Joint Source-Channel Coding Over Additive Noise Analog Channels Using Mixture of Variational Autoencoders
Joint Source-Channel Coding Over Additive Noise Analog Channels Using Mixture of Variational Autoencoders
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DOI：
10.1109/jsac.2021.3078489
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发表时间：
2021-07
影响因子：
16.4
通讯作者：
Yashas Malur Saidutta;A. Abdi;F. Fekri
中科院分区：
文献类型：
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作者：
Yashas Malur Saidutta;A. Abdi;F. Fekri
在本文中，我们提出了一种学习方案的联合信源信道编码（JSCC）在模拟独立的加性噪声信道。我们制定的学习问题表明，从率失真理论的最小化损失函数，是由变分自动编码器（VAE）的损失函数的上限。我们表明，当源维度大于通道维度时，两个源样本在彼此的邻域中的编码不需要彼此靠近。需要通过使用多个编码器并选择编码器来对不连续性的特定侧上的样本进行编码来解决这种不连续投影。我们探索两种选择方法，一种是基于一个直观的规则，另一种是作为一个学习任务的混合专家（莫伊）设置。我们分析了这些方法的梯度和原因，为什么后者更好地避免局部最优。我们通过模拟高斯源在AWGN信道上的JSCC系统的性能，并显示所学习的解决方案接近或优于先前提出的解决方案，证明了所提出的方法的有效性。所提出的方法也自然能够推广到其他源分布，我们展示了模拟的拉普拉斯源。学习的系统也对信道条件的变化具有鲁棒性。此外，如果信道条件在发射机和接收机处都是已知的，则单个系统可以被训练为在信道条件的范围上进行概括。最后，我们评估了我们提出的方法在三个不同的图像数据集，并展示了一致的改进现有的方法，由于VAE制定。
In this paper, we present a learning scheme for Joint Source-Channel Coding (JSCC) over analog independent additive noise channels. We formulate the learning problem by showing that the minimization loss function from rate-distortion theory, is upper bounded by the loss function of the Variational Autoencoder (VAE). We show that when the source dimension is greater than the channel dimension, the encoding of two source samples in the neighborhood of each other need not be near each other. Such discontinuous projection needs to be accounted for by using multiple encoders and selecting an encoder to encode samples on a particular side of the discontinuity. We explore two selection methodologies, one based on an intuitive rule and the other where it is posed as a learning task in a Mixture-of-Experts (MoE) setup. We analyze the gradients of these methods and reason why the latter is better at avoiding local optima. We show the efficacy of the proposed methodology by simulating the performance of the system for JSCC of Gaussian sources over AWGN channels and showing that the learned solutions are close to or better than the ones proposed earlier. The proposed methodology is also naturally capable of generalizing to other source distributions which we showcase by simulating for Laplace sources. The learned systems are also robust to changes in channel conditions. Further, a single system can be trained to generalize over a range of channel conditions provided the channel conditions are known at both the transmitter and the receiver. Finally, we evaluate our proposed methodology on three different image datasets and showcase consistent improvement over existing methods due to the VAE formulation.