting the number of streams transmitted based on the relationship between M and N. When M < N, the system dynamically increases the training series dimension to MxN; when M >= N, it uses the conventional MxM approach. This dynamic adaptation optimizes beamforming quality while matching the actual processing capabilities of the transmitter.
Solution Approach 2:
The patent changes the parameter of training series dimension from the conventional fixed MxM to a variable dimension MxK where K can be greater than M when N > M. This parameter change allows the system to adapt to asymmetric antenna configurations by adjusting the training series dimension to match the actual channel dimensions, improving beamforming efficiency in asymmetric scenarios.
Data Source
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AI summary
A wireless communication system is disclosed. The system performs data transmission from a first terminal including N antennas to a second terminal including M antennas using spatially multiplexed streams (N and M are integers larger than or equal to 2).