Learning-Based Adaptive Sliding-Window RLNC for High Bandwidth-Delay Product Networks

Shahzad,; Ali, Rashid; Haider, Amir; Kim, Hyung Seok

doi:10.1109/access.2023.3298102

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Fulcrum Coding Density and Channels With Blur Effect A Effic...1

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Cited by 4 publications

(1 citation statement)

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“…The probability of developing an innovative vector at the point of origin of the communication is achieved thanks to the density control in the coding matrix, i.e., the number of packets entering the coding process. The coding density is controllable because, when creating the linear coefficients, these are generated using a uniform distribution and the coding operator Xor [43].…”

Section: Fulcrum Coding Density and Channels With Blur Effect A Effic...mentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

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Section: Fulcrum Coding Density and Channels With Blur Effect A Effic...mentioning

confidence: 99%

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Julio,

Garcia,

Díaz

et al. 2023

IEEE Access

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RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet

Shahzad,

Ali,

Haider

et al. 2023

IEEE Access

Self Cite

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Tactile Internet (TI) has very stringent networking requirements and the transport layer plays a crucial role in meeting these requirements. However, the transport layer has several inherent limitations (e.g., bufferbloat, incast issue, and head of line blocking) due to which the performance of the current transport layer solutions is not optimal. We advocate replacing the "store-and-forward" strategy in transport layer solutions with the "compute-and-forward" strategy. One way to implement the "compute-and-forward" strategy is random linear network coding (RLNC). This paper proposes a learning-based RLNC framework called RS-RLNC that utilizes network and receiver feedback to optimally select between block-RLNC and sliding-RLNC to improve overall network performance. We present a simulation-based performance evaluation of current transport layer solutions against the state-of-the-art RLNC and RS-RLNC in terms of throughput, latency, and decoding complexity. Delay is reduced by a factor of 8.5% and decoding complexity is improved up to 20% compared to the state-of-the-art. Simulation results indicate that RS-RLNC has the potential to meet the stringent requirements of TI applications. Additionally, we present three future directions outlining the evolution of RS-RLNC to enhance the transport layer for TI compatibility.

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Survey on TCP Congestion Control in Ad Hoc Wireless Networks

Chongnan,

Zhongda

2024

2024 36th Chinese Control and Decision Conference (CCDC)

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Learning-Based Adaptive Sliding-Window RLNC for High Bandwidth-Delay Product Networks

Cited by 4 publications

References 26 publications

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

Middleware for Distributed Fulcrum Coding on the Fly: Leveraging Efficient Communications for 5G-IoT Heterogeneous Mobile Devices

RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet

Survey on TCP Congestion Control in Ad Hoc Wireless Networks

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