HV Poor
Princeton University
H-index: 182
North America-United States
Description
HV Poor, With an exceptional h-index of 182 and a recent h-index of 127 (since 2020), a distinguished researcher at Princeton University, specializes in the field of Communication Systems, Wireless Communications, Communications, Statistical Signal Processing, Information Theory.
His recent articles reflect a diverse array of research interests and contributions to the field:
An exact characterization of the generalization error of machine learning algorithms
On the tacit linearity assumption in common cascaded models of RIS-parametrized wireless channels
Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities
Physical Layer Security with DCO-OFDM-based VLC Under the Effects of Clipping Noise and Imperfect CSI
Electromagnetic information theory: Fundamentals, modeling, applications, and open problems
Overcoming Beam Squint in mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
On differential privacy for federated learning in wireless systems with multiple base stations
Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
Professor Information
University | Princeton University |
---|---|
Position | Michael Henry Strater University Professor |
Citations(all) | 157042 |
Citations(since 2020) | 76202 |
Cited By | 107072 |
hIndex(all) | 182 |
hIndex(since 2020) | 127 |
i10Index(all) | 1536 |
i10Index(since 2020) | 977 |
University Profile Page | Princeton University |
Research & Interests List
Communication Systems
Wireless Communications
Communications
Statistical Signal Processing
Information Theory
Top articles of HV Poor
An exact characterization of the generalization error of machine learning algorithms
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and lack of guarantees. As a result, they may fail to characterize the exact generalization ability of a learning algorithm. Our main contribution is an exact characterization of the expected generalization error of the well-known Gibbs algorithm (aka Gibbs posterior) using symmetrized KL information between the input training samples and the output hypothesis. Our result can be applied to tighten existing expected generalization error and PAC-Bayesian bounds. Our approach is versatile, as it also characterizes the generalization error of the Gibbs algorithm with data-dependent regularizer and that of the Gibbs algorithm in the asymptotic regime, where it converges to the empirical risk minimization algorithm. Of particular relevance, our results highlight the role the symmetrized KL information plays in controlling the generalization error of the Gibbs algorithm.
Authors
Gholamali Aminian,Yuheng Bu,Laura Toni,Miguel Rodrigues,Gregory Wornell
Journal
Advances in Neural Information Processing Systems
Published Date
2021/12/6
On the tacit linearity assumption in common cascaded models of RIS-parametrized wireless channels
The wireless channel is a linear input-output relation that depends non-linearly on the RIS configuration: physics-compliant models involve the inversion of an “interaction” matrix. We identify two independent origins of this structural non-linearity: i ) proximity-induced mutual coupling between close-by RIS elements; ii ) reverberation-induced long-range coupling between all RIS elements arising from multi-path propagation in complex radio environments. Mathematically, we cast the “interaction” matrix inversion as the sum of an infinite Born series [for i )] or Born-like series [for ii )] whose K th term physically represents paths involving K bounces between the RIS elements [for i )] or wireless entities [for ii )]. We identify the key physical parameters that determine whether these series can be truncated after the first and second term, respectively, as tacitly done in common cascaded models of RIS-parametrized …
Authors
Antonin Rabault,Luc Le Magoarou,Jérôme Sol,George C Alexandropoulos,Nir Shlezinger,H Vincent Poor,Philipp del Hougne
Journal
IEEE Transactions on Wireless Communications
Published Date
2024/2/28
Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities
Abstract Peer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised …
Authors
Ying Zhang,Valentin Robu,Sho Cremers,Sonam Norbu,Benoit Couraud,Merlinda Andoni,David Flynn,H Vincent Poor
Journal
Applied Energy
Published Date
2024/2/1
Physical Layer Security with DCO-OFDM-based VLC Under the Effects of Clipping Noise and Imperfect CSI
Visible light communications (VLC) and physical-layer security (PLS) are key candidate technologies for 6G wireless communication. This paper combines these two technologies by considering an orthogonal frequency division multiplexing (OFDM) technique called DC-biased optical OFDM (DCO-OFDM) equipped with PLS as applied to indoor VLC systems. First, a novel PLS algorithm is designed to protect the DCO-OFDM transmission of the legitimate user from an eavesdropper. A closed-form expression for the achievable secrecy rate is derived and compared with the conventional DCO-OFDM without security. To analyze the security performance of the PLS algorithm under the effects of the residual clipping noise and the channel estimation errors, a closed-form expression is derived for a Bayesian estimator of the clipping noise induced naturally at the DCO-OFDM systems after estimating the optical channel …
Authors
Erdal Panayirci,Ekin B Bektaş,H Vincent Poor
Journal
IEEE Transactions on Communications
Published Date
2024/2/26
Electromagnetic information theory: Fundamentals, modeling, applications, and open problems
Traditional massive multiple-input multiple- output (MIMO) information theory adopts non-physically consistent assumptions, including white-noised, scalar-quantity, far-field, discretized, and monochromatic EM fields, which mismatch the nature of the underlying electromagnetic (EM) fields supporting the physical layer of wireless communication systems. To incorporate EM laws into designing procedures of the physical layer, we first propose the novel concept of EM physical layer, whose backbone theory is called EM information theory (EIT). In this article, we systematically investigate the basic ideas and main results of EIT. First, we review the fundamental analytical tools of classical information theory and EM theory. Then, we introduce the modeling and analysis methodologies of EIT, including continuous field modeling, degrees of freedom, and mutual information analyses. Several EIT-inspired applications are …
Authors
Jieao Zhu,Zhongzhichao Wan,Linglong Dai,Mérouane Debbah,H Vincent Poor
Journal
IEEE Wireless Communications
Published Date
2024/1/31
Overcoming Beam Squint in mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
The beam squint effect, which manifests in different steering matrices in different sub-bands, has been widely considered a challenge in millimeter wave (mmWave) multi-input multi-output (MIMO) channel estimation. Existing methods either require specific forms of the precoding/combining matrix, which restrict their general practicality, or simply ignore the beam squint effect by only making use of a single sub-band for channel estimation. Recognizing that different steering matrices are coupled by the same set of unknown channel parameters, this paper proposes to exploit the common sparsity structure of the virtual channel model so that signals from different sub-bands can be jointly utilized to enhance the performance of channel estimation. A probabilistic model is built to induce the common sparsity in the spatial domain, and the first-order Taylor expansion is adopted to get rid of the grid mismatch in the …
Authors
Le Xu,Lei Cheng,Ngai Wong,Yik-Chung Wu,H Vincent Poor
Journal
IEEE Transactions on Signal Processing
Published Date
2024/2/22
On differential privacy for federated learning in wireless systems with multiple base stations
In this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system where each user is equipped with a classifier and communication cells have mostly fewer resource …
Authors
Nima Tavangaran,Mingzhe Chen,Zhaohui Yang,José Mairton B Da Silva Jr,H Vincent Poor
Journal
IET communications
Published Date
2024/1/17
Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
Stacked intelligent metasurfaces (SIM) are capable of emulating reconfigurable physical neural networks by relying on electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. A SIM is fabricated by densely integrating multiple metasurface layers, each consisting of a large number of small meta-atoms that can control the EM waves passing through it. In this paper, we harness a SIM for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to the conventional designs, an advanced SIM in front of the receiver array automatically carries out the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. As a result, the receiver array directly observes the angular spectrum of the incoming signal. In this context, the DOA estimates can be readily obtained by using probes to detect the energy distribution on the receiver array. This avoids the need for power-thirsty radio frequency (RF) chains. To enable SIM to perform the 2D DFT, we formulate the optimization problem of minimizing the fitting error between the SIM's EM response and the 2D DFT matrix. Furthermore, a gradient descent algorithm is customized for iteratively updating the phase shift of each meta-atom in SIM. To further improve the DOA estimation accuracy, we configure the phase shift pattern in the zeroth layer of the SIM to generate a set of 2D DFT matrices associated with orthogonal spatial frequency bins. Additionally, we analytically evaluate the performance of the proposed SIM-based DOA estimator by deriving a tight upper bound for the mean square error (MSE). Our numerical …
Authors
Jiancheng An,Chau Yuen,Yong Liang Guan,Marco Di Renzo,Mérouane Debbah,H Vincent Poor,Lajos Hanzo
Journal
arXiv preprint arXiv:2402.08224
Published Date
2024/2/13
Professor FAQs
What is HV Poor's h-index at Princeton University?
The h-index of HV Poor has been 127 since 2020 and 182 in total.
What are HV Poor's top articles?
The articles with the titles of
An exact characterization of the generalization error of machine learning algorithms
On the tacit linearity assumption in common cascaded models of RIS-parametrized wireless channels
Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities
Physical Layer Security with DCO-OFDM-based VLC Under the Effects of Clipping Noise and Imperfect CSI
Electromagnetic information theory: Fundamentals, modeling, applications, and open problems
Overcoming Beam Squint in mmWave MIMO Channel Estimation: A Bayesian Multi-Band Sparsity Approach
On differential privacy for federated learning in wireless systems with multiple base stations
Two-Dimensional Direction-of-Arrival Estimation Using Stacked Intelligent Metasurfaces
...
are the top articles of HV Poor at Princeton University.
What are HV Poor's research interests?
The research interests of HV Poor are: Communication Systems, Wireless Communications, Communications, Statistical Signal Processing, Information Theory
What is HV Poor's total number of citations?
HV Poor has 157,042 citations in total.
What are the co-authors of HV Poor?
The co-authors of HV Poor are Zhu Han, Andreas F. Molisch, Zhiguo Ding, Sergio Verdu, Shlomo Shamai (Shitz), Trung Q. Duong.