Blind Beamforming Weight Estimation for Wireless Systems
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Solution Overview
Problem
Computing beamforming weight vectors in wireless communication systems is computationally intensive, especially with increasing numbers of antenna elements, and the use of pilot signals introduces overhead, reducing system data capacity.
Innovation Solution
A low complexity blind beamforming generation process that computes a receive beamforming weight vector by combining correlated elements of a covariance matrix, avoiding computationally intensive singular value decomposition, and using this vector for both receive and transmit signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional beamforming weight vector computation methods are used, then accurate beamforming weights can be obtained, but computational complexity increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for beamforming weight computation by using blind estimation techniques that identify spatial signatures directly from received signals without requiring full covariance matrix decomposition. This extraction approach obtains sufficient beamforming weights while avoiding computationally intensive operations.
Solution Approach 2:
The patent changes the computational parameters by transitioning from exact matrix decomposition methods to iterative blind estimation algorithms. This parameter change allows the system to converge to accurate beamforming weights through successive approximations, reducing overall computational burden while maintaining precision.
2Productivity
If the number of antenna elements is increased, then system capacity and signal processing capability are improved, but computational complexity of beamforming weight computation increases
Solution Approach 1:
The patent segments the beamforming computation process into independent per-antenna-element operations. By processing each antenna element's spatial signature separately through blind estimation, the system scales linearly with the number of antennas rather than quadratically, enabling support for larger antenna arrays without exponential complexity increases.
Solution Approach 2:
The blind estimation algorithm allows the received signals themselves to provide the necessary information for weight computation without requiring external pilot signals. This self-service approach eliminates overhead and reduces the computational burden of processing additional pilot data, allowing the system to efficiently handle increased antenna counts.
3Measurement precision
If pilot or preamble signals are used for channel estimation, then accurate channel conditions can be obtained, but system overhead increases and data capacity is reduced
Solution Approach 1:
The patent enables the received data signals to serve dual purposes: both as information carriers and as sources for spatial signature estimation. The blind beamforming algorithm extracts channel and spatial information directly from the data signals themselves, eliminating the need for separate pilot signals and maximizing system data capacity.
Solution Approach 2:
The patent makes the data signals multi-functional by using them simultaneously for information transmission and for beamforming weight estimation. This universal use of data signals eliminates the need for dedicated pilot signals, reducing overhead and increasing effective data capacity while maintaining estimation accuracy.
Data Source
AI summary
Techniques are provided to compute beamforming weights at a communication device, e.g., a first communication device, based on transmissions received at a plurality of antennas from another communication device, e.g., a second communication device. A plurality of transmissions are received at the plurality of antennas of the first communication device from the second communication device. A covariance matrix associated with reception of a plurality of transmissions at the plurality of antennas of the first communication device is computed. Corresponding elements (e.g., all the rows or all the columns) of the covariance matrix are combined to produce a weighted channel signature vector. A receive beamforming weight vector is computed from the weighted channel signature vector.