DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
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DOI：
10.1109/jsait.2022.3231042
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Tze-Yang Tung;David Burth Kurka;Mikolaj Jankowski;Deniz Gündüz
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文献类型：
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作者：
Tze-Yang Tung;David Burth Kurka;Mikolaj Jankowski;Deniz Gündüz
最近的工作表明，现代机器学习技术可以为长期存在的联合信源-信道编码(JSCC)问题提供一种替代方法。对于使用深度神经网络(DNN)的无线图像和视频传输，已经证明了非常有希望的初步结果，优于使用单独信源和信道编码的流行数字方案。然而，这种方案的端到端训练需要可区分的信道输入表示；因此，先前的工作假设可以在该信道上传输任何复数值。这可以防止在硬件或协议只能允许由数字星座规定的某些信道输入集合的情况下应用这些代码。在这里，我们提出了DeepJSCC-Q，一种端到端优化的JSCC解决方案，用于使用有限通道输入字母表的无线图像传输。我们证明了DeepJSCC-Q可以获得与以前的工作类似的性能，这些工作允许任何复值信道输入，特别是当调制阶数较高时，并且随着调制阶数的增加，性能逐渐接近无约束信道输入的性能。重要的是，DeepJSCC-Q在不可预测的信道条件下保持了图像质量的优雅降级，这是在信道条件快速变化的移动系统中部署的理想特性。
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising initial results, superior to popular digital schemes that utilize separate source and channel codes, have been demonstrated for wireless image and video transmission using deep neural networks (DNNs). However, end-to-end training of such schemes requires a differentiable channel input representation; hence, prior works have assumed that any complex value can be transmitted over the channel. This can prevent the application of these codes in scenarios where the hardware or protocol can only admit certain sets of channel inputs, prescribed by a digital constellation. Herein, we propose DeepJSCC-Q, an end-to-end optimized JSCC solution for wireless image transmission using a finite channel input alphabet. We show that DeepJSCC-Q can achieve similar performance to prior works that allow any complex valued channel input, especially when high modulation orders are available, and that the performance asymptotically approaches that of unconstrained channel input as the modulation order increases. Importantly, DeepJSCC-Q preserves the graceful degradation of image quality in unpredictable channel conditions, a desirable property for deployment in mobile systems with rapidly changing channel conditions.