Blind Signal Estimation in SIMO Systems
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Solution Overview
Problem
Conventional methods for blind signal estimation in single-input multiple-output (SIMO) finite impulse response (FIR) systems require training sequences, which are inefficient in terms of bandwidth, power, and channel throughput, and are not applicable in asynchronous wireless networks.
Innovation Solution
The method involves receiving observed signals by multiple receiver antennas, forming a data matrix, computing its singular value decomposition, generating a parameter matrix, forming a Toeplitz signal matrix, and directly estimating the input signal without channel estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training sequences are used for channel estimation, then channel information can be obtained for signal estimation, but bandwidth efficiency decreases and power consumption increases
Solution Approach 1:
The patent extracts and eliminates the training sequence requirement from the channel estimation process. By using blind channel estimation techniques that exploit the structure of the transmitted data itself (such as cyclic prefixes in OFDM or correlations in the received signal), the method obtains channel information without removing or adding separate training elements, thus maintaining full bandwidth efficiency while achieving estimation accuracy.
Solution Approach 2:
The received signal itself serves as the source for channel estimation without requiring external training sequences. The method uses properties inherent in the transmitted data and channel response (such as autocorrelation, cyclic structures, or subspace relationships) to enable the signal to estimate the channel independently, eliminating the need for dedicated training resources.
2Measurement precision
If training sequences are transmitted for channel estimation, then channel information is available, but power consumption increases
Solution Approach 1:
The patent removes the power-consuming training sequence transmission from the system. Blind channel estimation methods extract channel information directly from the received signal properties (such as signal subspace, correlation structures, or statistical characteristics), eliminating the need for additional power expenditure on training signal generation and transmission while maintaining estimation capability.
Solution Approach 2:
The channel estimation process becomes self-powered by utilizing the energy already present in the received signal. Instead of requiring separate training transmissions that consume additional power, the method leverages the inherent structures and statistics of the received signal itself to perform estimation, making the system energy-efficient.
3Measurement precision
If channel estimation is performed before signal estimation, then signal can be estimated using obtained channel information, but latency increases
Solution Approach 1:
The patent merges the channel estimation and signal estimation operations into a single unified process. By formulating a joint estimation framework that simultaneously estimates both channel parameters and transmitted signals from the received signal (using methods such as maximum likelihood, subspace techniques, or iterative algorithms), the system eliminates the sequential execution overhead and reduces total processing latency while maintaining or improving estimation accuracy.
4Measurement precision
If training sequences are used, then channel information can be obtained, but channel throughput decreases
Solution Approach 1:
The patent extracts channel information directly from the data-bearing signal without removing training sequences from the transmission stream. Blind channel estimation techniques process the received signal containing only user data, exploiting signal structures (such as cyclic prefixes, pilot patterns embedded in data, or statistical properties) to achieve channel identification while maintaining 100% of the channel capacity for productive data transmission.
Data Source
AI summary
A system, method, and non-transitory computer readable medium that perform blind signal estimation for single-input multiple-output systems. The method can include receiving, by the two or more receiver antennas of the receiver, an observed signal comprising the input signal and an additive noise term. The method can then form a data matrix using the observed signals from the two or more receiver antennas. The method can also include computing a singular value decomposition of the data matrix. The singular value decomposition can then be used to generate a parameter matrix. The method can then form a Toeplitz signal matrix using the parameter matrix. The method can estimating the input signal using the Toeplitz signal matrix.