NN-Generated Interleavers for Variable-Entropy Wireless Control Bits
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Solution Overview
Problem
Conventional interleavers in wireless communication systems assume constant source entropy for control bits, failing to account for varying entropies due to UE patterns, leading to suboptimal decoding performance and increased block error rates.
Innovation Solution
Implement a neural network (NN)-assisted generation of interleavers and deinterleavers, using shared NN models at network nodes and UEs to adaptively generate orthogonal binary matrices based on channel and connection status, enabling efficient interleaving and deinterleaving of control messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conventional interleaver with fixed permutation order is used, then the device complexity is low, but the decoding performance deteriorates when control bit entropies vary
Solution Approach 1:
The interleaver is transformed from a static fixed permutation to a dynamic adaptive structure. The neural network generates different interleaver permutations based on input features including channel conditions, message length, and traffic patterns. This allows the interleaver to adapt its permutation order to match the actual entropy characteristics of control bits in different scenarios, improving decoding performance while maintaining reasonable complexity through efficient neural network inference
Solution Approach 2:
The interleaver parameters (permutation order) are changed dynamically based on varying conditions. Instead of using a single fixed permutation, the system adjusts the interleaver parameters according to channel status, message length, and traffic patterns. The neural network outputs different permutation sequences that optimize the mapping between control bits and polar code positions for specific entropy distributions, resolving the contradiction between fixed simplicity and adaptive performance
2Reliability
If the interleaver assumes constant source entropy for all control bits, then the design is simplified, but the block error rate increases when entropies actually vary
Solution Approach 1:
The interleaver design transitions from a uniform approach (same permutation for all bits) to a differentiated approach where each control bit position is mapped according to its specific entropy characteristics. The neural network learns to identify which bits have high entropy and which have low entropy, then applies locally optimized permutation patterns that match each bit's entropy profile. This local adaptation significantly reduces block error rates by ensuring high-entropy bits receive appropriate protection
Solution Approach 2:
The system performs preliminary analysis of control bit entropy characteristics before generating the interleaver permutation. The neural network is trained offline to recognize entropy patterns in different traffic scenarios and channel conditions, storing pre-computed optimal permutations. During actual operation, the system quickly selects the appropriate pre-computed interleaver based on current conditions, avoiding real-time complex calculations while achieving adaptive optimization
3Reliability
If a fixed interleaver configuration is used for all code rates, then the implementation is simple, but the performance deteriorates for varying code rates
Solution Approach 1:
The interleaver configuration is made dynamic with respect to code rate. The neural network generates different interleaver permutations tailored to specific code rates (e.g., 1/2, 2/3, 3/4). When the code rate changes, the system switches to the corresponding pre-computed interleaver or generates a new one through neural network inference. This dynamic adaptation ensures optimal performance across varying code rates while maintaining implementation simplicity through efficient selection or generation mechanisms
Data Source
AI summary
The present disclosure relates to a technical solution that improves the reliability of communications over a wireless communication channel by replacing a conventional interleaver (e.g., random interleaver) with a Neural Network (NN)-generated interleaver. For this purpose, a well-trained NN is used, which is configured to receive a UE connection status and a channel status as input data and outputs the interleaver in the form of an orthogonal binary matrix. The NN is shared by a UE and a network node. The network node may use the interleaver to interleave a set of bits in a downlink control message before the downlink control message is encoded, e.g., with an error correcting code, such as a polar code. The UE may generate and transpose the interleaver to obtain a deinterleaver to be applied to the downlink control message after its decoding (e.g., polar decoding).