OFDM Permutation Sequence Generation Without Lookup Tables
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems face increased hardware complexity and performance degradation due to the need for large permutation sequence tables in OFDM systems, which are required to distribute burst errors across multiple FEC blocks, leading to inefficiencies in error correction.
Innovation Solution
A wireless communication method and system that generates permutation sequences on the fly using computational circuits, eliminating the need for pre-stored lookup tables by computing sequences based on the data stream's content, such as its length and sampling spacing, to determine mapping relationships between data items in the data sequence and the permuted sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large table of permutation sequences is used to distribute burst errors across FEC blocks, then error correction capability is improved, but hardware complexity increases due to the need for large storage memories
Solution Approach 1:
The permutation sequence generation is made self-service through the use of hash functions and linear feedback shift registers (LFSRs) that automatically generate sequences based on input data, eliminating the need for external storage tables. The system generates its own permutation sequences on-demand using computational algorithms.
Solution Approach 2:
The mechanical storage system (large permutation sequence tables in memory) is replaced with a computational system using hash functions and LFSRs. This substitution transforms the approach from storing pre-computed sequences to dynamically generating them through mathematical operations, significantly reducing hardware storage requirements.
2Reliability
If permutation sequence tables are stored in memory, then random distribution of burst errors is achieved, but system performance degrades due to increased memory access time and hardware overhead
Solution Approach 1:
The permutation sequence generation is made continuous and on-demand through the use of hash functions and LFSRs. Instead of retrieving sequences from memory, the system continuously generates sequences as needed during data processing, eliminating memory access delays and maintaining continuous operational flow.
Solution Approach 2:
The approach changes from static storage of permutation sequences to dynamic generation based on input parameters. The hash function takes input data as parameters and generates corresponding permutation sequences, allowing the system to adapt sequences to specific data characteristics while avoiding memory access overhead.
3Ease of manufacture
If pre-stored permutation sequences are used, then implementation is simplified, but additional storage devices are required increasing hardware complexity
Solution Approach 1:
The permutation sequence tables are extracted and removed from the system architecture. Instead of storing sequences, the system extracts only the essential generation logic (hash function and LFSR), which produces sequences on-demand without requiring any storage memory for the sequences themselves.
Solution Approach 2:
The system uses a compact computational model (hash function + LFSR) that copies the essential functionality of large permutation tables into a minimal algorithmic structure. This computational copy generates the same effect as storing full tables but occupies negligible space.
Data Source
AI summary
A wireless communication method implemented in a communication system includes receiving a first data sequence, and processing the first data sequence to obtain information containing at least one of a first number and a sampling spacing associated with the first data sequence. The method also includes permuting the first data sequence to generate a permuted second data sequence. Permuting the first data sequence includes determining a first parameter based on at least one of the first number and the sampling spacing, determining a second parameter based on at least one of the first parameter, the first number, and the sampling spacing, and determining a mapping relationship between a j-th data item of the permuted second data sequence and an i-th data item of the first data sequence. The method further includes outputting the permuted second data sequence.