PHY Data Unit Spatial Stream Segmentation for Mixed Modulation Rates
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Solution Overview
Problem
Current wireless local area network (WLAN) technologies face challenges in efficiently transmitting data at different modulation and coding rates, particularly in mixed environments with devices supporting various IEEE standards like 802.11ac and legacy protocols, where ensuring low latency and reliability across different data streams is crucial for high-throughput applications such as video streaming.
Innovation Solution
A method and apparatus that modulate and encode data using different constellations and coding schemes, parsing data into spatial streams to create a single physical layer (PHY) data unit, allowing for simultaneous transmission of data with varying levels of resolution and quality, such as most significant bits (MSB) and least significant bits (LSB) of video data, to optimize reliability and throughput across different client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted using a single modulation scheme and coding rate, then device compatibility is improved, but transmission efficiency and reliability for different data types deteriorate
Solution Approach 1:
The patent segments data into different types (e.g., video data into MSB and LSB portions) and applies different modulation and coding schemes to each segment. This allows critical data to use more reliable modulation while less critical data uses higher efficiency modulation, resolving the contradiction between universal compatibility and optimized reliability for different data types
Solution Approach 2:
Different portions of the transmitted signal are assigned different quality characteristics through selective modulation schemes. Critical data portions receive more robust modulation (better error resistance) while non-critical portions use less robust but more efficient modulation, achieving local optimization of reliability without compromising overall system compatibility
2Productivity
If high-order modulation schemes are used to increase data rate, then throughput is improved, but error rate increases and reliability deteriorates
Solution Approach 1:
The patent divides data into segments with different importance levels and applies different modulation schemes to each segment. Critical segments use lower-order modulation for reliability while non-critical segments use higher-order modulation for throughput, thus achieving both high productivity and maintained reliability
Solution Approach 2:
The system dynamically changes modulation parameters (constellation size, coding rate) based on data type and channel conditions. By adjusting these parameters selectively for different data portions, the system optimizes the trade-off between throughput and reliability rather than using a fixed high-order modulation for all data
3Productivity
If different modulation schemes are used for different data types, then transmission efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments data at the source into distinct portions (e.g., video MSB/LSB) that can be independently processed with different modulation schemes. This segmentation approach simplifies the overall system architecture compared to complex adaptive modulation, as each segment follows a predetermined modulation pattern based on its type
Solution Approach 2:
The system employs a universal framework that handles multiple data types through a common segmentation and parallel processing architecture. This multi-functional approach allows the same system structure to efficiently manage different modulation schemes simultaneously, reducing overall system complexity despite the diversity of modulation techniques used
4Reliability
If robust error correction coding is applied to all data, then reliability is improved, but throughput decreases due to overhead
Solution Approach 1:
The patent segments data into critical and non-critical portions, applying strong error correction coding only to critical segments while using lighter or no coding on non-critical segments. This selective coding strategy maintains high reliability for important data while preserving throughput by reducing overall coding overhead
Solution Approach 2:
Different coding strengths are applied to different portions of the data stream based on their importance. Critical data receives local optimization with robust error correction, while non-critical data uses minimal coding, achieving local quality optimization that balances reliability and throughput rather than applying uniform strong coding to all data
Data Source
AI summary
In a method implemented in a communication device configured to transmit PHY data units via a communication channel, first data and second data is received. The first data is modulated according to a first constellation having a first number of constellation points, and the second data is modulated according to a second constellation having a second number of constellation points higher than the first number of constellation points. The first data and the second data is parsed to a plurality of spatial streams such that a first subset of the spatial streams includes at least some of the modulated first data but none of the modulated second data, and a second subset of the spatial streams includes at least some of the modulated second data but none of the modulated first data. A single PHY data unit that includes the plurality of spatial streams is generated.