Channel Impulse Response Estimation with CMIR Timing Refinement
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Solution Overview
Problem
Existing channel impulse response estimation methods in wireless communications systems, such as those used in GSM networks, face challenges in accurately estimating the channel impulse response and timing offset, leading to suboptimal interference cancellation and increased error rates due to assumptions about perfectly known timing offsets, which are not always valid.
Innovation Solution
A method that uses cross-correlation based timing and channel impulse response estimation followed by iterative Constant Modulus Interference Removal (CMIR) iterations, reducing computation and maintaining performance in both interference and Additive White Gaussian Noise (AWGN) cases, while allowing for joint timing and channel impulse response estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard least-squares estimate and static channel profile are used for interference cancellation, then the system complexity is reduced, but the measurement precision of channel impulse response deteriorates in interference and noise dominant cases
Solution Approach 1:
The patent transforms the static channel profile into a dynamic one by making the channel impulse response time-varying through iterative updates. The channel profile is continuously refined across multiple iterations using constant modulus interference removal, allowing the system to adapt to changing interference conditions while maintaining manageable complexity through structured iteration.
Solution Approach 2:
The patent implements feedback by using the detected interfering signal to update the channel impulse response estimate, which then feeds back into the interference cancellation process. This closed-loop approach allows the system to progressively improve estimation accuracy by incorporating information from previous iterations, directly addressing the measurement precision issue.
2Measurement precision
If iterative constant modulus interference removal is applied to improve channel impulse response estimation, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent applies partial action by performing a limited number of iterations (typically 2-5) rather than continuing until complete convergence. This provides sufficient improvement in measurement precision while avoiding the excessive computational complexity that would result from full convergence, achieving an optimal trade-off point.
Solution Approach 2:
The patent segments the complex estimation problem into distinct iterative steps: initial least-squares estimation, followed by constant modulus interference removal iterations, and final channel profile update. This segmentation allows each step to be computationally efficient while the sequence collectively achieves high precision.
3Device complexity
If timing offset is assumed to be perfectly known, then the device complexity is reduced, but the measurement precision of joint timing and channel estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by performing a rough timing offset estimation using cross-correlation before the main iterative channel estimation process. This preliminary timing estimate is then refined within the iterative framework, allowing the system to achieve high precision without the full complexity of joint optimization from scratch.
Data Source
AI summary
A receiver and associated method estimates a channel impulse response of the communications signal. A communications signal is received as a burst of transmitted symbols, including a known training sequence. The joint estimation of timing offset and initial channel impulse response is determined based on the cross-correlations of the known transmitted symbols and received communications signal. A constant modulus interference removal iteration is applied to improve the initial channel impulse estimation.