  Recent research on joint source channel coding (JSCC) for wireless
communications has achieved great success owing to the employment of deep
learning (DL). However, the existing work on DL based JSCC usually trains the
designed network to operate under a specific signal-to-noise ratio (SNR)
regime, without taking into account that the SNR level during the deployment
stage may differ from that during the training stage. A number of networks are
required to cover the scenario with a broad range of SNRs, which is
computational inefficiency (in the training stage) and requires large storage.
To overcome these drawbacks our paper proposes a novel method called Attention
DL based JSCC (ADJSCC) that can successfully operate with different SNR levels
during transmission. This design is inspired by the resource assignment
strategy in traditional JSCC, which dynamically adjusts the compression ratio
in source coding and the channel coding rate according to the channel SNR. This
is achieved by resorting to attention mechanisms because these are able to
allocate computing resources to more critical tasks. Instead of applying the
resource allocation strategy in traditional JSCC, the ADJSCC uses the
channel-wise soft attention to scaling features according to SNR conditions. We
compare the ADJSCC method with the state-of-the-art DL based JSCC method
through extensive experiments to demonstrate its adaptability, robustness and
versatility. Compared with the existing methods, the proposed method takes less
storage and is more robust in the presence of channel mismatch.
