Parallel Trellis Processing for Turbo Equalization Bottlenecks
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Solution Overview
Problem
Trellis-based methods such as Viterbi decoding and turbo equalization are computationally intensive, and there is a need for mechanisms to maximize the rate at which these methods can be performed, while also promoting flexibility in receiver functionality to handle signal processing operations in software-defined radio systems.
Innovation Solution
A method involving multiple processors operating in parallel on overlapping subsequences of symbol data sequences, using trellis structures to generate soft estimates for information bits, with alternating operations between demodulation and decoding processes to achieve turbo equalization, and utilizing a 2D grid architecture for processor interconnection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trellis-based methods (Viterbi decoding, turbo equalization) are implemented, then signal processing accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The received symbol sequence is divided into multiple overlapping subsequences, each processed by a separate processor. This segmentation allows the computationally intensive trellis-based methods to be distributed across multiple processing units, reducing the computational burden on each individual processor while maintaining overall processing accuracy.
Solution Approach 2:
The patent transitions from sequential single-processor execution to parallel multi-processor execution, adding a temporal parallelism dimension. Multiple processors operate simultaneously on different subsequences of the same symbol sequence, effectively distributing the computational complexity across multiple processing threads while preserving the accuracy benefits of trellis-based methods.
2Productivity
If parallel processing is used to increase processing rate, then productivity is improved, but device complexity increases
Solution Approach 1:
The processing system is segmented into multiple independent processors, each handling a specific subsequence. This segmentation enables parallel execution and increases processing rate, while the modular nature of the segmentation makes the overall system complexity manageable through standardized processor designs.
Solution Approach 2:
Each processor in the parallel system is designed to be universal and capable of processing any subsequence of the symbol data. This multi-functionality reduces device complexity by using identical or similar processor units throughout the system, rather than requiring specialized processors for different functions, thereby simplifying the overall processor architecture.
3Adaptability or versatility
If software-defined radio is implemented to improve flexibility, then adaptability is improved, but processing speed decreases due to software overhead
Solution Approach 1:
The software processing is segmented into parallel tasks that can be executed simultaneously on multiple processors. This segmentation reduces the sequential software overhead by distributing processing loads, thereby improving processing speed while maintaining the flexibility benefits of software-defined radio through programmable processing logic.
Solution Approach 2:
The system performs preliminary parallel processing of symbol subsequences before final decoding and reconstruction. By preparing processed data from multiple subsequences in advance through parallel operations, the system reduces the overall processing time and mitigates software overhead effects, while the software-defined architecture maintains adaptability for different communication protocols.
Data Source
AI summary
A receiver system and method for recovering information from a symbol data sequence Y. The symbol data sequence Y corresponds to a symbol data sequence X that is transmitted onto the channel by a transmitter. The symbol data sequence X is generated by the transmitter based on associated information bits. At the receiver, a set of two or more processors operate in parallel on two or more overlapping subsequences of the symbol data sequence Y, where each of the two or more overlapping subsequences of the symbol data sequence Y corresponds to a respective portion of a trellis. The trellis describes redundancy in the symbol data sequence Y. The action of operating in parallel generates soft estimates for the associated information bits. The soft estimates are useable to form a receive message corresponding to the associated information bits.