Parser for Multiple Data Streams in MIMO Systems
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Solution Overview
Problem
In MIMO communication systems, parsing data into multiple streams with varying data rates and puncture patterns is complex, especially when different data rates are associated with different numbers of bits in a given time interval, making it challenging to achieve good performance across all streams.
Innovation Solution
The technique involves encoding traffic data using a base code, generating code bits, and parsing them into multiple streams based on a parsing sequence that distributes code bits evenly across streams, with each stream having a specific puncture pattern and modulation scheme determined by its data rate, allowing for individually selectable data rates and puncture patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is parsed into multiple streams with different data rates and puncture patterns, then individual stream performance can be optimized, but the parsing complexity increases significantly
Solution Approach 1:
The code bits are segmented into multiple streams with different data rates and puncture patterns. Each stream is independently configured with specific modulation schemes and code rates, allowing optimized performance for each stream while managing complexity through structured segmentation
Solution Approach 2:
The parser pre-determines the parsing sequence and distributes code bits to streams in advance based on predetermined rules. The parse cycle and puncture cycles are planned beforehand, reducing real-time parsing complexity while maintaining individual stream optimization
2Productivity
If different data rates are used for different streams, then throughput can be improved, but the parsing process becomes more complicated
Solution Approach 1:
The system dynamically assigns different data rates to different streams based on channel conditions and performance requirements. The parser adapts the parsing sequence to accommodate varying data rates while maintaining manageable complexity through structured distribution rules
Solution Approach 2:
Different streams use different modulation schemes and code rates to achieve varying data rates. The parser manages these parameter variations by implementing a structured parsing sequence that accounts for the different bit requirements of each stream configuration
3Reliability
If code bits are distributed evenly across streams, then performance is improved, but the parsing sequence becomes more complex
Solution Approach 1:
The parser pre-determines the parsing sequence that distributes code bits evenly across streams. By planning the distribution in advance based on the number of puncture cycles required by each stream, the system achieves even distribution while managing sequence complexity through predetermined rules
Data Source
AI summary
Techniques to parse data into multiple (M) streams with selectable data rates are described. The modulation scheme and code rate for each stream are determined based on the data rate selected for that stream. The modulation schemes and code rates for all M streams are used to determine a parse cycle and the number of puncture cycles for each stream in the parse cycle. A sequence of puncture cycles is formed for the M streams such that the puncture cycle(s) for each stream are distributed as evenly as possible across the sequence. An encoder encodes traffic data in accordance with a base code (e.g., a rate 1/2 binary convolutional code) and generates code bits. A parser then parses the code bits into the M streams based on the sequence of puncture cycles, one puncture cycle at a time and in the order indicated by the sequence.