About

Dongning Guo is a Professor of Electrical and Computer Engineering at Northwestern University, with a courtesy appointment in Computer Science. He holds a Ph.D. and M.A. in Electrical Engineering from Princeton University, an M.Eng. from the National University of Singapore, and a B.Eng. in Electrical Engineering & Information Science from the University of Science & Technology of China. His research interests encompass wireless communications, spectrum policy, blockchain and decentralization, and information theory. His current projects focus on the fundamental limits of blockchain, spectrum sharing techniques and policies, next-generation terrestrial and non-terrestrial networks, and the applications of machine learning and blockchain in wireless networks.

Research topics

• Computer Science
• Artificial Intelligence
• Computer network
• Algorithm
• Embedded system
• Mathematical optimization
• Data science
• Telecommunications
• Engineering
• Distributed computing

Selected publications

• Inter-Satellite Link Configuration for Fast Delivery in Low-Earth-Orbit Constellations

  2026-03-07

  articleOpen accessSenior author

  End-to-end latency in large low-Earth-orbit (LEO) constellations is dominated by propagation delay, making total delay roughly proportional to the network diameter—the longest shortest-path in hops. Current inter-satellite link (ISL) layouts have rarely been optimized to minimize network diameter while simultaneously satisfying physical and operational constraints, including maximum link distance, line-of-sight, per-satellite hardware limits, and longterm link viability over orbital periods. In this study, the selection and assignment of inter-plane ISLs is formulated as a diameterminimization problem on a Starlink-inspired Walker-Delta constellation in which each satellite is equipped with two fixed intra-plane links and may activate up to two inter-plane links. Beginning with a feasible baseline, the topology is iteratively refined by a local-search procedure that replaces or reinforces links to shrink the diameter. The resulting ISL configuration meets all geometric and hardware limits, preserves link stability across multiple orbital periods, and yields a sparse, diameter-aware graph with potential for centralized routing capabilities. Simulations demonstrate the proposed algorithm achieves low worst-case latency without compromising ISL stability, and the trade-off between hop count and long-term link stability is empirically measured for guidance of future LEO network deployments.

  PublisherOA PDFDOI

• Inter-Satellite Link Optimization for Low-Latency Global Networking

  arXiv (Cornell University) · 2026-04-16

  preprintOpen accessSenior author

  Large-scale low-Earth-orbit satellite constellations offer a promising platform for global low-latency networking, aided by faster propagation in free space than in fiber and copper. In such systems, end-to-end latency is largely determined by the inter-satellite link (ISL) topology. In particular, the network diameter, the maximum shortest path between any pair of satellites, serves as a key performance metric for time-sensitive applications. Designing diameter-optimal topologies is challenging due to degree constraints, line-of-sight limitations, and orbital dynamics. This paper proposes a two-stage optimization framework for ISL topology design. First, a continuous relaxation of the link selection problem is formulated as a convex program that maximizes the algebraic connectivity of the Laplacian, serving as a tractable surrogate for diameter minimization. Second, the resulting fractional solution is mapped to a feasible discrete topology using integer linear programming. An iterative local-search heuristic is also developed as a baseline. Extensive simulations on Walker-Delta constellations show that the proposed method consistently achieves smaller network diameters and improved robustness compared to conventional heuristics, while allowing trade-offs between latency and link persistence. The approach offers a principled framework for designing high-performance satellite mesh networks. For a constellation of 1,500 satellites, each equipped with four ISLs of up to 2,500 km, the network diameter can be reduced to as low as 12, yielding end-to-end delays under 90 ms between any two points on Earth.

  PublisherDOI

• Inter-Satellite Link Optimization for Low-Latency Global Networking

  ArXiv.org · 2026-04-16

  articleOpen accessSenior author

  Large-scale low-Earth-orbit satellite constellations offer a promising platform for global low-latency networking, aided by faster propagation in free space than in fiber and copper. In such systems, end-to-end latency is largely determined by the inter-satellite link (ISL) topology. In particular, the network diameter, the maximum shortest path between any pair of satellites, serves as a key performance metric for time-sensitive applications. Designing diameter-optimal topologies is challenging due to degree constraints, line-of-sight limitations, and orbital dynamics. This paper proposes a two-stage optimization framework for ISL topology design. First, a continuous relaxation of the link selection problem is formulated as a convex program that maximizes the algebraic connectivity of the Laplacian, serving as a tractable surrogate for diameter minimization. Second, the resulting fractional solution is mapped to a feasible discrete topology using integer linear programming. An iterative local-search heuristic is also developed as a baseline. Extensive simulations on Walker-Delta constellations show that the proposed method consistently achieves smaller network diameters and improved robustness compared to conventional heuristics, while allowing trade-offs between latency and link persistence. The approach offers a principled framework for designing high-performance satellite mesh networks. For a constellation of 1,500 satellites, each equipped with four ISLs of up to 2,500 km, the network diameter can be reduced to as low as 12, yielding end-to-end delays under 90 ms between any two points on Earth.

  PublisherOA PDF

• How to Beat Nakamoto in the Race

  2025-11-19

  articleOpen accessSenior author

  This paper studies proof-of-work Nakamoto consensus protocols under bounded network delays, settling two long-standing questions in blockchain security: What is the most effective attack on block safety under a given block confirmation latency? And what is the resulting probability of safety violation? A Markov decision process (MDP) framework is introduced to precisely characterize the system state (including the blocktree and timings of all blocks mined), the adversary's potential actions, and the state transitions due to the adversarial action and the random block arrival processes. An optimal attack, called bait-and-switch, is proposed and proved to maximize the adversary's chance of violating block safety by ''beating Nakamoto in the race''. The exact probability of this violation is calculated for any given confirmation depth using Markov chain analysis, offering fresh insights into the interplay of network delay, confirmation rules, and blockchain security.

  PublisherDOI

• Multi-Agent Decision Transformer for Power Control in Wireless Networks

  2025-03-12 · 2 citations

  articleSenior author

  This paper introduces a novel offline approach to power control in wireless networks using a multi-agent reinforcement learning (MARL) framework. We develop a multi-agent decision transformer method to optimize performance metrics including sum-rate or packet delay. In this distributed method, each agent controls an individual link and determines its power level based on its own measurements and information exchange with a few agents within a limited neighborhood.Numerical results demonstrate that the proposed method achieves quality of service performance comparable to centralized methods using global information, for both sum-rate maximization and traffic-driven packet delay minimization problems. As an offline learning solution, it can efficiently leverage knowledge from existing mature techniques and offers significant advantages in the safety, stability, and convergence rate over existing online methods. This work provides a promising alternative for learning-based resource management in wireless networks.

  PublisherDOI

• Traffic-Aware Cellular User Association via Multi-Agent Reinforcement Learning

  2025-10-19

  articleSenior author

  The increasing density of cellular access points (APs) and user devices necessitates efficient user association strategies to balance traffic loads and ensure quality of service. This paper presents a traffic-aware user association framework leveraging multi-agent reinforcement learning (MARL), where APs cooperatively learn association policies based on network conditions and traffic demands. A dual-timescale architecture is empolyed: a slow timescale for association decisions and a fast timescale for power allocation and packet-level transmissions, enabling precise evaluation of network performance. Extensive simulations demonstrate that packet delays are significantly reduced by this approach compared to conventional methods, including user association with the nearest AP or association that maximizes the signal-to-noise-and-interference ratio, particularly in scenarios marked by high traffic, heterogeneous traffic distributions, or uneven user densities. The trained policies also show robust scalability across diverse network sizes and traffic conditions.

  PublisherDOI

• Security, Latency, and Throughput of Proof-of-Work Nakamoto Consensus

  IEEE Transactions on Information Theory · 2025-04-07 · 2 citations

  articleSenior author

  This paper investigates the fundamental trade-offs between block safety, confirmation latency, and transaction throughput of proof-of-work (PoW) longest-chain fork-choice protocols, also known as PoW Nakamoto consensus. New upper and lower bounds are derived for the probability of block safety violations as a function of honest and adversarial mining rates, a block propagation delay limit, and confirmation latency measured in both time and block depth. The results include the first non-trivial closed-form finite-latency bound applicable across all delays and mining rates up to the ultimate fault tolerance. Notably, the gap between these upper and lower bounds is narrower than previously established bounds for a wide range of parameters relevant to Bitcoin and its derivatives, including Litecoin and Dogecoin, as well as Ethereum Classic. Additionally, the study uncovers a fundamental trade-off between transaction throughput and confirmation latency, ultimately determined by the desired fault tolerance and the rate at which block propagation delay increases with block size.

  PublisherDOI

• Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation

  IEEE Transactions on Communications · 2025-01-27 · 5 citations

  articleSenior author

  Efficient and scalable radio resource allocation is essential for the success of wireless cellular networks. This paper presents a fully scalable multi-agent reinforcement learning (MARL) framework, where each agent manages spectrum, power allocation, and scheduling within a cell, using only locally available information. The objective is to minimize packet delays under stochastic traffic arrivals, applicable to both conflict graph models and cellular network configurations. This is formulated as a distributed learning problem and implemented using a multi-agent proximal policy optimization (MAPPO) algorithm. This traffic-driven MARL approach enables fully decentralized training and execution, ensuring scalability to arbitrarily large networks. Extensive simulations demonstrate that the proposed methods achieve quality of service (QoS) performance comparable to centralized algorithms that require global information, while the trained policies show robust scalability across diverse network sizes and traffic conditions.

  PublisherDOI

• Accelerating Multiuser Beamforming With Full-Dimension One-Bit Chains

  IEEE Journal of Selected Topics in Signal Processing · 2025-08-01

  articleSenior author

  Massive multiple-input multiple-output (MIMO) systems are vital for achieving high spectral efficiencies at mid-band and millimeter wave frequencies. Conventional hybrid MIMO architectures, which use fewer digital chains than antennas, offer a balance between performance, cost, and energy consumption but often prolong channel estimation. This paper proposes a novel architecture that integrates a set of full-dimension digital chains with one-bit analog-to-digital converters (ADCs) to overcome these limitations and provide an alternative trade-off. By assigning one digital chain to each receive antenna, the proposed approach captures energy from all receive antennas and accelerates angle-of-arrival (AoA) estimation and beam computation. Likelihood-based AoA estimation methods are developed to optimize analog beamforming in narrowband and wideband channels, in both single-user and multiuser scenarios. Numerical results, including the equivalent signal-to-noise ratio per bit post-equalization, demonstrate that full-dimension one-bit digital chains significantly improve the efficiency of beamforming.

  PublisherDOI

• Downlink Spectral Efficiency of Leo Satellite Constellations

  2025-06-22

  article

  This paper investigates the downlink spectral efficiency of low Earth orbit (LEO) satellite constellations, where spectral efficiency refers to the entire network's total data rate per unit spectrum per unit area on the Earth's surface. For practicality, all links employ single-user codebooks and treat interference as noise. A key finding is that, unlike terrestrial networks, the spectral efficiency of LEO constellations does not increase indefinitely with satellite density. Under typical assumptions about antenna array beam widths, this study explores the satellite density that maximizes spectral efficiency. As a special case, a regular deployment of satellites and ground terminals is analyzed across various densities. Simulation results reveal that regular configurations achieve higher spectral efficiency compared to random configurations. Furthermore, while the total downlink capacity of any LEO constellation remains significantly lower than that of terrestrial networks, there is substantial potential for growth-up to a few orders of magnitude-compared to current capacity levels.

  PublisherDOI

Recent grants

• CIF: Small: Wireless Massive Access: From Fundamental Limits to Practical Design

  NSF · $500k · 2019–2024

• Collaborative Research: Virtual Full-Duplex Wireless Networking

  NSF · $212k · 2012–2015

• CAREER: Information Transmission and Optimal Estimation: Fundamentals and Applications

  NSF · $400k · 2007–2013

• CIF: Small: Limited Feedback and Information Exchange for Wireless Systems

  NSF · $498k · 2010–2014

• CIF: Small: Many-User Information Theory: A New Paradigm

  NSF · $500k · 2014–2018

Frequent coauthors

• Abbas El Gamal

  99 shared

• Muriel Médard

  Massachusetts Institute of Technology

  99 shared

• Aylin Yener

  99 shared

• Marko Delimar

  University of Zagreb

  98 shared

• Devavrat Shah

  98 shared

• José M. F. Moura

  98 shared

• Young-Han Kim

  98 shared

• Dr Prendergast

  Institute of Electrical and Electronics Engineers

  98 shared

Labs

• Communications and Networking LaboratoryPI

Education

• Ph.D., Electrical Engineering

  Princeton University