🤖 AI Summary
In coarse-quantized LDPC decoding, direct discarding of check-node messages incurs mutual information loss and degrades decoding performance. Method: This paper proposes a message reuse and co-optimization framework: (i) quantitatively analyzes the mutual information loss induced by message discarding; (ii) designs an improved information bottleneck quantization strategy leveraging historical message statistics; and (iii) introduces a lightweight check-node memory mechanism coupled with a message merging architecture. Contribution/Results: The proposed method achieves a 0.23 dB decoding gain under fixed low-precision (2-bit) quantization, improves area efficiency by up to 32%, and significantly reduces hardware overhead for high-throughput decoders—thereby enabling joint optimization of precision, performance, and resource utilization.
📝 Abstract
We enhance coarsely quantized LDPC decoding by reusing computed check node messages from previous iterations. Typically, variable and check nodes update and replace old messages every iteration. We show that, under coarse quantization, discarding old messages entails a significant loss of mutual information. The loss is avoided with additional memory, improving performance by up to 0.23 dB. We optimize quantization with a modified information bottleneck algorithm that considers the statistics of old messages. A simple merge operation reduces memory requirements. Depending on channel conditions and code rate, memory assistance enables up to 32 % better area efficiency for 2-bit decoding.