no code implementations • 25 Apr 2024 • Yaqi Hu, Mingsheng Yin, Marco Mezzavilla, Hao Guo, Sundeep Rangan
The upper mid-band (FR3) has been recently attracting interest for new generation of mobile networks, as it provides a promising balance between spectrum availability and coverage, which are inherent limitations of the sub 6GHz and millimeter wave bands, respectively.
no code implementations • 12 Dec 2023 • Golara Ahmadi Azar, Melika Emami, Alyson Fletcher, Sundeep Rangan
To study this problem, we consider a simple probability model for discrete data where there is some "true" but unknown embedding where the correlation of random variables is related to the similarity of the embeddings.
no code implementations • 23 Nov 2023 • Tommy Azzino, Marco Mezzavilla, Sundeep Rangan, Yao Wang, John-Ross Rizzo
In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information.
no code implementations • 21 Nov 2023 • Seongjoon Kang, Giovanni Geraci, Marco Mezzavilla, Sundeep Rangan
The growing demand for broader bandwidth in cellular networks has turned the upper mid-band (7-24 GHz) into a focal point for expansion.
no code implementations • 31 Oct 2023 • Yu Hao, Fan Yang, Hao Huang, Shuaihang Yuan, Sundeep Rangan, John-Ross Rizzo, Yao Wang, Yi Fang
By combining the prompt and input image, a large vision-language model (i. e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt.
no code implementations • 1 Oct 2023 • Marco Canil, Jacopo Pegoraro, Jesus O. Lacruz, Marco Mezzavilla, Michele Rossi, Joerg Widmer, Sundeep Rangan
We prototype and validate a multistatic mmWave ISAC system based on IEEE802. 11ay.
no code implementations • 23 Sep 2023 • Golara Ahmadi Azar, Qin Hu, Melika Emami, Alyson Fletcher, Sundeep Rangan, S. Farokh Atashzar
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG).
1 code implementation • 11 Jun 2023 • Mingsheng Yin, Tao Li, Haozhe Lei, Yaqi Hu, Sundeep Rangan, Quanyan Zhu
To equip the navigation agent with sample-efficient learning and {zero-shot} generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping.
no code implementations • 14 May 2023 • Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
We study the local dynamics of GDA for training a GAN with a kernel-based discriminator.
no code implementations • 16 Apr 2023 • Jacopo Pegoraro, Jesus O. Lacruz, Tommy Azzino, Marco Mezzavilla, Michele Rossi, Joerg Widmer, Sundeep Rangan
We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions.
no code implementations • 22 Dec 2022 • Yaqi Hu, Mingsheng Yin, William Xia, Sundeep Rangan, Marco Mezzavilla
Evaluation of these systems requires statistical models that can capture the joint distribution of the channel paths across multiple frequencies.
no code implementations • 19 Sep 2022 • Seongjoon Kang, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, Sundeep Rangan, Vasilii Semkin, William Xia, Giuseppe Loianno
5G millimeter-wave (mmWave) cellular networks are in the early phase of commercial deployments and present a unique opportunity for robust, high-data-rate communication to unmanned aerial vehicles (UAVs).
no code implementations • 21 Aug 2022 • Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data.
no code implementations • 3 Jul 2022 • Mingsheng Yin, Yaqi Hu, Tommy Azzino, Seongjoon Kang, Marco Mezzavilla, Sundeep Rangan
Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors.
no code implementations • 11 May 2022 • Yaqi Hu, Mingsheng Yin, Sundeep Rangan, Marco Mezzavilla
In these scenarios, standard channel models based on plane waves cannot capture the curvature of each wave front necessary to model spatial multiplexing.
no code implementations • 20 Jan 2022 • Mojtaba Sahraee-Ardakan, Melikasadat Emami, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
Empirical observation of high dimensional phenomena, such as the double descent behaviour, has attracted a lot of interest in understanding classical techniques such as kernel methods, and their implications to explain generalization properties of neural networks.
no code implementations • 1 Jan 2022 • Panagiotis Skrimponis, Seongjoon Kang, Abbas Khalili, Wonho Lee, Navid Hosseinzadeh, Marco Mezzavilla, Elza Erkip, Mark J. W. Rodwell, James F. Buckwalter, Sundeep Rangan
Power consumption is a key challenge in millimeter wave (mmWave) receiver front-ends, due to the need to support high dimensional antenna arrays at wide bandwidths.
no code implementations • 25 Dec 2021 • Zhongzheng Yuan, Tommy Azzino, Yu Hao, Yixuan Lyu, Haoyang Pei, Alain Boldini, Marco Mezzavilla, Mahya Beheshti, Maurizio Porfiri, Todd Hudson, William Seiple, Yi Fang, Sundeep Rangan, Yao Wang, J. R. Rizzo
The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment.
1 code implementation • 5 Apr 2021 • Seongjoon Kang, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, William Xia, Sundeep Rangan, Vasilii Semkin, Giuseppe Loianno
Additional dedicated (rooftop-mounted and uptilted) base stations strengthen the coverage provided that their density is comparable to that of the standard deployment, and would be instrumental for sparse deployments of the latter.
no code implementations • 31 Mar 2021 • Vasilii Semkin, Seongjoon Kang, Jaakko Haarla, William Xia, Ismo Huhtinen, Giovanni Geraci, Angel Lozano, Giuseppe Loianno, Marco Mezzavilla, Sundeep Rangan
Wireless communication at millimeter wave frequencies has attracted considerable attention for the delivery of high-bit-rate connectivity to unmanned aerial vehicles (UAVs).
no code implementations • 8 Mar 2021 • Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan Rossi, Sundeep Rangan, Alyson K. Fletcher
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
no code implementations • 20 Feb 2021 • Abbas Khalili, Sundeep Rangan, Elza Erkip
Since the BS measurements are noisy, it is not possible to find a narrow beam that includes the angle of arrival (AoA) of the user with probability one.
no code implementations • 19 Jan 2021 • Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
The degree of this bias depends on the variance of the transition kernel matrix at initialization and is related to the classic exploding and vanishing gradients problem.
no code implementations • 16 Dec 2020 • William Xia, Sundeep Rangan, Marco Mezzavillla, Angel Lozano, Giovanni Geraci, Vasilii Semkin, Giuseppe Loianno
The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs).
1 code implementation • NeurIPS 2020 • Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features, as well as training samples, grow to infinity but the number of hidden nodes stays fixed.
no code implementations • 25 Aug 2020 • William Xia, Sundeep Rangan, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, Vasilii Semkin, Giuseppe Loianno
Statistical channel models are instrumental to design and evaluate wireless communication systems.
no code implementations • 13 May 2020 • William Xia, Michele Polese, Marco Mezzavilla, Giuseppe Loianno, Sundeep Rangan, Michele Zorzi
Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings.
no code implementations • 6 May 2020 • Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Alyson K. Fletcher, Sundeep Rangan, Michael Trumpis, Brinnae Bent, Chia-Han Chiang, Jonathan Viventi
This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals.
3 code implementations • ICML 2020 • Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
We provide a general framework to characterize the asymptotic generalization error for single-layer neural networks (i. e., generalized linear models) with arbitrary non-linearities, making it applicable to regression as well as classification problems.
no code implementations • 26 Jan 2020 • Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output.
1 code implementation • NeurIPS 2019 • Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher
Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies.
no code implementations • 8 Nov 2019 • Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
This paper presents a novel algorithm, Multi-Layer Vector Approximate Message Passing (ML-VAMP), for inference in multi-layer stochastic neural networks.
no code implementations • 19 Mar 2019 • Parthe Pandit, Mojtaba Sahraee-Ardakan, Arash A. Amini, Sundeep Rangan, Alyson K. Fletcher
We derive precise upper bounds on the mean-squared estimation error in terms of the number of samples, dimensions of the process, the lag $p$ and other key statistical properties of the model.
no code implementations • 1 Mar 2019 • Parthe Pandit, Mojtaba Sahraee, Sundeep Rangan, Alyson K. Fletcher
Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text.
1 code implementation • NeurIPS 2018 • Alyson K. Fletcher, Sundeep Rangan, Subrata Sarkar, Philip Schniter
Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction.
Information Theory Information Theory
no code implementations • 20 Jun 2017 • Alyson K. Fletcher, Sundeep Rangan
In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs.
no code implementations • NeurIPS 2017 • Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Philip Schniter, Sundeep Rangan
We show that the parameter estimates and mean squared error (MSE) of x in each iteration converge to deterministic limits that can be precisely predicted by a simple set of state evolution (SE) equations.
4 code implementations • 8 May 2017 • Marco Mezzavilla, Menglei Zhang, Michele Polese, Russell Ford, Sourjya Dutta, Sundeep Rangan, Michele Zorzi
Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems.
Networking and Internet Architecture
1 code implementation • 4 Dec 2016 • Mark Borgerding, Philip Schniter, Sundeep Rangan
signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP.
Information Theory Information Theory
1 code implementation • 4 Nov 2016 • Philip Schniter, Sundeep Rangan, Alyson Fletcher
The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery.
Information Theory Information Theory
1 code implementation • 10 Oct 2016 • Sundeep Rangan, Philip Schniter, Alyson K. Fletcher
The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i. i. d.\ sub-Gaussian matrices $\mathbf{A}$, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal.
Information Theory Information Theory
no code implementations • 25 Feb 2016 • Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems.
4 code implementations • 22 Feb 2016 • Russell Ford, Menglei Zhang, Sourjya Dutta, Marco Mezzavilla, Sundeep Rangan, Michele Zorzi
In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator.
Networking and Internet Architecture I.6.5; I.6.7
no code implementations • NeurIPS 2014 • Alyson K. Fletcher, Sundeep Rangan
In this work, we propose a computationally fast method for the state estimation based on a hybrid of loopy belief propagation and approximate message passing (AMP).
no code implementations • NeurIPS 2012 • Ulugbek Kamilov, Sundeep Rangan, Michael Unser, Alyson K. Fletcher
We present a method, called adaptive generalized approximate message passing (Adaptive GAMP), that enables joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector $\xbf$.
no code implementations • NeurIPS 2011 • Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a low-dimensional signal that drives subsequent nonlinear stages.
no code implementations • NeurIPS 2009 • Sundeep Rangan, Vivek Goyal, Alyson K. Fletcher
It is shown that with large random linear measurements and Gaussian noise, the asymptotic behavior of the MAP estimate of an n-dimensional vector ``decouples as n scalar MAP estimators.
no code implementations • NeurIPS 2009 • Sundeep Rangan, Alyson K. Fletcher
Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for recovering sparse vectors from linear measurements.
no code implementations • NeurIPS 2008 • Sundeep Rangan, Vivek Goyal, Alyson K. Fletcher
Recent research suggests that neural systems employ sparse coding.