Constellation Shaping Coding Selection for QAM Capacity Gain
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Solution Overview
Problem
Modern wireless communication systems using quadrature amplitude modulation (QAM) with uniform bit-probability constellations are limited by an achievable capacity that fails to meet the Shannon capacity, hindering the ability to meet increasing demands for mobile broadband access.
Innovation Solution
Implement probabilistic constellation shaping (PCS) techniques that dynamically adjust the probability of using constellation symbols, combining it with systematic-type coding to maintain shaping gain while avoiding the loss of coding gain, thereby enhancing throughput and adapting transmission rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probabilistic constellation shaping is applied to improve channel capacity, then the achievable capacity approaches Shannon capacity, but coding gain is lost due to incompatibility with conventional non-systematic coding
Solution Approach 1:
The information sequence is divided into two parts: a first part that is probabilistically shaped and a second part that is not shaped. This segmentation allows the shaped portion to approach channel capacity while the unshaped portion can be systematically coded to preserve coding gain, resolving the contradiction between capacity improvement and coding gain preservation.
Solution Approach 2:
Different coding strategies are applied to different portions of the information sequence. The first portion uses probabilistic shaping with non-systematic coding to maximize capacity, while the second portion uses systematic coding to preserve coding gain. This local differentiation resolves the contradiction by optimizing each portion for its specific purpose.
2Ease of manufacture
If uniform bit-probability QAM constellation is used, then the system is simple to implement, but the achievable capacity fails to meet Shannon capacity
Solution Approach 1:
The probability distribution of constellation symbols is changed from uniform to non-uniform in the first portion of the information sequence. This parameter change enables the system to approach Shannon capacity while maintaining a grid-like constellation structure that is still relatively simple to implement, resolving the contradiction between implementation simplicity and achievable capacity.
3Productivity
If probabilistic constellation shaping is applied to increase throughput, then transmission rates can be adapted, but the coding efficiency deteriorates due to loss of systematic structure
Solution Approach 1:
By segmenting the information sequence into shaped and unshaped portions, the system can increase throughput through probabilistic shaping while preserving coding efficiency in the unshaped portion through systematic coding. This segmentation resolves the contradiction between throughput increase and coding efficiency preservation.
Solution Approach 2:
The system uses a composite coding approach combining probabilistic shaping and systematic coding in different portions of the information sequence. This composite strategy allows the system to achieve both high throughput through shaping and high coding efficiency through systematic coding, resolving the contradiction between productivity and information loss.
Data Source
AI summary
Aspects of the disclosure relate to wireless communication utilizing a modulation and coding scheme that selectively or dynamically applies probabilistically-shaped coding (PCS) to modulate a transmitted waveform. A communication device may determine whether to apply PCS. If PCS is to be applied, the device can encode the message based on a systematic code; and if PCS is not to be applied, the device can encode the message based on a non-systematic code. Other aspects, embodiments, and features are also claimed and described.