7 research outputs found
SCAN: Semantic Communication with Adaptive Channel Feedback
In existing semantic communication systems for image transmission, some
images are generally reconstructed with considerably low quality. As a result,
the reliable transmission of each image cannot be guaranteed, bringing
significant uncertainty to semantic communication systems. To address this
issue, we propose a novel performance metric to characterize the reliability of
semantic communication systems termed semantic distortion outage probability
(SDOP), which is defined as the probability of the instantaneous distortion
larger than a given target threshold. Then, since the images with lower
reconstruction quality are generally less robust and need to be allocated with
more communication resources, we propose a novel framework of Semantic
Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by
adaptively adjusting the overhead of channel feedback for images with different
reconstruction qualities, thereby enhancing transmission reliability. To
realize SCAN, we first develop a deep learning-enabled semantic communication
system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by
leveraging the channel state information (CSI) and noise variance in the model
design. We then develop a performance evaluator to predict the reconstruction
quality of each image at the transmitter by distilling knowledge from
DeepSC-MIMO. In this way, images with lower predicted reconstruction quality
will be allocated with a longer CSI codeword to guarantee the reconstruction
quality. We perform extensive experiments to demonstrate that the proposed
scheme can significantly improve the reliability of image transmission while
greatly reducing the feedback overhead
Distortion Exponent in MIMO Fading Channels with Time-Varying Source Side Information
Transmission of a Gaussian source over a time-varying multiple-input
multiple-output (MIMO) channel is studied under strict delay constraints.
Availability of a correlated side information at the receiver is assumed, whose
quality, i.e., correlation with the source signal, also varies over time. A
block-fading model is considered for the states of the time-varying channel and
the time-varying side information; and perfect state information at the
receiver is assumed, while the transmitter knows only the statistics. The high
SNR performance, characterized by the \textit{distortion exponent}, is studied
for this joint source-channel coding problem. An upper bound is derived and
compared with lowers based on list decoding, hybrid digital-analog
transmission, as well as multi-layer schemes which transmit successive
refinements of the source, relying on progressive and superposed transmission
with list decoding. The optimal distortion exponent is characterized for the
single-input multiple-output (SIMO) and multiple-input single-output (MISO)
scenarios by showing that the distortion exponent achieved by multi-layer
superpositon encoding with joint decoding meets the proposed upper bound. In
the MIMO scenario, the optimal distortion exponent is characterized in the low
bandwidth ratio regime, and it is shown that the multi-layer superposition
encoding performs very close to the upper bound in the high bandwidth expansion
regime.Comment: Submitted to IEEE Transactions on Information Theor