Channel Coefficient Determination via Mixed-Norm Convex Optimization
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Solution Overview
Problem
Conventional training-based methods for determining channel state information (CSI) in multipath channels are ill-suited for exploiting the inherently low-dimensional nature of sparse or approximately sparse multipath channels, leading to inefficiencies in power and spectral efficiency in communication systems.
Innovation Solution
A device and method using a mixed-norm convex optimization process, specifically the Dantzig selector or Lasso estimator, to determine channel coefficients from sampled multipath signals, effectively characterizing the channel associated with the multipath signal, thereby improving CSI estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional training-based methods (exhaustive probing with linear least squares or non-linear parametric estimators) are used to determine channel state information, then the channel can be characterized, but the method is ill-suited for exploiting the low-dimensional nature of sparse multipath channels, leading to reduced spectral efficiency and power efficiency
Solution Approach 1:
The patent changes the estimation approach from conventional linear least squares or non-linear parametric estimators to a mixed-norm convex optimization estimator. This parameter change in the estimation method enables exploitation of the sparse multipath channel structure, achieving accurate CSI estimation while improving spectral efficiency by reducing the training signal overhead required.
Solution Approach 2:
Instead of using exhaustive probing methods that assume full channel dimensionality, the patent inverts the approach by assuming sparsity and using convex optimization to recover the channel from reduced measurements. This inversion allows the system to exploit the low-dimensional nature of sparse channels rather than fighting against it.
2Reliability
If conventional training-based methods are used to learn channel state information, then channel characterization is achieved, but the method requires excessive training signals and processing, reducing power efficiency
Solution Approach 1:
The patent changes the estimation methodology to mixed-norm convex optimization, which requires fewer training signals and less processing power compared to conventional methods. This parameter change maintains reliable channel state information accuracy while significantly improving power efficiency by reducing the computational burden and training overhead.
Solution Approach 2:
The patent extracts only the essential channel information by exploiting sparsity, rather than estimating all possible channel parameters. This extraction approach using convex optimization reduces the amount of data that needs to be processed and transmitted, thereby improving power efficiency while maintaining the necessary reliability for coherent communication.
3Measurement precision
If exhaustive probing with linear least squares is used to estimate channel coefficients, then channel characterization is obtained, but the method fails to exploit the sparse structure, leading to increased complexity and reduced efficiency
Solution Approach 1:
The patent changes the estimation parameter from linear least squares to mixed-norm convex optimization. This change exploits the sparse structure of multipath channels by incorporating sparsity constraints into the optimization problem, reducing processing complexity while maintaining or improving channel coefficient determination accuracy.
Solution Approach 2:
The patent applies local quality by focusing computational resources on estimating only the dominant sparse path components rather than all possible channel parameters. The mixed-norm convex optimization method identifies and estimates only the significant multipath components, reducing device complexity while maintaining measurement precision for the critical channel characteristics.
Data Source
AI summary
A device, method, and computer-readable medium are provided. The device includes, but is not limited to, an antenna configured to receive a multipath signal and a processor operably coupled to the antenna to receive the multipath signal. The processor is configured to determine a training signal transmitted from a second device to create the received multipath signal; sample the received multipath signal; and determine channel coefficients based on the sampled multipath signal and the determined training signal using a mixed-norm convex optimization process, wherein the channel coefficients characterize a channel associated with the multipath signal.