Soft Syndrome Decoding Between Modem and FEC Hardware
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Solution Overview
Problem
Existing wireless communication systems face increased latency and decreased security due to the exchange of decoding information between components, particularly between modem and FEC performing hardware, which can compromise system throughput and security.
Innovation Solution
Implementing syndrome-based decoding techniques where a modem component generates log likelihood ratios (LLRs) and syndromes, which are then used by FEC performing components to generate an error vector, reducing the need for extensive information exchange and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decoding information is exchanged between modem and FEC performing hardware, then decoding can be performed, but latency increases and security decreases
Solution Approach 1:
The patent extracts only the essential syndrome information (compressed to N-K bits) from the full decoding data, separating it from the modem component and transmitting only this compressed form to the FEC hardware. This extraction principle reduces the volume of exchanged information while maintaining decoding capability, thereby reducing latency without compromising reliability.
Solution Approach 2:
The syndrome acts as an intermediary between the modem and FEC hardware. Instead of exchanging full decoding information, the syndrome serves as a compressed mediator that carries essential error detection and correction information, reducing communication overhead and latency while preserving decoding functionality.
2Reliability
If decoding information is exchanged between modem and FEC performing hardware, then decoding can be performed, but security decreases
Solution Approach 1:
The patent extracts only the syndrome (N-K bits) from the complete decoding information set, removing sensitive intermediate data that would be exposed in full information exchange. This selective extraction maintains decoding capability while minimizing security exposure by transmitting only the essential error correction information.
Solution Approach 2:
The patent transforms the decoding information from its original high-dimensional form (full LLR vectors and intermediate calculations) into a compressed parameter representation (syndrome vector). This parameter transformation reduces the information content to only what is necessary for error correction, thereby improving security while maintaining decoding reliability.
3Measurement precision
If extensive information exchange occurs between components, then decoding accuracy can be maintained, but system throughput decreases
Solution Approach 1:
The patent extracts the syndrome (N-K bits) as the essential decoding information, removing redundant data from the exchange process. This extraction maintains decoding accuracy by preserving the critical error detection and correction information while reducing the total data volume, thereby improving system throughput without sacrificing precision.
Solution Approach 2:
The patent uses partial action by transmitting only the syndrome (a subset of the full decoding information) rather than all possible decoding data. This partial information exchange is sufficient for maintaining decoding accuracy while significantly reducing communication overhead, thus improving overall system throughput.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. The described techniques may enable a receiving device to perform syndrome-based decoding. For example, a modem component of the receiving device may receive an encoded message and generate a syndrome and one or more log likelihood ratios (LLRs) associated with the encoded message, including LLR magnitudes and a sign vector associated with the one or more LLRs. The modem component may output the syndrome and LLR magnitudes to a forward error correction (FEC) performing component, which may generate an error vector using the syndrome and the LLR magnitudes. The modem component may obtain the error vector from the FEC and may generate an information vector (e.g., a decoded message, a codeword) based on the error vector and the sign vector.