Semantic Signal Processing for Fading-Channel Generalization
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Solution Overview
Problem
Existing semantic communication technologies face challenges in power allocation and performance analysis, with encoders/decoders exhibiting channel-type dependence and poor generalization, leading to degraded communication quality, especially when applied to fading channels.
Innovation Solution
A method that combines semantic features with channel gains by optimizing transmitter-side and receiver-side signal processing functions, allowing encoders/decoders trained in AWGN channels to adapt to fading channels, minimizing differences between original and reconstructed semantic signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If encoders/decoders are trained in AWGN channels, then training simplicity is improved, but channel-type dependence increases and generalization to fading channels deteriorates
Solution Approach 1:
The patent performs preliminary channel estimation and equalization before semantic decoding. By pre-processing the received signals to compensate for fading channel effects before the decoder operates, the system enables AWGN-trained decoders to function effectively in fading channels without retraining, thus maintaining training simplicity while improving channel adaptability
Solution Approach 2:
The patent introduces channel estimation and equalization as intermediary processing steps between the fading channel and the AWGN-trained decoder. These intermediaries transform the fading channel output into a form compatible with AWGN-trained decoders, acting as a bridge that enables generalization without retraining
2Reliability
If semantic encoders/decoders are designed for specific channel types, then channel performance is improved, but device complexity increases due to multiple channel-specific models
Solution Approach 1:
The patent makes the AWGN-trained encoder/decoder universal by combining them with channel estimation and equalization modules. This universal structure can handle both AWGN and fading channels using the same semantic processing models, eliminating the need for separate channel-specific models while maintaining performance through the adaptive preprocessing stage
3Reliability
If power allocation is optimized for each user, then communication quality is improved, but computational complexity increases
Solution Approach 1:
The patent optimizes power allocation parameters at the transmitter based on channel state information before semantic encoding. By adjusting the power distribution parameters in advance, the system improves communication quality for multiple users while keeping the computational complexity manageable through efficient parameter optimization rather than complex iterative solutions
Data Source
AI summary
In a semantic communication method, input information from a transmitter may be input into a pre-trained semantic encoder to obtain semantic signals. Then, the semantic signals may be transmitted to a first channel to obtain a third semantic signal. Moreover, a transmitter-side signal processing may be performed on the semantic signals to obtain processed semantic signals. Then the processed semantic signals may be transmitted to a second channel to obtain fourth semantic signals. Further, a receiver-side signal processing may be performed on the fourth semantic signals obtain fifth semantic signals. Finally, the fifth semantic signals may be optimized and then be input into a pre-trained semantic decoder for semantic decoding to obtain first decoded information and second decoded information.