Wireless Channel Estimation With Reduced-State Kalman Smoothing
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Solution Overview
Problem
Non-ideal channel responses in wireless communications systems degrade performance, and existing Kalman smoothers employing state augmentation impose high computational burdens and power consumption.
Innovation Solution
Implement a Kalman smoother using an extended state vector for channel estimation, reducing computational burden and power consumption by minimizing the number of elements in the state vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a Kalman smoother employing state augmentation is used for channel estimation, then non-causal channel estimation is achieved, but computational burden and power consumption increase significantly
Solution Approach 1:
The patent changes the parameter of state vector dimensionality from the conventional augmented state (size kp(D+1)) to an extended state with fewer elements. This parameter change reduces the computational complexity of matrix operations while maintaining the non-causal estimation capability through proper formulation of the state transition model.
Solution Approach 2:
The patent extracts and eliminates redundant elements from the state vector that are present in conventional state augmentation approaches. By taking out only the necessary state variables and formulating a minimal extended state vector, the computational burden is reduced while preserving the essential non-causal estimation functionality.
2Measurement precision
If a Kalman smoother employing state augmentation is used for channel estimation, then non-causal channel estimation is achieved, but power consumption increases significantly
Solution Approach 1:
The patent changes the parameter of state vector dimensionality to reduce the number of floating-point operations required for matrix multiplications and inversions in the Kalman smoother. This parameter change directly reduces power consumption in hardware implementations where computational operations consume significant energy.
Solution Approach 2:
The patent employs a simpler, more efficient state vector formulation that requires less computational resources. This approach uses 'cheaper' computational operations that consume less power, making the solution more suitable for energy-constrained wireless communication systems.
3Loss of time
If the state vector contains elements from multiple update intervals, then non-causal estimation is enabled, but the number of elements increases computational complexity
Solution Approach 1:
The patent optimizes the parameter of state vector composition by selecting only the essential elements needed for non-causal estimation. Instead of including all possible state elements from multiple update intervals, the extended state vector includes only the minimal necessary elements, reducing complexity while maintaining the time-loss compensation capability.
Solution Approach 2:
The patent segments the state vector into essential and non-essential components, keeping only the critical elements that enable non-causal estimation. This segmentation approach organizes the state information efficiently, reducing the overall state vector size while preserving the necessary temporal information for looking ahead D update intervals.
Data Source
AI summary
A system and a method are disclosed for channel estimation. In some embodiments, a method includes: calculating, by a receiver, a first channel estimate, at an nth point in time, the calculating including calculating an (n+D)th state vector, the (n+D)th state vector corresponding to a channel history at an (n+D)th point in time, D being a positive integer; performing signal processing of a received signal, based on the first channel estimate, to generate processed data; and transmitting the processed data to a data consumer, the (n+D)th state vector including elements from p update intervals, the first channel estimate at the nth point in time including an element for each of k subcarriers, k being a positive integer, and the (n+D)th state vector having fewer than kp(D+1) elements.