Optical Receiver Soft-Decision Decoding for Over-Complete Frames
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Solution Overview
Problem
There is no method for generating soft-decision information in communication systems using over-complete frames, preventing the use of soft-decision decoding in reception apparatuses.
Innovation Solution
An optical reception apparatus that generates an n-dimensional received vector by demodulating a modulated optical signal, converts it into m-dimensional candidate vectors, computes the probability of transmission vectors for each partial space, and calculates the log-likelihood ratio to decode the transmission bit string.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If communication method using over-complete frame is adopted to increase communication capacity, then information transmission capacity is improved, but method for generating soft-decision information is missing preventing use of soft-decision decoding
Solution Approach 1:
The received vector is segmented into multiple candidate vectors corresponding to different partial spaces. The decoding process is divided into: converting the received vector into candidate vectors, computing probabilities for each partial space, calculating log-likelihood ratios, and performing soft-decision decoding. This segmentation enables soft-decision decoding in over-complete frame systems by breaking down the complex decoding task into manageable steps.
2Reliability
If soft-decision decoding is used to improve decoding accuracy, then error correction performance is improved, but no method exists for generating required soft-decision information in over-complete frame systems
Solution Approach 1:
Candidate vectors serve as intermediaries between the received vector and the final decoding result. The converting unit generates candidate vectors that represent possible transmitted signals in different partial spaces. These candidate vectors mediate the computation of log-likelihood ratios, enabling soft-decision decoding without requiring direct complex calculations on the received vector alone.
Solution Approach 2:
The system performs preliminary conversion of the received vector into candidate vectors before the actual decoding process. By pre-computing the candidate vectors and their corresponding probabilities in different partial spaces, the system prepares the necessary soft-decision information in advance, making the subsequent decoding process more efficient and accurate.
3Productivity
If over-complete frame is used to transmit more information items than dimensions, then communication efficiency is improved, but traditional decoding methods cannot be applied
Solution Approach 1:
The invention transitions from traditional single-space decoding to multi-dimensional partial space decoding. By dividing the vector space into multiple partial spaces and computing candidate vectors in each dimension, the system adapts over-complete frame structures to work with soft-decision decoding methods, enabling compatibility between high-efficiency communication formats and accurate decoding techniques.
Data Source
AI summary
A receiving unit (2020) generates a received frame from a modulated optical signal. The modulated optical signal is generated such that a transmission symbol is generated by mapping an encoded bit string obtained by encoding a transmission bit string to an m-dimensional symbol space, a transmission frame is generated by mapping the transmission symbol to an n-dimensional frame space (n<m), and an optical carrier wave is modulated by using the transmission frame. A converting unit (2040) generates candidate vectors (m-dimensional vectors belonging to a partial symbol space within the symbol space) by using a received frame. A first computing unit (2070) computes a probability of that the transmission symbol belonging to the partial symbol space is transmitted for each partial symbol space. A second computing unit (2080) computes a log-likelihood ratio of each bit of the encoded bit string by using the probability.