Deep Refinement-Based Joint Source Channel Coding over Time-Varying Channels
arxiv(2023)
摘要
In recent developments, deep learning (DL)-based joint source-channel coding
(JSCC) for wireless image transmission has made significant strides in
performance enhancement. Nonetheless, the majority of existing DL-based JSCC
methods are tailored for scenarios featuring stable channel conditions, notably
a fixed signal-to-noise ratio (SNR). This specialization poses a limitation, as
their performance tends to wane in practical scenarios marked by highly dynamic
channels, given that a fixed SNR inadequately represents the dynamic nature of
such channels. In response to this challenge, we introduce a novel solution,
namely deep refinement-based JSCC (DRJSCC). This innovative method is designed
to seamlessly adapt to channels exhibiting temporal variations. By leveraging
instantaneous channel state information (CSI), we dynamically optimize the
encoding strategy through re-encoding the channel symbols. This dynamic
adjustment ensures that the encoding strategy consistently aligns with the
varying channel conditions during the transmission process. Specifically, our
approach begins with the division of encoded symbols into multiple blocks,
which are transmitted progressively to the receiver. In the event of changing
channel conditions, we propose a mechanism to re-encode the remaining blocks,
allowing them to adapt to the current channel conditions. Experimental results
show that the DRJSCC scheme achieves comparable performance to the other
mainstream DL-based JSCC models in stable channel conditions, and also exhibits
great robustness against time-varying channels.
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