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1. Low-Latency Short-Packet Transmission over a Large Spatial Scale

   https://doi.org/10.3390/e23070916  

   Huang, Lei; Zhao, Xiaoyu; Chen, Wei; Poor, H. Vincent (July 2021, Entropy)

   Short-packet transmission has attracted considerable attention due to its potential to achieve ultralow latency in automated driving, telesurgery, the Industrial Internet of Things (IIoT), and other applications emerging in the coming era of the Six-Generation (6G) wireless networks. In 6G systems, a paradigm-shifting infrastructure is anticipated to provide seamless coverage by integrating low-Earth orbit (LEO) satellite networks, which enable long-distance wireless relaying. However, how to efficiently transmit short packets over a sizeable spatial scale remains open. In this paper, we are interested in low-latency short-packet transmissions between two distant nodes, in which neither propagation delay, nor propagation loss can be ignored. Decode-and-forward (DF) relays can be deployed to regenerate packets reliably during their delivery over a long distance, thereby reducing the signal-to-noise ratio (SNR) loss. However, they also cause decoding delay in each hop, the sum of which may become large and cannot be ignored given the stringent latency constraints. This paper presents an optimal relay deployment to minimize the error probability while meeting both the latency and transmission power constraints. Based on an asymptotic analysis, a theoretical performance bound for distant short-packet transmission is also characterized by the optimal distance–latency–reliability tradeoff, which is expected to provide insights into designing integrated LEO satellite communications in 6G. 

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2. Robust Lyapunov Optimization for Multihop Communication in LEO Satellite Networks

   https://doi.org/10.1109/WiSEE61249.2024.10850167  

   Huang, Zhemin; Jiang, Zhong-Ping; Han, Zhu; Liu, Yong (December 2024, IEEE)

   With the development of space-air-ground integrated networks, Low Earth Orbit (LEO) satellite networks are envisioned to play a crucial role in providing data transmission services in the 6G era. However, the increasing number of connected devices leads to a surge in data volume and bursty traffic patterns. Ensuring the communication stability of LEO networks has thus become essential. While Lyapunov optimization has been applied to network optimization for decades and can guarantee stability when traffic rates remain within the capacity region, its applicability in LEO satellite networks is limited due to the bursty and dynamic nature of LEO network traffic. To address this issue, we propose a robust Lyapunov optimization method to ensure stability in LEO satellite networks. We theoretically show that for a stabilizable network system, traffic rates do not have to always stay within the capacity region at every time slot. Instead, the network can accommodate temporary capacity region violations, while ensuring the long-term network stability. Extensive simulations under various traffic conditions validate the effectiveness of the robust Lyapunov optimization method, demonstrating that LEO satellite networks can maintain stability under finite violations of the capacity region. 

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3. Detection of Denial-of-Service Attacks in a Software-Defined LEO Constellation Network

   Agnew, Dennis; McNair, Janise (March 2023, Government Microcircuit Applications and Critical Technology Conference (GOMAC Tech))

   Satellite communication (SATCOM) is a critical infrastructure for tactical networks--especially for the intermittent communication of submarines. To ensure data reliability, recent SATCOM research has begun to embrace several advances, such as low earth orbit (LEO) satellite networks to reduce latency and increase throughput compared to long-distance geostationary (GEO) satellites, and software-defined networking (SDN) to increase network control and security. This paper proposes an SD-LEO constellation for submarines in communication networks. An SD-LEO architecture is proposed, to Denial-of-Service (DoS) attack detection and classification using the extreme gradient boosting (XGBoost) algorithm. Numerical results demonstrate greater than ninety-eight percent in accuracy, precision, recall, and F1-scores. 

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4. Time-Shift Coding for Uncoordinated MACs

   https://doi.org/10.1109/LATINCOM59467.2023.10361874  

   Yuan, Peihong; Duffy, Ken R; Médard, Muriel (November 2023, IEEE LatinAmerican Conference on Communications)

   In this work, we discuss time-shift coding and GRAND ZigZag decoding for uncoordinated multiple access channels (MACs). Time-shift coding relies on the delay difference of the received packets to prevent fully overlapped collisions. Users transmit with different time-shifts in designated time slots until the receiver successfully decodes all messages. At the receiver side, ZigZag decoding is employed to separate packets with linear complexity in noiseless scenarios. For cases with noise, a guessing random additive noise decoding (GRAND)-based algorithm is utilized to identify the most probable noise vector in conjunction with ZigZag decoding. Simulation results demonstrate that time-shift coding significantly reduces collision probabilities, thereby enhancing system throughput in noiseless scenarios. In the context of Gaussian MAC, the GRAND ZigZag decoding method outperforms successive interference cancellation (SIC)-based schemes in high signal-to-noise ratio (SNR) regimes. 

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5. Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate

   Jafri, Syed Usman; Rao, Sanjay; Shrivastav, Vishal; Tawarmalani, Mohit (April 2024, USENIX Symposium on Networked Systems Design and Implementation (NSDI))

   Online traffic classification enables critical applications such as network intrusion detection and prevention, providing Quality-of-Service, and real-time IoT analytics. However, with increasing network speeds, it has become extremely challenging to analyze and classify traffic online. In this paper, we present Leo, a system for online traffic classification at multi-terabit line rates. At its core, Leo implements an online machine learning (ML) model for traffic classification, namely the decision tree, in the network switch's data plane. Leo's design is fast (can classify packets at switch's line rate), scalable (can automatically select a resource-efficient design for the class of decision tree models a user wants to support), and runtime programmable (the model can be updated on-the-fly without switch downtime), while achieving high model accuracy. We implement Leo on top of Intel Tofino switches. Our evaluations show that Leo is able to classify traffic at line rate with nominal latency overhead, can scale to model sizes more than twice as large as state-of-the-art data plane ML classification systems, while achieving classification accuracy on-par with an offline traffic classifier. 

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