Energy-Based Arithmetic Coding for PAS Under Complexity Constraints
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently encoding and transmitting data over varying channel conditions, particularly in the context of probabilistic amplitude shaping, which affects spectral efficiency and signal quality.
Innovation Solution
Implementing a two-phase energy-based arithmetic coding for probabilistic amplitude shaping (PAS) that determines energy associated with a symbol sequence through multiple iterations, enhancing the encoding process to improve transmission efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encoding methods are used for probabilistic amplitude shaping, then device complexity is reduced, but spectral efficiency and signal quality deteriorate
Solution Approach 1:
The encoding process is divided into two distinct phases: a first phase that determines an energy value associated with the symbol sequence, and a second phase that determines the actual symbol sequence based on multiple iterations and the energy value from the first phase. This segmentation allows complex probabilistic amplitude shaping to be broken down into manageable steps, improving signal quality while maintaining reasonable device complexity.
Solution Approach 2:
The first phase performs preliminary determination of the energy value associated with the symbol sequence before the second phase determines the actual symbol sequence. This preliminary action enables the second phase to iterate more efficiently by having energy constraints pre-established, thereby improving spectral efficiency without proportionally increasing overall complexity.
2Productivity
If multi-phase iterative encoding is implemented, then spectral efficiency improves, but processing time increases
Solution Approach 1:
By segmenting the encoding into two phases where the first phase computes energy values and the second phase performs iterations based on those energy values, the processing can be optimized. The segmentation allows parallel computation possibilities and avoids redundant calculations, improving spectral efficiency while controlling processing time.
Solution Approach 2:
The first phase performs preliminary energy determination before the iterative second phase. This preliminary action provides the iterative process with pre-computed energy constraints, allowing the iterations to converge faster and reducing the total processing time while maintaining improved spectral efficiency.
3Productivity
If energy-based arithmetic coding is applied, then transmission efficiency improves, but computational complexity increases
Solution Approach 1:
The energy-based arithmetic coding is implemented through segmented phases: first determining energy values associated with symbol sequences, then using those energy values to guide the determination of actual symbol sequences through iterative processes. This segmentation makes the computational complexity more manageable while preserving transmission efficiency gains.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitting node may obtain a k-bit sequence of information bits. The transmitting node may encode the k-bit sequence to an output sequence that corresponds to a length-n symbol sequence in a set of symbol sequences of length n and over an alphabet Am in accordance with a first phase of energy-based arithmetic coding for probabilistic amplitude shaping (PAS) and a second phase of energy-based arithmetic coding for PAS. The transmitting node may perform, to a receiving node, a transmission based at least in part on the length-n symbol sequence. Numerous other aspects are described.