Joint design of compressed sensing and network coding for wireless meshed networks

基于网络编码和压缩感知范式分别带来的令人印象深刻的功能，将它们结合在一起的想法似乎是显而易见的。通过将它们结合起来，我们可以通过显着减少数据量来实现大规模感知场景的低延迟通信。我们的第一阶段提案旨在打破这两种关键技术的不可知组合，并将其替换为无线网状网络的整体方法。我们确定了相关的场景和应用，以设计一个强大的联合编码/重新编码/解码和压缩方案。目前，我们正在将我们的联合方法部署在现实生活中，用于工业物联网设备的音频和视频传输。我们将在第一阶段结束时提供一个完整的演示器。在项目的第二阶段，我们希望继续我们的研究工作，以实现未来通信系统的低延迟，可扩展性和安全性。第二阶段的主要目标是：）有限域压缩传感，ii.）联合编码计算，iii.）用于每个节点的最优编码/压缩决策的自适应学习策略，以及iv.）通过用户活动检测进行组测试。虽然网络编码已经在有限域中运行，但挑战在于将压缩感知从真实的域转换为有限域计算。我们希望有限域压缩感知能够克服计算复杂度高的缺点，这对延迟有直接影响。此外，我们希望继续在用户激活和安全无线分布式存储的组合组测试和编码计算方面的工作。在这种情况下，我们希望联合网络编码和压缩传感设计与我们的新想法将大大提高内置的安全性和可靠性的任何未来的通信系统。通过引入机器学习，特别是基于深度神经网络的学习，我们希望大大降低实时应用的延迟和复杂性。

Based on the impressive features that network coding and compressed sensing paradigms have brought separately, the idea of bringing them together seems obvious. By combining them, we can realize low latency communication for large-scale sensing scenarios just by reducing the amount of data significantly. Our first phase proposal aimed to break with the agnostic combination of the two key techniques and replace it with a holistic approach for wireless meshed networks. We identified the relevant scenarios and applications, in order to design a robust joint encoding/recoding/decoding and compression scheme. Currently, we are deploying our joint approach in real-life for industrial IoT-devices for audio and video transmissions. We will deliver a full-fledge demonstrator by the end of the first phase.In the second phase of the project, we would like to continue our research work with novel ideas and concepts in order to achieve low-latency, scalability, and security for future communication systems. The main goals of phase two are i.) finite field compressed sensing, ii.) joint coded computation, iii.) adaptive learning strategies for optimal coding/compression decisions per node, and iv.) group testing with user activity detection. While network coding already operates in finite fields, the challenge is to change compressed sensing from real field to finite field computation. We expect that finite-field compressed sensing will overcome the drawback of costly computational complexity, which has a direct impact on latency. Furthermore, we would like to continue the work in combinatorial group testing and coded computation for user activation and secure wireless distributed storage. In this context, we expect that joint network coding and compressed sensing design with our new ideas will enhance tremendously the built-in security and reliability of any future communication system. By bringing in machine learning, and in particular learning based on deep neural networks, we expect to considerably reduce the latency and complexity for real-time applications.