We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Although some noticeable efforts have been done to produce large-scale datasets in the area or radio fingerprinting, other physical-layer learning problems ( e.g. Project+Code: http://ge.in.tum.de/publications/2017-sig-chu/, Liquid Splash Modeling with Neural Networks , PDF: https://arxiv.org/pdf/2005.05456, Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics , On the other hand, the time-varying nature of the channel could compromise adversarial attempts. Therefore, physical-layer deep learning model have also to be relatively small to be feasibly implemented on embedded devices. PDF: https://arxiv.org/pdf/1805.05086, Graph networks as learnable physics engines for inference and control , PDF: https://arxiv.org/pdf/1708.07469, Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations , Indeed, prior work in computer vision has shown that the accuracy of a deep learning model can be significantly compromised by crafting adversarial inputs. share, The significant computational requirements of deep learning present a ma... active and quickly growing field of research. The target of this section is to discuss existing system-level challenges in physical-layer deep learning as well as the state of the art in addressing these issues. share, The explosion of 5G networks and the Internet of Things will result in a... The issue of stochasticity of physical-layer deep learning has been mostly investigated in the context of radio fingerprinting. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter … The third factor is the unavoidable imperfections hidden inside the RF circuitry of off-the-shelf radios (i.e. [zhang2019deep]. We now present an agenda of research opportunities in the field of physical-layer deep learning. Indeed, recent research [restuccia2019deepradioid, shawabka2020exposing] has shown that the wireless channel makes it highly unlikely to deploy deep learning algorithms that will function without periodic fine-tuning of the weights. ∙ A neural network, in combination with techniques such as compressive sensing, could be trained to infer the channel directly based on the I/Q samples, without requiring additional pilots. Moreover, next-generation networks will necessarily require fast and fine-grained optimization of parameters at all the layers of the protocol stack. , WiFi, Bluetooth or Zigbee) and attempt to heuristically change parameters such as modulation scheme, coding level, packet size, etc based on metrics computed in real time from pilots and/or training symbols. Since the FIR is tailored to the specific device’s hardware, it is shown that an adversary is not able to use a stolen FIR to imitate a legitimate device’s fingerprint. Specifically, the first row of the filter (i.e., A, B, C) detects I/Q patterns where the waveform transitions from the first to the third quadrant, while the second row (i.e., D, E, F) detects transitions from the third to the second quadrant. optimization loop that trains the deep neural network. PDF: https://arxiv.org/pdf/1803.09109, A proposal on machine learning via dynamical systems , Work fast with our official CLI. PDF: http://papers.nips.cc/paper/9672-hamiltonian-neural-networks.pdf, Interactive Differentiable Simulation , In particular, in the receiver (RX) DSP chain the incoming waveform is first received and placed in an I/Q buffer (step 1). RFLearn’s performance and design cycle were evaluated on a custom FPGA-defined radio. Figure 6 summarizes the challenges discussed below. PDF: https://arxiv.org/pdf/2002.00021, Learning to Simulate Complex Physics with Graph Networks , ∙ So far, physical-layer deep learning techniques have been validated in controlled, lab-scale environments and with a limited number of wireless technologies. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D 2 NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work … PDF: https://arxiv.org/pdf/2003.00868, Incorporating Symmetry into Deep Dynamics Models for Improved Generalization , a... Project: https://github.com/fabienbaradel/cophy, Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations , Apart from forward or inverse, the type of integration between learning ∙ 01/23/2019 ∙ by Jithin Jagannath, et al. PDF: https://arxiv.org/pdf/2003.08723, WeatherBench: A benchmark dataset for data-driven weather forecasting , Project+Code: https://cims.nyu.edu/~schlacht/CNNFluids.htm, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , In this paper, we have provided an overview of physical-layer deep learning and the state of the art in this topic. For a more detailed compendium of the state of the art, the interested reader can take a look at the comprehensive survey of the topic by Zhang et al. Project+Code: https://github.com/thunil/Deep-Flow-Prediction, Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors , This article is an updated version of: de Bezenac, Pajot A and Gallinari P 2018 Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge 6th Int. Learning-based Radio Fingerprinting Algorithms, Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless The newly-developed Platforms for Advanced Wireless Research (PAWR) will be fundamental in addressing the above challenge [pawr]. ∙ share. PDF: https://arxiv.org/pdf/1812.04426, Deep Learning the Physics of Transport Phenomena , PDF: https://www.sciencedirect.com/science/article/abs/pii/S0045793019302282, Deep Neural Networks for Data-Driven Turbulence Models , ∙ For this reason, the research community desperately needs large-scale experimentation to really understand whether these techniques can be applied in realistic wireless ecosystems where hundreds of nodes, protocols and channels will necessarily coexist. papers that you think should be included by sending a mail to i15ge at cs.tum.de, We propose a novel deep learning model for spatio-temporal modeling of skeletal data, for application in rehabilitation assessment. Critically, this allows not only to save hardware resources, but also to keep both latency and energy consumption constant, which are highly-desirable features in embedded systems design and are particular critical in wireless systems, as explained in Section. challenges and how deep learning can be applied to address crucial problems in Latency and power consumption are compared to a software-based implementation, which is shown to perform respectively 17x and 15x worse than RFLearn. deep learning (DL) techniques. PDF: https://arxiv.org/pdf/2004.05477, Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction , The above and similar CNN-based approaches [OShea-ieeejstsp2018], although demonstrated to be effective, do not fully take into account that a physical-layer deep learning system is inherently stochastic in nature; Figure 5 summarizes the main sources of randomness. It is very well understood what deep neural networks (DNNs) actually learn as discriminating features in computer vision applications. Similarly, if the DNN performs modulation recognition every 1ms, the DNN has to run with latency much less than 1ms if it wants to detect modulation changes. observations). Machine learning enables computers to address problems by learning from data. e... optimization of spectrum resources an urgent necessity. of deep learning (DL) for the physical layer. Thus, designing optimizations to applications. Finally, the incoming waveform is released from the I/Q buffer and sent for demodulation (step 4). • We introduce an effective mechanism for regularizing the training of deep neural networks in small data regimes. PDF: https://arxiv.org/pdf/1910.08613, IDENT: Identifying Differential Equations with Numerical Time evolution , PDF: https://arxiv.org/pdf/1911.08655, DeepFlow: History Matching in the Space of Deep Generative Models ,

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