Deep Joint Source-Channel Coding for Underwater Image Transmission
Abstract
Traditional methods for coding underwater acoustic communications are bound to be surpassed by methods optimizing for source-channel coding jointly. However, the complexity of joint-optimization has thwarted successful breakthroughs in this area. We, therefore, present a novel approach, where we model the coding problem as the translation problem of the input sequence to another ‘language’, depending on the estimated channel conditions. We use Long Short-Term Memory (LSTM)-based sequence-to-sequence models to enable this and explain our approach in detail.
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