Chirp-Compensated Training Sequence for IM-DD OFDM
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Solution Overview
Problem
The introduction of chirp in optical signals during amplitude modulation in high-speed transmission systems using intensity modulation-direct detection (IM-DD) with orthogonal frequency division multiplexing (OFDM) leads to frequency deviation, disrupting orthogonality and causing severe inter-symbol crosstalk, with no effective solution in prior art to address this issue.
Innovation Solution
A training sequence generation method that involves generating a pseudo-random sequence, obtaining a chirp coefficient, and using a negated chirp coefficient to modulate it, creating a chirped pseudo-random sequence. This sequence is then transformed into a training symbol segment with specific amplitude adjustments and cyclic prefixes, effectively offsetting positive and negative chirp effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If amplitude modulation is used in IM-DD OFDM system, then spectral efficiency is improved, but frequency deviation occurs due to chirp
Solution Approach 1:
The patent applies preliminary anti-action by pre-modulating the training sequence with a negative chirp coefficient before transmission. This anticipates the positive chirp effect that will occur during amplitude modulation and compensates for it in advance, thereby eliminating frequency deviation without sacrificing spectral efficiency
Solution Approach 2:
The patent changes the chirp coefficient parameter of the training sequence from positive to negative, creating a compensating effect. By inverting the chirp parameter, the system counteracts the frequency deviation introduced by the modulator, maintaining orthogonality while preserving the spectral efficiency benefits of amplitude modulation
2Device complexity
If amplitude modulation is used, then system complexity is reduced, but orthogonality of subcarriers is destroyed
Solution Approach 1:
The patent applies preliminary anti-action by pre-compensating for the orthogonality destruction through negative chirp modulation of the training sequence. This anticipatory compensation ensures that when the positive chirp occurs during amplitude modulation, the subcarrier orthogonality is preserved, maintaining system stability without increasing complexity
Solution Approach 2:
The patent converts the harmful frequency deviation effect into a beneficial compensation mechanism. By using negative chirp in the training sequence, the system turns the problematic chirp effect into a corrective tool that restores and maintains subcarrier orthogonality, allowing amplitude modulation to proceed without sacrificing system stability
3Ease of manufacture
If training sequence is generated without chirp compensation, then generation process is simple, but inter-symbol crosstalk is severe
Solution Approach 1:
The patent applies preliminary anti-action by incorporating negative chirp compensation directly into the training sequence generation process. This pre-compensation approach eliminates inter-symbol crosstalk at its source without requiring complex post-processing, maintaining generation simplicity while eliminating the harmful crosstalk effect
Solution Approach 2:
The patent performs preliminary action by pre-modulating the training sequence with negative chirp before it is transmitted. This advance preparation ensures that when the training sequence passes through the modulator, the frequency deviation is already compensated, preventing inter-symbol crosstalk from occurring in the first place
Data Source
AI summary
Embodiments of the present invention provide training sequence generation method which includes generating a pseudo random sequence; obtaining a chirp coefficient of a modulator using a negated chirp coefficient to modulate the pseudo random sequence; constructing a training symbol segment that includes L subcarriers in a frequency domain, transforming the training symbol segment from the frequency domain to a time domain to obtain a training symbol segment in the time domain, and generating a training sequence based on the training symbol segment in the time domain and outputting the training sequence.