Hierarchical Deep Channel Coding for Short-Block Low-Latency Decoding
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Solution Overview
Problem
Current channel coding techniques, such as LDPC and turbo codes, face challenges in meeting the low-latency requirements of 6G cellular data networks for augmented reality and ultra-reliable low-latency communication (URLLC) applications, especially with short block lengths, and are inefficient in handling non-AWGN channels, leading to suboptimal performance and redundant feedback mechanisms.
Innovation Solution
A hierarchical channel coding approach using a concatenation of two autoencoders with feedback from layer-0 to layer-1, incorporating learned recurrent neural network codes and conditional feedback for iterative error refinement, which reduces redundant feedback and improves decoding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional channel coding techniques (LDPC, turbo codes) are used, then reliability is improved, but latency increases and block error rate performance deteriorates for short block lengths
Solution Approach 1:
The patent divides the channel coding task into two hierarchical layers: layer-0 autoencoder for initial encoding/decoding and layer-1 autoencoder for iterative error refinement. This segmentation allows the system to process short block lengths efficiently at layer-0 while providing optional refinement at layer-1, thereby reducing latency compared to traditional single-stage codes while maintaining reliability.
Solution Approach 2:
The patent implements dynamic feedback mechanisms where the layer-1 autoencoder selectively refines errors based on layer-0 decoding outcomes. The feedback loop adapts the coding strategy based on channel conditions and error patterns, enabling optimal performance for short block lengths without incurring fixed latency overhead of traditional approaches.
2Reliability
If traditional channel coding techniques are used, then reliability is improved, but device complexity increases due to redundant feedback mechanisms
Solution Approach 1:
The patent segments the feedback mechanism into hierarchical layers where layer-0 provides primary encoding and layer-1 provides selective refinement. This segmentation eliminates redundant feedback by only activating layer-1 when layer-0 decoding is insufficient, thereby reducing overall system complexity while maintaining decoding performance.
Solution Approach 2:
The patent implements intelligent feedback control where the layer-1 autoencoder is activated based on layer-0 decoding performance metrics. This feedback mechanism avoids unnecessary processing by only engaging additional complexity when errors are detected, optimizing the trade-off between reliability and device complexity.
3Reliability
If hierarchical deep channel coding is used, then block error rate performance is improved for short block lengths, but device complexity increases
Solution Approach 1:
The patent divides the coding structure into two manageable layers with distinct functions: layer-0 for primary encoding and layer-1 for error refinement. This segmentation makes the complex hierarchical structure more tractable and implementable while achieving superior block error rate performance for short block lengths compared to traditional single-layer codes.
Solution Approach 2:
The patent implements a nested hierarchical structure where layer-1 is embedded within the overall coding framework and only activates when needed. This nesting approach allows the system to maintain simple operation for most cases (using only layer-0) while providing enhanced protection when required, balancing complexity and performance.
4Reliability
If iterative error refinement with feedback is used, then decoding performance is improved, but loss of time increases due to multiple transmissions
Solution Approach 1:
The patent performs preliminary error correction at layer-0 before potential layer-1 refinement, eliminating many errors in advance. This preliminary action reduces the need for iterative refinement and multiple transmissions, thereby improving decoding performance while minimizing additional transmission time.
Solution Approach 2:
The patent implements feedback control where layer-1 iterative refinement is selectively activated based on layer-0 decoding outcomes. This feedback mechanism avoids unnecessary iterative processing and retransmissions by only engaging additional refinement when errors are detected, optimizing the balance between decoding performance and transmission time.
Data Source
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AI summary
Systems, methods, apparatuses, and computer program products for hierarchical coding. One method may include encoding, by a layer-0 autoencoder, bit sequence x into a layer-0 codeword. The method may further include transmitting, by the layer-0 autoencoder, the layer-0 codeword over a channel, and decoding, by the layer-0 decoder, a received layer-0 codeword.