Enumerative Distribution Matching for QAM Symbol Probability Control
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Solution Overview
Problem
Current data communication systems face challenges in efficiently modulating and demodulating data signals, particularly in quadrature amplitude modulation, where controlling the probability distribution of symbol amplitudes to optimize transmission efficiency is not effectively addressed.
Innovation Solution
The system converts data blocks of a fixed first bit length into codewords of a second fixed bit length with variable symbol composition, using enumerative decoding and Hamming Weight to control the probability distribution of symbols, ensuring that high amplitude symbols are less probable than low amplitude symbols, through the use of a distribution matcher and a binary matcher in a transmitter and receiver apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data blocks are converted to codewords with fixed bit length using conventional modulation, then transmission can be performed with simple hardware, but the probability distribution of symbol amplitudes cannot be optimized, reducing transmission efficiency
Solution Approach 1:
The patent changes the parameter of symbol amplitude probability distribution by using enumerative decoding to generate codewords with constrained Hamming weight. This controls the probability distribution of modulation symbols, making high-amplitude symbols less probable and low-amplitude symbols more probable, thereby optimizing transmission efficiency without requiring complex hardware modifications
Solution Approach 2:
The patent replaces complex hardware-based amplitude control mechanisms with a software-based enumerative decoding algorithm. Instead of using complex modulation hardware to control symbol probabilities, the system uses computational algorithms to generate codewords with specific Hamming weight constraints, achieving the same effect through information processing rather than mechanical/electrical control
2Productivity
If the probability of high amplitude symbols is increased to maximize information rate, then higher information rates are achieved, but the error probability increases due to reduced robustness
Solution Approach 1:
The patent optimizes the parameter of symbol amplitude probability distribution by constraining the Hamming weight of generated codewords. This creates a balanced probability distribution where high-amplitude symbols are less probable and low-amplitude symbols are more probable, simultaneously achieving high information rates through efficient coding while maintaining reliability through the robustness of low-amplitude symbols
3Productivity
If variable symbol composition is used in codewords to optimize probability distribution, then transmission efficiency is improved, but the complexity of encoding and decoding increases
Solution Approach 1:
The patent performs preliminary action by pre-generating and storing tables of valid codewords with constrained Hamming weight during system initialization. The enumerative decoding algorithm uses these pre-computed tables to efficiently map information bits to codewords, avoiding the need for complex real-time calculations during actual transmission, thereby reducing encoding complexity while maintaining the benefits of variable symbol composition
Solution Approach 2:
The patent implements a dynamic encoding approach where the enumerative decoding algorithm adaptively selects codewords based on the input information bits and the constrained Hamming weight requirements. The system dynamically adjusts the symbol composition of codewords to optimize probability distribution while maintaining efficient encoding through the use of pre-computed lookup tables and systematic search algorithms
Data Source
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AI summary
An apparatus comprising means for: converting data blocks that have a first fixed bit length to respective codewords, for controlling transmission, that have a second fixed bit length greater than the first fixed bit length and that have constrained but variable symbol composition comprising: converting each data block of fixed first bit length to an index value; enumeratively decoding the index value to obtain a codeword of the second fixed bit length that has a symbol composition that can vary with the index value