HARQ Buffer Compression Using Subset-Based MMI VQ for LTE
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing VQ algorithms for HARQ data compression in mobile communication networks require high processing power and memory, making them inefficient for real-time operations and increasing the demand for HARQ memory size due to exhaustive searches and large buffer space requirements.
Innovation Solution
The implementation of a simplified MMI VQ method that divides source data into predefined subsets, using a modified Lloyd algorithm to generate a codebook, reducing the need for exhaustive searches and minimizing memory requirements by training data within subsets rather than the entire codebook.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional VQ algorithms are used for HARQ data compression, then data compression is achieved, but processing power and memory requirements increase significantly
Solution Approach 1:
The patent divides the codebook into multiple subsets based on LLR magnitude ranges. Instead of performing exhaustive search over the entire codebook, the search is restricted to relevant subsets, significantly reducing the number of comparisons required while maintaining compression effectiveness.
Solution Approach 2:
The patent applies different quantization strategies to different regions of the codebook based on LLR magnitude characteristics. High-magnitude LLRs use one quantization approach while low-magnitude LLRs use another, optimizing processing efficiency for each region's specific properties.
2Measurement precision
If exhaustive search is used in VQ for accurate codebook matching, then compression accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The codebook is segmented into subsets based on LLR magnitude ranges. The search process only examines relevant subsets rather than the entire codebook, reducing computational complexity from O(N) to O(N/k) where k is the number of subsets, while maintaining accurate matching within each subset.
Solution Approach 2:
Instead of performing complete exhaustive search over all codebook entries, the patent performs partial search limited to relevant subsets. This partial action suffices to achieve the required compression accuracy while significantly reducing processing time.
3Manufacturing precision
If codebook generation is performed using classical GLA or modified Lloyd algorithm, then codebook quality is improved, but processing resources and memory space requirements increase
Solution Approach 1:
The codebook generation process is divided into multiple stages using modified Lloyd algorithm. The algorithm iteratively refines subset boundaries and codewords separately, reducing the computational burden of each iteration while converging to a high-quality codebook structure.
Solution Approach 2:
The patent performs preliminary codebook generation and subset classification during system initialization. This preliminary action creates a structured codebook that can be efficiently searched during runtime without requiring complex processing resources during actual HARQ operations.
Data Source
AI summary
Methods of simplified MMI VQ based HARQ buffer reduction are disclosed. In one embodiment of the invention, the VQ obtains distribution information from source data based on a predefined codebook subset design. The subset design is predefined. Source data is trained within its corresponding subset to generate codebook. In one embodiment of the invention, the predefined subset algorithm is based on the LLR sign value. In another embodiment of the invention, source data is divided into subset based on predefined algorithm. The index to codebook is generated by search through the corresponding subset of the codebook instead of the whole codebook. In one embodiment of the invention, the training method is a modified Lloyd algorithm for MMI VQ. In another embodiment of the invention, the training method is a modified Lloyd algorithm for Euclidean distance VQ.