More Like this

1. DeepRange: Acoustic Ranging via Deep Learning

   https://doi.org/https://doi.org/10.1145/3432195  

   Wenguang Mao, Wei Sun (December 2020, Proceedings of the ACM on interactive mobile wearable and ubiquitous technologies)

   null (Ed.)

   Acoustic ranging is a technique for estimating the distance between two objects using acoustic signals, which plays a critical role in many applications, such as motion tracking, gesture/activity recognition, and indoor localization. Although many ranging algorithms have been developed, their performance still degrades significantly under strong noise, interference and hardware limitations. To improve the robustness of the ranging system, in this paper we develop a Deep learning based Ranging system, called DeepRange. We first develop an effective mechanism to generate synthetic training data that captures noise, speaker/mic distortion, and interference in the signals and remove the need of collecting a large volume of training data. We then design a deep range neural network (DRNet) to estimate distance. Our design is inspired by signal processing that ultra-long convolution kernel sizes help to combat the noise and interference. We further apply an ensemble method to enhance the performance. Moreover, we analyze and visualize the network neurons and filters, and identify a few important findings that can be useful for improving the design of signal processing algorithms. Finally, we implement and evaluate DeepRangeusing 11 smartphones with different brands and models, 4 environments (i.e., a lab, a conference room, a corridor, and a cubic area), and 10 users. Our results show that DRNet significantly outperforms existing ranging algorithms. 

   more » « less
2. Parameterized Interference Cancellation for Single-Carrier Underwater Acoustic Communications

   https://doi.org/10.1109/IEEECONF38699.2020.9389172  

   Ge, Wei; Wang, Zhaohui; Yin, Jingwei; Han, Xiao (October 2020, Global Oceans 2020: Singapore – U.S. Gulf Coast)

   Underwater acoustic communications provide promising solutions for remote and real-time aquatic exploration and monitoring. However, the underwater environment is rich in various kinds of interferences. Those interferences could severely degrade the acoustic communication performance. This work tackles interference cancellation in a single-carrier modulated communication system. Based on the Nyqusit sampling theorem, the interference is parameterized by a finite number of unknown parameters. The Page test is applied to detect the presence of an interfering waveform in the received signal. An iterative receiver is developed, which iteratively performs the interference estimation/cancellation and traditional receiver processing. The proposed receiver is evaluated when the communication waveform is interfered by the ice-cracking impulsive noise and the sonar signal collected from the Arctic. The data processing results reveal that the proposed receiver achieves considerable decoding performance improvement through the iterative interference estimation and cancellation. 

   more » « less
3. Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

   https://doi.org/10.1109/JOE.2019.2956299  

   Qin, Xiangzhao; Qu, Fengzhong; Zheng, Yahong Rosa (July 2020, IEEE Journal of Oceanic Engineering)

   This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation. 

   more » « less
4. On the Characterization of Quantum Flip Stars with Quantum Network Tomography

   https://doi.org/10.1109/QCE57702.2023.00142  

   De_Andrade, Matheus Guedes; Navas, Jake; Montaño, Inès; Towsley, Don (September 2023, IEEE)

   The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is crucial in developing network protocols capable of overcoming the effects of noise in quantum networks. In this context, quantum network tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. In this work, we propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately. We build upon previously defined quantum network tomography protocols, as well as provide novel methods for the unique characterization of bit-flip probabilities in stars. We introduce a theoretical benchmark based on the Quantum Fisher Information matrix to compare the efficiency of quantum network protocols. We apply our techniques to the protocols proposed, and perform an initial analysis on the potential benefits of entanglement for Quantum Network Tomography. Furthermore, we simulate the protocols using NetSquid to assess the convergence properties of the estimators obtained for particular parameter regimes. Our findings show that the efficiency of protocols depend on parameter values and motivate the search for adaptive quantum network tomography protocols. 

   more » « less
5. Stealthy DGoS Attack: DeGrading of Service under the Watch of Network Tomography

   Chiu, Cho-Chun; He, Ting (April 2020, IEEE INFOCOM)

   Network tomography is a powerful tool to monitor the internal state of a closed network that cannot be measured directly, with broad applications in the Internet, overlay networks, and all-optical networks. However, existing network tomography solutions all assume that the measurements are trust-worthy, leaving open how effective they are in an adversarial environment with possibly manipulated measurements. To understand the fundamental limit of network tomography in such a setting, we formulate and analyze a novel type of attack that aims at maximally degrading the performance of targeted paths without being localized by network tomography. By analyzing properties of the optimal attack, we formulate novel combinatorial optimizations to design the optimal attack strategy, which are then linked to well-known problems and approximation algorithms. Our evaluations on real topologies demonstrate the large damage of such attacks, signaling the need of new defenses. 

   more » « less