ACK/NACK Sequence Compression for 6G Feedback Overhead
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Solution Overview
Problem
In ultra-large-scale 6G machine communication, existing ACK/NACK feedback methods result in significant signaling overhead due to the large number of users, making current 5G mechanisms unsuitable for 6G contention-based communication, and existing 5G NR methods fail to effectively reduce the large ACK/NACK codebook sequences overhead.
Innovation Solution
Implementing a data transmission method that utilizes machine learning models to compress ACK/NACK sequences, allowing terminals and network devices to autonomously or based on network indication select and apply machine learning models to reduce feedback overhead by converting ACK/NACK sequences into sparse indication sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bitmap ACK/NACK mechanism is used for PUSCH in 6G ultra-large-scale machine communication, then all users can be acknowledged, but the downlink feedback overhead becomes large
Solution Approach 1:
The patent extracts only the essential information from the full bitmap ACK/NACK sequence by identifying and transmitting only the positions of '1' bits (ACK signals) rather than transmitting the entire bitmap. This extraction approach reduces feedback overhead while maintaining reliability, as the receiver can reconstruct the full acknowledgment status from the extracted position information.
Solution Approach 2:
The patent transforms the feedback mechanism from a one-dimensional bitmap sequence to a two-dimensional structure by associating ACK positions with UE identifiers. This dimensional change allows the system to represent acknowledgment information more efficiently by leveraging the existing UE ID dimension, thereby reducing the required feedback bits while maintaining complete acknowledgment coverage.
2Adaptability or versatility
If ACK/NACK bit + UE ID feedback method is used, then user identification is achieved, but the signaling overhead remains large due to large number of users
Solution Approach 1:
The patent merges the UE identifier information with the ACK/NACK feedback mechanism by transmitting the UE ID only when a ACK signal is detected. This combining approach allows the system to maintain user identification capability while reducing overhead, as the UE ID is transmitted only for active users with ACK signals rather than for all users systematically.
Solution Approach 2:
The patent applies partial action by transmitting UE ID information only for users who require it (those with ACK signals) rather than for all users. This selective transmission approach reduces the overall signaling overhead while maintaining sufficient user identification capability for the subset of users who need retransmission coordination.
3Adaptability or versatility
If 5G ACK/NACK feedback mechanism is used for 6G PDSCH, then backward compatibility is maintained, but the uplink feedback overhead becomes large
Solution Approach 1:
The patent introduces dynamic adaptation by allowing the feedback mechanism to switch between traditional 5G-compatible methods and the new compressed bitmap method based on system conditions. This dynamic approach maintains backward compatibility for legacy systems while enabling overhead reduction for 6G scenarios, as the system can select the appropriate feedback method based on the number of users and network conditions.
4Ease of operation
If scheduling-based communication method is used, then resource allocation is controlled, but the access signaling overhead becomes large in ultra-large-scale machine communication
Solution Approach 1:
The patent introduces compressed bitmap feedback as an intermediary mechanism between the scheduler and the terminals. This intermediary compresses the scheduling information and acknowledgment feedback into a more efficient format, reducing the signaling overhead required for resource allocation control while maintaining the scheduler's ability to manage ultra-large-scale machine communication effectively.
Data Source
AI summary
A data transmission method, a data transmission apparatus, and a data transmission device are provided, where the method on the terminal side includes: obtaining, by the terminal, an ACK/NACK sequence to be fed back; selecting, by the terminal, a machine learning model applied to the ACK/NACK sequence; compressing, by the terminal, the ACK/NACK sequence through the machine learning model to obtain an indication sequence; and transmitting, by the terminal, the indication sequence to the network device.