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1. Octopus: In-Network Content Adaptation to Control Congestion on 5G Links

   Chen, Yongzhou; Tahir, Ammar; Yan, Francis; Mittal, Radhika (February 2024, ACM/IEEE)

   It is challenging to meet the bandwidth and latency requirements of interactive real-time applications (e.g., virtual reality, cloud gam- ing, etc.) on time-varying 5G cellular links. Today’s feedback-based congestion controllers try to match the sending rate at the endhost with the estimated network capacity. However, such controllers can- not precisely estimate the cellular link capacity that changes at timescales smaller than the feedback delay. We instead propose a different approach for controlling congestion on 5G links. We send real-time data streams using an imprecise controller (that errs on the side of overestimating network capacity) to ensure high through- put, and then adapt the transmitted content by dropping appropriate packets in the cellular base stations to match the actual capacity and minimize delay. We build a system called Octopus to realize this ap- proach. Octopus provides parameterized primitives that applications at the endhost can configure differently to express different content adaptation policies. Octopus transport encodes the corresponding app-specified parameters in packet header fields, which the base- station logic can parse to execute the desired dropping behavior. Our evaluation shows how real-time applications involving standard and volumetric videos can be designed to exploit Octopus, and achieve 1.5–18× better performance than state-of-the-art schemes. 

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2. Octopus: In-Network Content Adaptation to Control Congestion on 5G Links

   Chen, Yongzhou; Tahir, Ammar; Yan, Francis; Mittal, Radhika (February 2024, IEEEACM Symposium on Edge Computing)

   It is challenging to meet the bandwidth and latency requirements of interactive real-time applications (e.g., virtual reality, cloud gaming, etc.) on time-varying 5G cellular links. Today’s feedback-based congestion controllers try to match the sending rate at the endhost with the estimated network capacity. However, such controllers cannot precisely estimate the cellular link capacity that changes at timescales smaller than the feedback delay. We instead propose a different approach for controlling congestion on 5G links. We send real-time data streams using an imprecise controller (that errs on the side of overestimating network capacity) to ensure high throughput, and then adapt the transmitted content by dropping appropriate packets in the cellular base stations to match the actual capacity and minimize delay. We build a system called Octopus to realize this approach. Octopus provides parameterized primitives that applications at the endhost can configure differently to express different content adaptation policies. Octopus transport encodes the corresponding app-specified parameters in packet header fields, which the basestation logic can parse to execute the desired dropping behavior. Our evaluation shows how real-time applications involving standard and volumetric videos can be designed to exploit Octopus, and achieve 1.5–18× better performance than state-of-the-art schemes. 

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3. Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression

   https://doi.org/10.1109/TWC.2022.3202750  

   Del Rosario, Mason; Ding, Zhi (February 2023, IEEE transactions on wireless communications)

   Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network. 

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4. Time-Aware Packet Forwarding in Programmable Data Planes

   Song, Yuqun; Hasan, Monowar (May 2025, IEEE)

   Networks in many safety-critical systems like avionics, automotive, and industrial plants have strict end-to-end delay requirements to be met for correct system operation. Existing software-defined real-time networks do not support data plane programmability provided by recent protocol-independent switch architectures such as P4. Our research enables time-aware flow forwarding in P4-enabled software-defined time-critical networks. In this paper, we introduce time-aware flow scheduling for P4-enabled SDN architectures. We study two scheduling policies: the first one prioritizes flows based on slack (i.e., how much time is left to reach the destination), and the second one uses finish time as a priority metric, which is determined from its data rate requirements. Both approaches were implemented and tested in the P4 software stack. We find that the slack-based forwarding scheme performs better in retaining real-time requirements. Our publicly released scheduler implementations will assist network engineers in adapting programmable switches to safety-critical applications that demand precise timing guarantees. 

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5. Bounding Network-Induced Delays of Wireless PRP Infrastructure for Industrial Control Systems

   https://doi.org/10.1109/ICC.2019.8761893  

   Yang, Huan; Cheng, Liang (May 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC))

   Recently, wireless communication technologies, such as Wireless Local Area Networks (WLANs), have gained increasing popularity in industrial control systems (ICSs) due to their low cost and ease of deployment, but communication delays associated with these technologies make it unsuitable for critical real-time and safety applications. To address concerns on network-induced delays of wireless communication technologies and bring their advantages into modern ICSs, wireless network infrastructure based on the Parallel Redundancy Protocol (PRP) has been proposed. Although application-specific simulations and measurements have been conducted to show that wireless network infrastructure based on PRP can be a viable solution for critical applications with stringent delay performance constraints, little has been done to devise an analytical framework facilitating the adoption of wireless PRP infrastructure in miscellaneous ICSs. Leveraging the deterministic network calculus (DNC) theory, we propose to analytically derive worst-case bounds on network- induced delays for critical ICS applications. We show that the problem of worst-case delay bounding for a wireless PRP network can be solved by performing network-calculus-based analysis on its non-feedforward traffic pattern. Closed-form expressions of worst-case delays are derived, which has not been found previously and allows ICS architects/designers to compute worst- case delay bounds for ICS tasks in their respective application domains of interest. Our analytical results not only provide insights into the impacts of network-induced delays on latency- critical tasks but also allow ICS architects/operators to assess whether proper wireless RPR network infrastructure can be adopted into their systems. 

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