Arithmetic coding including symbol sequence determination
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Solution Overview
Problem
Existing wireless communication technologies face inefficiencies in encoding and decoding schemes, particularly in achieving optimal throughput, reducing latency, and ensuring quality of service, while being energy-efficient and computationally light.
Innovation Solution
Implementing an arithmetic coding (AC) encoding method constrained by a sequence length and a set of target compositions, which calculates transition probabilities and selects symbols based on these probabilities to achieve a desired amplitude distribution, reducing computing burden and power use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encoding schemes are used to achieve reliable data transmission, then communication reliability is improved, but processing complexity and energy consumption increase
Solution Approach 1:
The encoding process is divided into multiple iterations, where each iteration processes a portion of the data and refines the encoding progressively. This segmentation allows complex encoding tasks to be broken down into manageable steps, reducing overall processing complexity while maintaining reliability through iterative refinement.
Solution Approach 2:
Target compositions are determined in advance before the actual encoding process begins. By pre-calculating and storing the target compositions that represent desired symbol distributions, the system avoids complex real-time calculations during encoding, thereby reducing processing complexity and energy consumption while ensuring reliable transmission.
2Productivity
If advanced modulation schemes like 4096-QAM are implemented to increase throughput, then data transmission rate is improved, but computational load and energy use increase
Solution Approach 1:
The target compositions representing optimal symbol distributions for high-order modulation schemes are pre-determined and stored. During actual data transmission, the system simply retrieves and applies these pre-calculated compositions rather than performing complex real-time optimization, enabling high throughput with reduced energy consumption.
Solution Approach 2:
The system changes the parameter of symbol distribution by using different target compositions for different transmission conditions. This allows adaptive optimization of the encoding process to match channel conditions, achieving high throughput efficiently without requiring excessive computational resources for real-time parameter optimization.
3Loss of time
If complex encoding operations are performed to reduce latency, then transmission speed is improved, but processing complexity increases
Solution Approach 1:
The encoding operation is segmented into multiple iterations, each performing a simple transformation based on pre-determined target compositions. This segmentation reduces the complexity of each individual processing step while achieving the overall latency reduction goal through the cumulative effect of multiple simple iterations.
Data Source
AI summary
This disclosure provides methods, devices and systems for encoding data, to achieve a symbol distribution, for wireless communication. One implementation includes a method in which arithmetic coding (AC) encoding is constrained by a set of one or more target compositions for symbol sequences. The target compositions are known, and the encoding method is performed in multiple iterations. Each iteration generates a symbol and establishes a composition prefix, which is taken into account in the subsequent iteration. The methods generate output sequences defining symbols that are used to encode data for transmission.