从局部信道状态信息学习渐进分布式压缩策略
Learning Progressive Distributed Compression Strategies from Local Channel State Information
摘要：
本文提出了一个深度学习框架来设计分布式压缩策略，分布式代理需要压缩源的高维观测数据，然后通过带宽受限的链路将压缩后的比特发送到融合中心进行源重构。此外，我们要求压缩策略是渐进的，以便它可以适应代理和融合中心之间不断变化的链路带宽。此外，为了确保可扩展性，我们调查的策略，只依赖于本地信道状态信息（CSI）在每个代理。为此，我们使用一种数据驱动的方法，其中每个代理的渐进线性组合和统一量化策略都是根据其本地CSI进行训练的。为了应对量化操作建模的挑战（在神经网络的训练中总是产生零梯度），我们提出了一种新的方法，利用批量训练数据的统计数据来设置均匀量化器的动态范围。在数值上，我们表明，建议的分布式估计策略设计，只有本地CSI可以显着降低信令开销，并可以实现较低的均方误差失真的源重建比国家的最先进的设计，需要全球CSI在可比的整体通信成本。
英文摘要：
This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, then send the compressed bits via bandwidth limited links to a fusion center for source reconstruction. Further, we require the compression strategy to be progressive so that it can adapt to the varying link bandwidths between the agents and the fusion center. Moreover, to ensure scalability, we investigate strategies that depend only on the local channel state information (CSI) at each agent. Toward this end, we use a data-driven approach in which the progressive linear combination and uniform quantization strategy at each agent are trained as a function of its local CSI. To deal with the challenges of modeling the quantization operations (which always produce zero gradients in the training of neural networks), we propose a novel approach of exploiting the statistics of the batch training data to set the dynamic ranges of the uniform quantizers. Numerically, we show that the proposed distributed estimation strategy designed with only local CSI can significantly reduce the signaling overhead and can achieve a lower mean-squared error distortion for source reconstruction than state-of-the-art designs that require global CSI at comparable overall communication cost.
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