arXiv subjects

Dongning Guo

Publications and source records attributed to Dongning Guo.

At least 19 recordsLinked to original sources

arXiv2026

Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Management

This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrained offline via supervised sequence modeling of trajectories generated by existing policies, providing a safe and sample-efficient initialization. It is then fine-tuned online using a hybrid objective that incorporates critic-guided gradients, enabling performance improvements beyond the offline policy. To facilitate stable offline-to-online transfer and effective multi-agent coordination, the framework incorporates return-weighted sampling, a critic conditioned on neighbors' actions, and neighborhood-correlated exploration. The approach is fully distributed: both training and execution rely only on local observations and limited information exchange among neighboring agents. Evaluations with dynamic traffic arrivals in two settings: (i) joint scheduling and power allocation and (ii) coordinated beamforming, show that the proposed method achieves quality-of-service (QoS) performance comparable to centralized methods. Moreover, when pretrained on lower-quality datasets, online fine-tuning is also observed to surpass the initial offline policy. These results demonstrate a promising learning-based alternative for wireless resource management.

eess.SY↗

arXiv2026

Bitcoin Mempool Linearization

In the Bitcoin system, transactions arrive continuously at miners' mempools and await inclusion in future blocks. Every non-coinbase transaction must spend one or more unspent outputs created by previous transactions, inducing dependency constraints among transactions in the mempool. At the same time, miners are economically incentivized to prioritize transactions with higher fee rates, measured as transaction fee per unit size. This paper formulates the mempool linearization problem: given a set of transactions with associated fees, sizes, and dependency relationships, compute a dependency-respecting transaction ordering that maximizes fee-rate efficiency while supporting efficient updates as the mempool evolves dynamically. The problem is characterized through a partition of transactions into disjoint dependency-respecting subsets ordered by decreasing aggregate fee rate, together with an equivalent linear programming formulation. Motivated by structural properties of basic feasible solutions in the simplex method, a new algorithm called spanning forest linearization (SFL) is developed. Operating directly on the transaction dependency graph, SFL iteratively merges and splits chunks of transactions to refine a global ordering, and is guaranteed to terminate at an optimal solution. Evaluation on both synthetic and real-world Bitcoin mempool data shows that SFL consistently computes optimal linearizations with substantially lower runtime than competing approaches, including a method based on the parametric preflow algorithm of Gallo, Grigoriadis, and Tarjan. These results indicate that SFL provides a practical and scalable framework for transaction prioritization by decentralized miners in large and rapidly evolving mempools. SFL has also been incorporated into the Bitcoin Core codebase for transaction cluster linearization.

cs.DS↗

arXiv2026

Inter-Satellite Link Optimization for Low-Latency Global Networking

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.

cs.NI↗

arXiv2026

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

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 long-term link viability over orbital periods. In this study, the selection and assignment of inter-plane ISLs is formulated as a diameter-minimization 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 that 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.

cs.NI↗

arXiv2025

How to Beat Nakamoto in the Race

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.

cs.CR↗

arXiv2024

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

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.

cs.CR↗

arXiv2024

Spectral Efficiency of Low Earth Orbit Satellite Constellations

This paper investigates the maximum achievable downlink spectral efficiency of low Earth orbit (LEO) satellite constellations. Spectral efficiency is defined here as the total network sum rate per unit bandwidth per unit area of Earth's surface. To estimate an upper bound on spectral efficiency, the problem is reduced to a single-channel network model, where all satellites and ground terminals operate over a common narrowband frequency channel. Within this model, a regular benchmark configuration is proposed and analyzed, with satellites and terminals arranged in hexagonal lattices. Numerical results validate that this configuration provides an upper bound on spectral efficiency for multi-channel LEO networks when satellite-terminal associations minimize the total squared link distance. Further improvements are achievable by adjusting association rules to prevent neighboring satellites from simultaneously serving terminals in the same region, highlighting the critical role of interference-aware association strategies.

eess.SP↗

arXiv2024

Fault-Tolerant Spectrum Usage Consensus for Low-Earth-Orbit Satellite Constellations

Operators of low-Earth-orbit (LEO) non-geostationary satellite networks, also known as mega-constellations, are required by current regulations to share all available satellite spectrum. This paper proposes a consensus mechanism to facilitate spectrum sharing with accountability by multiple operators, a subset of which may even be adversarial. A distributed ledger is used to securely record and track the state of consensus on spectrum usage, including interference incidents and the corresponding responsible parties. A key challenge is that operators generally do not have initial agreement due to noise in their analog measurements. To address this, two categories of spectrum-sharing solutions are studied in detail. The first category employs an exact Byzantine fault tolerant (BFT) agreement model; the second category utilizes an approximate BFT agreement model. Practical considerations were taken into account regarding the BFT agreements, substantiated by numerical findings on the feasibility of the proposed solutions within the context of non-geostationary orbit satellite networks (NGSO).

cs.NI↗

arXiv2024

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

This paper introduces a novel approach to radio resource allocation in multi-cell wireless networks using a fully scalable multi-agent reinforcement learning (MARL) framework. A distributed method is developed where agents control individual cells and determine spectrum and power allocation based on limited local information, yet achieve quality of service (QoS) performance comparable to centralized methods using global information. The objective is to minimize packet delays across devices under stochastic arrivals and applies to both conflict graph abstractions and cellular network configurations. This is formulated as a distributed learning problem, implementing a multi-agent proximal policy optimization (MAPPO) algorithm with recurrent neural networks and queueing dynamics. This traffic-driven MARL-based solution enables decentralized training and execution, ensuring scalability to large networks. Extensive simulations demonstrate that the proposed methods achieve comparable QoS performance to genie-aided centralized algorithms with significantly less execution time. The trained policies also exhibit scalability and robustness across various network sizes and traffic conditions.

cs.MA↗

arXiv2023

Reducing Satellite Interference to Radio Telescopes Using Beacons

This paper proposes the transmission of beacon signals to alert potential interferers of an ongoing or impending passive sensing measurement. We focus on the interference from Low-Earth Orbiting (LEO) satellites to a radio-telescope. We compare the beacon approach with two versions of Radio Quiet Zones (RQZs): fixed quiet zones on the ground and in the sky, and dynamic quiet zones that vary across satellites. The beacon-assisted approach can potentially exploit channel reciprocity, which accounts for short-term channel variations between the satellite and radio telescope. System considerations associated with beacon design and potential schemes for beacon transmission are discussed. The probability of excessive Radio Frequency Interference (RFI) at the radio telescope (outage probability) and the fraction of active links in the satellite network are used as performance metrics. Numerical simulations compare the performance of the approaches considered, and show that the beacon approach enables more active satellite links relative to quiet zones for a given outage probability.

eess.SP↗

arXiv2023

RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet

Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband Radio Service (CBRS). In this paper, we propose a supervised deep learning-based spectrum sensing approach called RadYOLOLet that can detect low-power radar signals in the presence of interference and estimate the radar signal parameters. The core of RadYOLOLet is two different convolutional neural networks (CNN), RadYOLO and Wavelet-CNN, that are trained independently. RadYOLO operates on spectrograms and provides most of the capabilities of RadYOLOLet. However, it suffers from low radar detection accuracy in the low signal-to-noise ratio (SNR) regime. We develop Wavelet-CNN specifically to deal with this limitation of RadYOLO. Wavelet-CNN operates on continuous Wavelet transform of the captured signals, and we use it only when RadYOLO fails to detect any radar signal. We thoroughly evaluate RadYOLOLet using different experiments corresponding to different types of interference signals. Based on our evaluations, we find that RadYOLOLet can achieve 100% radar detection accuracy for our considered radar types up to 16 dB SNR, which cannot be guaranteed by other comparable methods. RadYOLOLet can also function accurately under interference up to 16 dB SINR.

eess.SP↗

arXiv2023

ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning

Spatial prediction of the radio propagation environment of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be categorized as 'reactive' spatial prediction, where the predictions are made based on a small set of measurements from an active transmitter whose radio environment is to be predicted. Emerging spectrum-sharing paradigms would benefit from 'proactive' spatial prediction of the radio environment, where the spatial predictions must be done for a transmitter for which no measurement has been collected. This paper proposes a novel, supervised deep learning-based framework, ProSpire, that enables spectrum sharing by leveraging the idea of proactive spatial prediction. We carefully address several challenges in ProSpire, such as designing a framework that conveniently collects training data for learning, performing the predictions in a fast manner, enabling operations without an area map, and ensuring that the predictions do not lead to undesired interference. ProSpire relies on the crowdsourcing of transmitters and receivers during their normal operations to address some of the aforementioned challenges. The core component of ProSpire is a deep learning-based image-to-image translation method, which we call RSSu-net. We generate several diverse datasets using ray tracing software and numerically evaluate ProSpire. Our evaluations show that RSSu-net performs reasonably well in terms of signal strength prediction, 5 dB mean absolute error, which is comparable to the average error of other relevant methods. Importantly, due to the merits of RSSu-net, ProSpire creates proactive boundaries around transmitters such that they can be activated with 97% probability of not causing interference. In this regard, the performance of RSSu-net is 19% better than that of other comparable methods.

cs.NI↗

arXiv2023

An Asynchronous Massive Access Scheme with Dynamic Range Considerations

This paper studies the performance of a transmission and reception scheme for massive access under some practical challenges. One challenge is the near-far problem, i.e., an access point often receives signals from different transmitting devices at vastly different signal strengths. Another challenge is that the signals from different devices may be subject to arbitrary, analog, and heterogeneous delays. This paper considers a fully asynchronous model which is more realistic than the frame or symbol level synchrony needed in most existing work. A main theorem characterizes the asymptotic scaling of the codelength with the number of devices, a device delay upper bound, and the dynamic range of received signal strengths across devices. The scaling result suggests potential advantages of grouping devices with similar received signal strengths and letting the groups use time sharing. The performance of the proposed scheme is evaluated using simulations with and without grouping.

cs.IT↗

arXiv2022

Bitcoin's Latency--Security Analysis Made Simple

Simple closed-form upper and lower bounds are developed for the security of the Nakamoto consensus as a function of the confirmation depth, the honest and adversarial block mining rates, and an upper bound on the block propagation delay. The bounds are exponential in the confirmation depth and apply regardless of the adversary's attack strategy. The gap between the upper and lower bounds is small for Bitcoin's parameters. For example, assuming an average block interval of 10 minutes, a network delay bound of ten seconds, and 10% adversarial mining power, the widely used 6-block confirmation rule yields a safety violation between 0.11% and 0.35% probability.

cs.CR↗

arXiv2021

Asynchronous Massive Access and Neighbor Discovery Using OFDMA

The fundamental communication problem in the wireless Internet of Things (IoT) is to discover a massive number of devices and to allow them reliable access to shared channels. Oftentimes these devices transmit short messages randomly and sporadically. This paper proposes a novel signaling scheme for grant-free massive access, where each device encodes its identity and/or information in a sparse set of tones. Such transmissions are implemented in the form of orthogonal frequency-division multiple access (OFDMA). Under some mild conditions and assuming device delays to be bounded unknown multiples of symbol intervals, sparse OFDMA is proved to enable arbitrarily reliable asynchronous device identification and message decoding with a codelength that is O(K(log K + log S + log N)), where N denotes the device population, K denotes the actual number of active devices, and log S is essentially equal to the number of bits a device can send (including its identity). By exploiting the Fast Fourier Transform (FFT), the computational complexity for discovery and decoding can be made to be sub-linear in the total device population. To prove the concept, a specific design is proposed to identify up to 100 active devices out of $2^{38}$ possible devices with up to 20 symbols of delay and moderate signal-to-noise ratios and fading. The codelength compares much more favorably with those of standard slotted ALOHA and carrier-sensing multiple access (CSMA) schemes.

cs.IT↗

arXiv2021

Asynchronous Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes

Providing connectivity to a massive number of devices is a key challenge in 5G wireless systems. In particular, it is crucial to develop efficient methods for active device identification and message decoding in a multi-cell network with fading, path loss, and delay uncertainties. This paper presents such a scheme using second-order Reed-Muller (RM) sequences and orthogonal frequency-division multiplexing (OFDM). For given positive integer $m$, a codebook is generated with up to $2^{m(m+3)/2}$ codewords of length $2^m$, where each codeword is a unique RM sequence determined by a matrix-vector pair with binary entries. This allows every device to send $m(m + 3)/2$ bits of information where an arbitrary number of these bits can be used to represent the identity of a node, and the remaining bits represent a message. There can be up to $2^{m(m+3)/2}$ different identities. Using an iterative algorithm, an access point can estimate the matrix-vector pairs of each nearby device, as long as not too many devices transmit in the same frame. It is shown that both the computational complexity and the error performance of the proposed algorithm exceed another state-of-the-art algorithm. The device identification and message decoding scheme developed in this work can serve as the basis for grant-free massive access for billions of devices with hundreds of simultaneously active devices in each cell.

cs.IT↗

arXiv2021

Close Latency--Security Trade-off for the Nakamoto Consensus

Bitcoin is a peer-to-peer electronic cash system invented by Nakamoto in 2008. While it has attracted much research interest, its exact latency and security properties remain open. Existing analyses provide security and latency (or confirmation time) guarantees that are too loose for practical use. In fact the best known upper bounds are several orders of magnitude larger than a lower bound due to a well-known private-mining attack. This paper describes a continuous-time model for blockchains and develops a rigorous analysis that yields close upper and lower bounds for the latency--security trade-off. For example, when the adversary controls 10\% of the total mining power and the block propagation delays are within 10 seconds, a Bitcoin block is secured with less than $10^{-3}$ error probability if it is confirmed after four hours, or with less than $10^{-9}$ error probability if confirmed after ten hours. These confirmation times are about two hours away from their corresponding lower bounds. To establish such close bounds, the blockchain security question is reduced to a race between the Poisson adversarial mining process and a renewal process formed by a certain species of honest blocks. The moment generation functions of relevant renewal times are derived in closed form. The general formulas from the analysis are then applied to study the latency--security trade-off of several well-known proof-of-work longest-chain cryptocurrencies. Guidance is also provided on how to set parameters for different purposes.

cs.CR↗

arXiv2021

A New Distributed Method for Training Generative Adversarial Networks

Generative adversarial networks (GANs) are emerging machine learning models for generating synthesized data similar to real data by jointly training a generator and a discriminator. In many applications, data and computational resources are distributed over many devices, so centralized computation with all data in one location is infeasible due to privacy and/or communication constraints. This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN. Specifically, in each iteration, the server sends the global GAN to the devices, which then update their local discriminators; the devices send their results to the server, which then computes their average as the global discriminator and updates the global generator accordingly. Two different update schedules are designed with different levels of parallelism between the devices and the server. Numerical results obtained using three popular datasets demonstrate that the proposed framework can outperform a state-of-the-art framework in terms of convergence speed.

cs.LG↗