PUSCH Parallel Encoding Architecture for Lower LTE Uplink Latency
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Solution Overview
Problem
Current 3GPP LTE PUSCH encoding methods experience latency due to sequential processing of channel quality information (CQI), uplink shared channel (UL-SCH), rank indication (RI), and acknowledgement (ACK) data, which hinders efficient data transmission in wireless communication systems.
Innovation Solution
A parallel encoding approach for PUSCH data using dedicated encoding units for CQI, UL-SCH, RI, and ACK data, combined with a channel interleaving unit that employs dual-port RAMs and a handshake protocol to interleave and store data in specific buffer areas, optimizing data processing and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If sequential encoding processing is used for CQI, UL-SCH, RI, and ACK data, then device complexity is reduced, but latency increases and productivity decreases
Solution Approach 1:
The encoder is divided into four independent encoding units (CQI encoding unit, UL-SCH encoding unit, RI encoding unit, ACK encoding unit), each capable of processing different data types in parallel. This segmentation allows simultaneous encoding operations without increasing overall system complexity, as each unit operates independently with dedicated resources.
Solution Approach 2:
The channel interleaving unit serves as a universal component that receives and interleaves output from all four encoding units. This multi-functional unit consolidates the complexity of managing multiple encoding streams, providing a single point of integration that handles diverse data types (CQI, UL-SCH, RI, ACK) through a unified interleaving process.
2Productivity
If sequential encoding processing is used for CQI, UL-SCH, RI, and ACK data, then device complexity is reduced, but productivity decreases
Solution Approach 1:
The encoder is divided into four independent encoding units (CQI encoding unit, UL-SCH encoding unit, RI encoding unit, ACK encoding unit), each capable of processing different data types in parallel. This segmentation allows simultaneous encoding operations without increasing overall system complexity, as each unit operates independently with dedicated resources.
Solution Approach 2:
All four encoding units operate continuously and simultaneously, ensuring that no processing stage waits for another to complete. The parallel architecture maintains continuous useful action across all data types (CQI, UL-SCH, RI, ACK), maximizing throughput without requiring complex coordination mechanisms.
3Productivity
If parallel encoding operations are implemented, then latency is reduced and productivity is enhanced, but device complexity increases
Solution Approach 1:
The encoder is divided into four independent encoding units (CQI encoding unit, UL-SCH encoding unit, RI encoding unit, ACK encoding unit), each capable of processing different data types in parallel. This segmentation allows simultaneous encoding operations without increasing overall system complexity, as each unit operates independently with dedicated resources.
Solution Approach 2:
The channel interleaving unit acts as an intermediary that receives encoded output from all four parallel encoding units and performs interleaving to combine them. This mediator component simplifies the integration of parallel operations by providing a single consolidation point, managing the complexity of coordinating multiple parallel streams through a unified interleaving process.
Data Source
AI summary
Provided are a Physical Uplink Shared CHannel (PUSCH) encoder and an encoding method of the same. The PUSCH encoder includes a plurality of encoding units and a channel interleaving unit interleaving the data transferred from the plurality of encoding units. Herein, the respective encoding units perform encoding operations on different data. The PUSCH encoder is configured to perform the respective encoding operations in parallel. In accordance with the PUSCH encoder and the encoding method thereof, the encoding operations are performed on different data in parallel to reduce latency, leading to an increase in processing speed of a codec system. Furthermore, the increase in processing speed of the codec system results in the improvement of performance and throughput per unit hour in a data communication system.


