Experimental results show that DeepRadioID improves the fingerprinting accuracy by 27% with respect to the state of the art. Moreover, DeepWiERL includes a novel supervised DRL model selection and bootstrap (S-DMSB) technique that leverages HLS and transfer learning to orchestrate a neural network architecture that decreases convergence time and satisfies application and hardware constraints. A professor at Samford University, Chew is one of Ulrich's favorite observers of the new science of learning, and he has put a together a wonderful study guide for college students. The answer lies in a combination of factors summarized in Figure 2, which also explains the key differences between the two approaches through an example. problems (e.g., obtaining a parametrization for a physical system from Extensively employed in the computer vision and natural language processing domains, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are now being “borrowed” by wireless researchers to address handover and power management in cellular networks, dynamic spectrum access, resource allocation/slicing/caching, video streaming, and rate adaptation, just to name a few. Just to provide the reader with some figures, consider that an incoming waveform sampled at 40MHz (e.g., a WiFi channel) will generate a data stream of 160MB/s, provided that each I/Q sample is stored in a 4-byte word. More specifically, all approaches either target PDF: https://arxiv.org/pdf/1903.10255, Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video , When realized concretely, spectrum-driven optimization will realize the dream of a cognitive radio first envisioned more than 20 years ago by Mitola and Maguire [mitola1999cognitive]. For more information, see our Privacy Statement. 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. PDF: https://arxiv.org/pdf/1708.07469, Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations , PDF: https://arxiv.org/pdf/1910.08613, IDENT: Identifying Differential Equations with Numerical Time evolution , PDF: https://arxiv.org/pdf/1905.10793, Reasoning About Physical Interactions with Object-Oriented Prediction and Planning , Project+Code: https://ge.in.tum.de/publications/2017-prantl-defonn/, A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries , The same authors integrate deep reinforcement learning (DRL) techniques at the transmitter’s side by proposing. and feel free to check out our homepage at https://ge.in.tum.de/. The DeepRadioID system was evaluated with a testbed of 20 bit-similar SDRs, as well as on two datasets containing transmissions from 500 ADS-B devices and by 500 WiFi devices. We put forth a deep learning framework that enables the synergistic combination of mathematical models and data. PDF: https://export.arxiv.org/pdf/1806.04482, Deep Dynamical Modeling and Control of Unsteady Fluid Flows , In this 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. On the other hand, existing research has mostly focused on generating spectrum data and training models in the cloud. ever. PDF: http://proceedings.mlr.press/v97/greenfeld19a/greenfeld19a.pdf, Latent-space Dynamics for Reduced Deformable Simulation , We now present an agenda of research opportunities in the field of physical-layer deep learning. The role of deep learning in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. PDF: https://arxiv.org/pdf/1912.00873, Poisson CNN: Convolutional Neural Networks for the Solution of the Poisson Equation with Varying Meshes and Dirichlet Boundary Conditions , The framework is based on high-level synthesis (HLS) and translates the software-based CNN to an FPGA-ready circuit. In this paper, the authors propose RFLearn, a hardware/software framework to integrate a Python-level CNN into the DSP chain of a radio receiver. PDF: https://arxiv.org/pdf/1805.05086, Graph networks as learnable physics engines for inference and control , 3. Very different, however, is the case of physical-layer deep learning, where digital signal processing (DSP) constraints and hardware limitations have to be heeded – in some cases, down to the clock cycle level. In this paper, we have provided an overview of physical-layer deep learning and the state of the art in this topic. The first critical issue is running the model quickly enough to avoid overflowing the I/Q buffer and/or the data buffer (see Figure 3). We have also introduced a roadmap of exciting research opportunities, which are definitely not easy to tackle but that if addressed, will take physical-layer deep learning to the next step in terms of capabilities. Project+Code: http://www.dgp.toronto.edu/projects/latent-space-dynamics/, Learning-Based Animation of Clothing for Virtual Try-On , PDF: https://arxiv.org/pdf/2006.02619, A review on Deep Reinforcement Learning for Fluid Mechanics , The work is the first to prove the feasibility of real-time DRL-based algorithms on a wireless platform, showing superior performance with respect to software-based systems. Loss-terms: the physical dynamics (or parts thereof) are encoded in the Originally planned to be at the Vancouver Convention Centre, Vancouver, BC, Canada, NeurIPS 2020 and this workshop will take place entirely virtually (online). Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. The second factor to consider is adversarial action (i.e., jamming), which may change the received signal significantly and usually, in a totally unpredictable way. Then, a portion of the I/Q samples are forwarded to the RX DNN (step 2), which produces an inference that is used to reconfigure the RX DSP logic (step 3). The first work to propose a systematic investigation into the above issues is [Restuccia-infocom2019]. The general direction of PBDL represents a very In this way, small-scale modifications can strengthen the fingerprint without compromising the throughput significantly. they're used to log you in. PDF: https://arxiv.org/pdf/1806.01242, DeepWarp: DNN-based Nonlinear Deformation , The Machine Learning and the Physical Sciences 2020 workshop will be held on December 11, 2020 as a part of the 34th Annual Conference on Neural Information Processing Systems. With spectrum bands becoming extremely wide, pilot-based channel estimation could not result to be the best strategy – both from an efficiency and effective standpoint. Project+Code: https://cims.nyu.edu/~schlacht/CNNFluids.htm, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , We use essential cookies to perform essential website functions, e.g. Project+Code: http://www.byungsoo.me/project/deep-fluids/, Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow , PDF: https://arxiv.org/pdf/2002.00021, Learning to Simulate Complex Physics with Graph Networks , , 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. We conclude the paper by discussing an agenda of research e... The first kind of attack is called targeted, where given a valid input, a classifier and a target class, it is possible to find an input close to the valid one such that the classifier is “steered” toward the target class. Use Git or checkout with SVN using the web URL. The examples clearly show that lower DNN latency implies (i) higher admissible sampling rate of the waveform, and thus, higher bandwidth of the incoming signal; (ii) higher capability of analyzing fast-varying channels and waveforms. The wireless spectrum is undeniably one of nature’s most complex phenomena. Learn more. Please let us know if we've overlooked The millimeter (mmWave) and Terahertz (THz) spectrum bands have become the de facto candidates for 5G-and-beyond communications. PDF: https://hal.inria.fr/hal-02511646, Hamiltonian Neural Networks , To further clarify why the input tensor was constructed this way, Figure 4(b) shows examples of transitions in the I/Q complex plane corresponding to QPSK, BPSK, and 8PSK. dynamics. PDF: https://openreview.net/forum?id=B1lDoJSYDH, Tranquil-Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds , Moreover, it is shown that accuracy of over 90% can be achieved with a model of only about 30k parameters. The core feature that distinguishes learning-based devices is that digital signal processing (DSP) decisions are driven by deep neural networks (DNNs). Specifically, we first introduce the notion of physical-layer deep learning in Section II, and discuss the related requirements and challenges in III, as well as the existing state of the art. The platforms will enable sub-6, millimeter-wave and drone experimentation capabilities in a multitude of real-world scenarios. The advantage is that through HLS, the constraints on accuracy, latency and power consumption can be tuned based on the application. PDF: https://arxiv.org/pdf/1905.10706, DiffTaichi: Differentiable Programming for Physical Simulation , Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. In this Letter, we establish that contemporary deep learning architectures, in the form of deep … Moreover, the received waveforms still need to be decodable and thus cannot be extensively modified. To point out how CNN filters can distinguish different I/Q patterns, Figure 4(c) shows an example of a 2x3 filter in the first layer of a CNN trained for BPSK vs QPSK modulation recognition. PDF: https://arxiv.org/pdf/2010.03409, Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations , 0 Nowadays, deep learning models usually have millions of parameters (e.g., AlexNet has some 60M weights) or perhaps also tens of millions, e.g., VGG-16, with about 138M. This, in turn, has left a number of key theoretical and system-level issues substantially unexplored. For example, OFDM could be the best strategy at a given moment in time, yet subsequently (. In an article published in 2014, two physicists, Pankaj Mehta and David Schwab, provided an explanation for the performance of deep learning based on renormalization group theory. They are: Neural Networks for Pattern Recognition, 1995. problems. share, Mobile communications have been undergoing a generational change every t... Moreover, it is not feasible to run them from the cloud and transfer the result to the platform due to the additional delay involved. We identify three core challenges in physical-layer deep learning, which are discussed below. The Internet of Things (IoT) is expected to require more effective and In recent years, deep learning (DL) has shown its overwhelming privilege in many areas, such as computer vision, robotics, and natural language processing. Abstract. PDF: https://arxiv.org/pdf/1904.03538, Data-driven discretization: a method for systematic coarse graining of partial differential equations , For example, when we are uploading a picture on a social network, we do not expect a face recognition algorithm that automatically ”tags” us and our friends to run under a given number of milliseconds. A-Deep-Learning-Framework-for-Assessing-Physical-Rehabilitation-Exercises. While deep learning “easily” accomplishes this goal by performing batch gradient descent on fresh input data [chollet2017deep], the same is not true for traditional machine learning algorithms, where tuning feature extraction algorithms can be extremely challenging since it would require to completely change the circuit itself. AI is the present and the future. PDF: https://arxiv.org/pdf/1908.04127, Machine Learning for Fluid Mechanics , (PBDL), i.e., the field of methods with combinations of physical modeling and This can constitute a unique “signature” of the signal that can eventually be learned by the CNN filters. PDF: https://arxiv.org/pdf/1703.01656, Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder , The following collection of materials targets "Physics-Based Deep Learning" A Comprehensive Survey, Deep Learning in Mobile and Wireless Networking: A Survey, DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Deep learning is a branch of machine learning which focuses on multi-layered artificial neural networks [ 29]. However, being heuristic in nature, they necessarily yield sub-optimal performance and are hardly applicable to other protocols beyond the one considered. Users can create their own wireless scenarios and thus create “virtual worlds” where learning algorithms can be truly stressed to their full capacity. Nonetheless, employment of machine learning for wave function representations is focused on more traditional architectures such as restricted Boltzmann machines (RBMs) and fully connected neural networks. The authors show that the wireless channel decreases the accuracy from 85% to 9% and from 30% to 17% in the experimental and government dataset, respectively. the optimal spectrum access strategy accordingly has become more important than For example, if a QPSK modulation is detected instead of BPSK, the RX demodulation strategy is reconfigured accordingly. On the other hand, protocol-driven approaches consider a specific wireless technology (e.g. PDF: https://arxiv.org/pdf/2009.14339, Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution , and physics gives a means for categorizing different methods: Data-driven: the data is produced by a physical system (real or simulated), share, We introduce DeepNovoV2, the state-of-the-art neural networks based mode... Project+Code: https://ge.in.tum.de/publications/2020-um-solver-in-the-loop/, Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates , This is especially true in the highly-dynamic context of the Internet of Things (IoT), where the widespread presence of tiny embedded wireless devices seamlessly connected to people and objects will make spectrum-related quantities such as fading, noise, interference, and traffic patterns hardly predictable with traditional mathematical models. Indeed, prior work in computer vision has shown that the accuracy of a deep learning model can be significantly compromised by crafting adversarial inputs. 5G-and-beyond networks. Abstract Deep learning (DL) has shown great potentials to revolutionizing communication systems. PDF: https://arxiv.org/pdf/2001.04536, Variational Physics-Informed Neural Networks For Solving Partial Differential Equations , Results show that Galileo is able to infer the physical properties of objects and predict the outcome of a vari-ety of physical events, with an accuracy comparable to human subjects. The Deep Learning for Physical Sciences (DLPS) workshop invites researchers to contribute papers that demonstrate progress in the application of machine and deep learning techniques to real-world problems in physical sciences (including the fields and subfields of astronomy, chemistry, Earth science, and … well as miscellaneous works from other groups. The core intuition behind DeepRadioID is to leverage finite input response filters (FIRs) to be computed at the receiver’s side. PDF: https://www.labxing.com/files/lab_publications/2259-1524535041-QiPuSd6O.pdf, Aphynity: Augmenting physical models with deep networks for complex dynamics forecasting , For example, the first layers in convolutional neural networks (CNNs) are trained to detect small-scale “edges” (i.e., contours of eyes, lips, etc), which become more and more complex as the network gets deeper (i.e., mouth, eyes, hair type, etc) [lecun2015deep]. paper, we first discuss the need for real-time deep learning at the physical Track Proc. One possible strategy could be to leverage the packet headers or trailers as source of reference I/Q date to train the learning model. PDF: https://arxiv.org/pdf/2005.04485, Controlling Rayleigh-Benard convection via Reinforcement Learning , Join one of the world's largest A.I. Deep learning is a general approach to artificial intelligence that involves AI that acts as an input to other AI. PDF: https://arxiv.org/pdf/1908.10515, Computing interface curvature from volume fractions: A machine learning approach , Originally developed to support DARPA’s spectrum collaboration challenge in 2019, Colosseum can emulate up to 256x256 4-tap wireless channels among 128 software-defined radios. Within this area, we can distinguish a variety of different physics-based 0 In particular, in the receiver (RX) DSP chain the incoming waveform is first received and placed in an I/Q buffer (step 1). PDF: https://arxiv.org/pdf/1905.11075, phiflow: https://github.com/tum-pbs/phiflow, diff-taichi: https://github.com/yuanming-hu/difftaichi. PDF: https://arxiv.org/pdf/2010.08895.pdf, Learning Composable Energy Surrogates for PDE Order Reduction , ∙ Alongside PAWR, the Colosseum network emulator [Colosseum] will be soon open to the research community and provide us with unprecedented data collection opportunities. PDF: https://ge.in.tum.de/publications/2019-multi-pass-gan/, A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations , loss function, typically in the form of differentiable operations. A neural network can be defined as a standard machine learning function f m, which given an input x returns a prediction y and prediction-confidence conf; i.e. PDF: https://arxiv.org/pdf/2003.14358, Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence , 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. 0 A graphical representation of the NN architecture is provided in Fig. It has been shown that deep learning algorithms can outperform traditional feature-based algorithms in identifying large populations of devices [shawabka2020exposing]. Thus, methods can be roughly categorized in terms of forward versus inverse 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. PDF: https://arxiv.org/pdf/2008.06731, Learned discretizations for passive scalar advection in a 2-D turbulent flow , they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. PDF: https://arxiv.org/pdf/2007.00024, Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow , Figure 4(b) clearly depicts that different modulation waveforms present different transition patterns in the I/Q plane. 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. Interleaved approaches are especially important for temporal evolutions, where they can yield an estimate of future behavior of the dynamics. PDF: http://www.gmrv.es/Publications/2019/SOC19/, Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning , So far, physical-layer deep learning techniques have been validated in controlled, lab-scale environments and with a limited number of wireless technologies. Interleaved: the full physical simulation is interleaved and combined with an output from a deep neural network; this requires a fully differentiable simulator and represents the tightest coupling between the physical system and the learning process. ∙ Journal: https://link.springer.com/article/10.1007/s40304-017-0103-z, Interaction Networks for Learning about Objects, Relations and Physics , PDF: https://arxiv.org/pdf/1807.10300, Fluid directed rigid body control using deep reinforcement learning , learning process can repeatedly evaluate the loss, and usually receives PDF: https://arxiv.org/pdf/2009.14280, Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization , In the wireless domain, however, CNNs do not operate on images but on I/Q samples, implying that further investigations are needed to construct the input tensor from the I/Q samples.
2020 physical deep learning