Wireless Receiver Channel Estimation Without Extra DMRS Overhead
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Solution Overview
Problem
In wireless communication systems like 5G NR, allocating more Demodulation Reference Signals (DMRS) for better channel estimation reduces resources for user data transmission, leading to reduced capacity and throughput.
Innovation Solution
A method involving multi-layer channel estimation using DMRS resource elements, synchronization, equalization, de-interleaving, and channel decoding, with optional AI deep-learning models, to improve channel estimation without increasing DMRS resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more DMRS resources are allocated for better channel estimation, then channel estimation precision is improved, but user data transmission capacity and throughput are reduced
Solution Approach 1:
The patent applies preliminary action by performing channel decoding attempts before finalizing the channel estimation process. The receiver first decodes codeblocks using initial channel estimates, then uses successfully decoded codeblocks to refine the channel estimation in subsequent iterations, improving overall estimation accuracy without requiring additional DMRS resources.
Solution Approach 2:
The patent implements self-service by having the successfully decoded codeblocks serve the dual purpose of data recovery and channel estimation refinement. The decoded codeblocks are reused to generate improved channel estimates, which then benefit subsequent decoding operations, creating a self-improving system that doesn't require external additional resources.
2Reliability
If more DMRS resources are allocated for better channel estimation, then signal recovery accuracy is improved, but available resources for user data transmission are reduced
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the channel estimation process based on decoding success rates. When decoding succeeds, the system transitions from using only DMRS for estimation to incorporating decoded codeblock information, effectively changing the estimation parameters without requiring additional physical resources.
Solution Approach 2:
The successfully decoded codeblocks serve themselves dual purposes: they are both the recovered data and the source of improved channel estimation information. This self-service approach allows the system to improve signal recovery accuracy while maintaining the same resource allocation for user data.
3Productivity
If iterative channel estimation using decoded codeblocks is performed, then throughput is improved, but processing complexity increases
Solution Approach 1:
The patent applies partial action by performing iterative channel estimation only when decoding succeeds partially (some codeblocks decoded successfully). The system doesn't always perform full iterative estimation, but conditionally applies it based on decoding outcomes, balancing complexity improvement benefits.
Solution Approach 2:
The patent implements feedback by using the results of channel decoding (successfully decoded codeblocks) to feed back into the channel estimation process. This feedback loop allows the system to refine estimates based on actual decoding performance, improving throughput while managing complexity through conditional execution.
Data Source
AI summary
In some implementations, a process implemented in a receiver device may include receiving a signal including one or more DMRS resource elements. The process may include synchronizing the received signal in the time domain, demodulating OFDM symbols of the signal, obtaining a first resource grid from the demodulated OFDM symbols, and performing multi-layer channel estimation utilizing the DMRS resource elements and the first resource grid to generate an estimate of a first channel matrix. Further, the process may include equalizing the first resource grid using the first channel matrix to obtain an equalized resource grid for each of a plurality of transmitted layers. In addition, the process may include de-interleaving, de-mapping, demodulation, and descrambling of the equalized resource grid for each of the plurality of transmitted layers to obtain LLR for a plurality of coded blocks, and channel decoding of the LLR to output a plurality of reassembled code-blocks.