About

Jeffrey Andrews is a professor and holds the Truchard Family Chair in Engineering in the Chandra Family Department of Electrical & Computer Engineering at The University of Texas at Austin. He received his B.S. in Engineering with High Distinction from Harvey Mudd College, and his M.S. and Ph.D. in Electrical Engineering from Stanford University. Dr. Andrews is the Director of the 6G@UT research center and has developed CDMA systems at Qualcomm. He has served as a consultant to major industry players including Samsung, Nokia, Qualcomm, Apple, Verizon, AT&T, Intel, Microsoft, Sprint, and NASA. He is a co-author of the books Fundamentals of WiMAX and Fundamentals of LTE, and has held editorial roles such as Editor-in-Chief of the IEEE Transactions on Wireless Communications and Chair of the IEEE Journal on Selected Areas in Information Theory Steering Committee. His research interests encompass wireless communication, information theory, communication theory, stochastic geometry, and machine learning for communications. Dr. Andrews is an IEEE Fellow, an ISI Highly Cited Researcher, and has received numerous awards including the IEEE Kiyo Tomiyasu Award, IEEE Heinrich Hertz Prize, and the IEEE Communications Society Technical Achievement Award.

Research topics

• Computer Security
• Computer Science
• Computer network
• Telecommunications

Selected publications

• Satellite Selection for In-Band Coexistence of Dense LEO Networks

  IEEE Transactions on Wireless Communications · 2026-01-01 · 1 citations

  articleSenior author

  We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

  PublisherDOI

• Generative Modeling for FR3 Channels based on Experimental Measurements

  2025-10-26

  articleSenior author

  The FR3 band (7-24 GHz) is positioned to be an important new spectral band for 6G cellular networks. Currently, there is limited knowledge of the detailed propagation behavior and characteristics of FR3 channels, particularly in the context of cellular deployment. We leverage a recent measurement campaign by AT&T Labs that comprises extensive channel measurements at several distinct FR3 carrier frequencies. We present a diffusion-based generative modeling approach conditioned on user and base station locations to produce directional channel images for realistic FR3 channel characterization. We also introduce a new comparison metric based on delay and angular spreads to evaluate generated channel samples against these ground truth measurements. Our findings reveal that the diffusion model consistently outperforms competitive baselines like conditional variational autoencoders and convolutional neural networks by 57.7 and 71.2%, respectively, in terms of the proposed metric.

  PublisherDOI

• Generating High Dimensional User-Specific Wireless Channels Using Diffusion Models

  IEEE Transactions on Wireless Communications · 2025-08-26 · 8 citations

  articleSenior author

  Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11 dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).

  PublisherDOI

• Spectrum Coexistence Between Passive Satellites and Terrestrial Network via Chernoff Bounds

  2025-06-08

  articleSenior author

  We develop tractable characterizations of the interference resulting from terrestrial cellular networks radiating towards passive satellite sensing receivers. Such a setting has important implications for the future allocation and terrestrial use of spectrum in the 100 to 300 GHz band. Building on a recently developed stochastic geometry approach, we focus on the outage probability experienced by to a constellation of satellite sensors, which depends upon the distribution of the interference experienced by a typical satellite sensor. The distribution is a function of spatial and temporal randomness. We obtain upper bounds on the outage probability using a large deviation technique for Poisson shot noise, which is a novel adaptation of the Chernoff technique. This analytical method allows for the distribution of the interference to be tightly and tractably bounded. Our analysis theoretically confirms that the satellite sensor's outage probability decreases exponentially as the interference constraint is relaxed, and allows bounding of very low outage probability values, which would be very difficult to simulate.

  PublisherDOI

• Beamforming and Power Allocation for ISAC-Enabled Satellite-Terrestrial Networks

  2025-10-26

  articleSenior author

  In this paper, we investigate the integration of a low earth orbit (LEO) satellite communication system with a terrestrial network supporting integrated sensing and communication (ISAC). As both systems share the same spectrum, significant mutual interference arises, degrading the performance of the integrated satellite-terrestrial network (ISTN). To address this challenge, we propose a joint beamforming and power allocation algorithm designed for ISTNs to mitigate mutual interference. Specifically, a power allocation scheme is introduced for multi-beam LEO satellites to protect terrestrial ISAC operations, while a beamforming scheme is designed to support ISAC functionality and simultaneously protect satellite users. Simulation results show that the proposed approach effectively reduces mutual interference and enhances both communication and sensing performances.

  PublisherDOI

• Stochastic Geometry Analysis of Wireless Networks Using Matrix Laplace Transforms

  2025-06-08

  articleSenior author

  In this paper, we consider a matrix function generalization of the Laplace transform of a random variable, termed the matrix Laplace transform. We characterize the conditions under which the matrix Laplace transform exists, establish its relation to the higher order moments and CCDF of a random variable, and derive the matrix Laplace transform of general Poisson shot noise. Techniques leveraging matrix Laplace transforms can provide improved tractability in the analysis of wireless networks using stochastic geometry. In particular, when one considers the underlying point process of transmitters in the network to follow a Poisson Point Process (PPP), techniques exploiting matrix Laplace transforms provide tractable expressions for the coverage probability of the network when the fading power on the desired signal follows a general phase-type distribution, the metadistribution of the SINR when the fading power on the desired signal follows an exponential distribution, and the distribution of the interference power observed by the typical user in the network.

  PublisherDOI

• Satellite Selection for In-Band Coexistence of Dense LEO Networks

  ArXiv.org · 2025-03-19

  preprintOpen accessSenior author

  We study spectrum sharing between two dense low-earth orbit (LEO) satellite constellations, an incumbent primary system and a secondary system that must respect interference protection constraints on the primary system. In particular, we propose a secondary satellite selection framework and algorithm that maximizes capacity while guaranteeing that the time-average interference and absolute interference inflicted upon each primary ground user never exceeds specified thresholds. We solve this NP-hard constrained, combinatorial satellite selection problem through Lagrangian relaxation to decompose it into simpler problems which can then be solved through subgradient methods. A high-fidelity simulation is developed based on public FCC filings and technical specifications of the Starlink and Kuiper systems. We use this case study to illustrate the effectiveness of our approach and that explicit protection is indeed necessary for healthy coexistence. We further demonstrate that deep learning models can be used to predict the primary satellite system associations, which helps the secondary system avoid inflicting excessive interference and maximize its own capacity.

  PublisherOA PDFDOI

• Self-Nomination: Deep Learning for Decentralized CSI Feedback Reduction in MU-MIMO Systems

  ArXiv.org · 2025-04-23 · 1 citations

  preprintOpen accessSenior author

  This paper introduces a novel deep learning-based user-side feedback reduction framework, termed self-nomination. The goal of self-nomination is to reduce the number of users (UEs) feeding back channel state information (CSI) to the base station (BS), by letting each UE decide whether to feed back based on its estimated likelihood of being scheduled and its potential contribution to precoding in a multiuser MIMO (MU-MIMO) downlink. Unlike SNR- or SINR-based thresholding methods, the proposed approach uses rich spatial channel statistics and learns nontrivial correlation effects that affect eventual MU-MIMO scheduling decisions. To train the self-nomination network under an average feedback constraint, we propose two different strategies: one based on direct optimization with gradient approximations, and another using policy gradient-based optimization with a stochastic Bernoulli policy to handle non-differentiable scheduling. The framework also supports proportional-fair scheduling by incorporating dynamic user weights. Numerical results confirm that the proposed self-nomination method significantly reduces CSI feedback overhead. Compared to baseline feedback methods, self-nomination can reduce feedback by as much as 65%, saving not only bandwidth but also allowing many UEs to avoid feedback altogether (and thus, potentially enter a sleep mode). Self-nomination achieves this significant savings with negligible reduction in sum-rate or fairness.

  PublisherOA PDFDOI

• Area Spectral Efficiency of a Power-Constrained Direct-to-Cell Satellite System

  2025-10-26

  articleSenior author

  This paper investigates the theoretical limits of downlink area spectral efficiency (ASE) in a power-constrained direct-to-cell (D2C) satellite system in low-Earth orbit (LEO). This framework uses a sum power constraint and a per-beam power constraint as a means to determine both the efficiency of a single beam, as well as the total number of beams that may be formed. We evaluate ASE by modeling RF losses, antenna array performance, and SINR-dependent throughput under different geometries, interference parameters, and fading distributions. General results are evaluated for a large antenna array optimized for D2C, demonstrating optimistic rates for this system between 0.5 and 3.5 Mbps. However, this framework provides a method to analyze spectral efficiency and rates of any D2C system.

  PublisherDOI

• Self-Nomination Based Feedback Reduction for MU-MIMO

  2024-10-27 · 1 citations

  articleSenior author

  This paper presents a deep learning-based decentralized feedback reduction method, termed “self-nomination,” for multi-user multiple-input multiple-output (MU-MIMO) systems. Traditional approaches require all user equipment (UE) to provide channel state information (CSI) feedback regardless of current channel conditions, leading to excessive overhead and energy consumption. In contrast, our method enables UEs to autonomously decide whether to feed back CSI by analyzing their own channel vectors. We formulate the uplink feedback capacity as a constraint and employ a primal-dual learning framework for training. To handle the non-differentiability of hard decision-making in the feedback process, we utilize the straight-through estimator. Simulation results demonstrate that our approach reduces the number of UEs providing uplink feedback by approximately 50%, while maintaining a comparable downlink sum rate. In addition to also saving UE power, another positive side effect of this technique is complexity reduction in downlink MU-MIMO scheduling, since there are fewer UEs to consider.

  PublisherDOI

Recent grants

• Heterogeneous Network Connectivity and Capacity

  NSF · $425k · 2010–2014

• CAREER: The Transmission Capacity of Hierarchical Wireless Networks

  NSF · $400k · 2007–2013

• Collaborative Research: Cognitive Ad Hoc Networks: Capacity Optimization Through Local Adaptation

  NSF · $115k · 2006–2009

• CIF: Medium: Fundamental Properties of Millimeter Wave Networks: Signal, Interference, and Connectivity

  NSF · $1000k · 2015–2020

• RINGS:Deep Generative Models for Ultra High-Dimensional Next Generation Communication Systems

  NSF · $732k · 2022–2026

Frequent coauthors

• Robert W. Heath

  115 shared

• Harpreet S. Dhillon

  Virginia Tech

  53 shared

• Sarabjot Singh

  36 shared

• François Baccelli

  École Normale Supérieure - PSL

  36 shared

• Steven Weber

  Drexel University

  35 shared

• Radha Krishna Ganti

  Indian Institute of Technology Madras

  34 shared

• Eren Balevi

  33 shared

• Abhishek Gupta

  Chaudhary Charan Singh University

  33 shared

Labs

• Texas ECE - Electrical & Computer Engineering at UT AustinPI

Education

• PhD, EE

  Stanford University

Awards & honors

• 2016 IEEE Communications Society & Information Theory Societ…
• 2014 IEEE Stephen O. Rice Prize
• 2014 IEEE Leonard G. Abraham Prize
• 2018 IEEE Leonard G. Abraham Prize
• 2011 IEEE Heinrich Hertz Prize