TCP-Based M2M Traffic via Random-Access Satellite Links: Throughput Estimation

Bacco, Manlio; Cola, Tomaso de; Giambene, Giovanni; Gotta, Alberto

doi:10.1109/taes.2018.2865150

Cited by 24 publications

(9 citation statements)

References 31 publications

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“…By using CRSDA, a closedloop link congestion control was designed. The throughput estimation model facilitating satellite random access can accurately adapt to the satellite scenario's simulation [16]. As a promising technology, deep reinforcement learning (DRL) has also been proposed to be applied to congestion control in the satellite IoT networks [17].…”

Section: A Motivations and Contributionsmentioning

confidence: 99%

Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks

Liu

Zhang

Long

et al. 2022

IEEE Internet Things J.

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Random access schemes in satellite Internet of Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this paper. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-Q learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms' performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.

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Section: A Motivations and Contributionsmentioning

confidence: 99%

Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks

Liu

Zhang

Long

et al. 2022

IEEE Internet Things J.

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“…In [7], the authors evaluated the quality of video streaming applications using TCP over satellite links. The interactions of TCP at the transport layer and random access schemes at the Media Access Control (MAC) layer for machine-to-machine services over the satellite links were investigated in [8]. In [9], the performance of multi-path TCP was analyzed through a satellite network, which contains several WiFi access points connected to a server together with a convoy of drones for providing Internet access.…”

Section: A Literature Review and Motivationsmentioning

confidence: 99%

“…Given [γ k , γ k+1 ), the average bit error rate (BER) at the channel-state k-th can be computed as FSO model: Search γ rn that satisfiesτ rn = c r T r ≤ t cr 8:…”

Section: ) Channel-state Modelingmentioning

confidence: 99%

TCP Performance Over Satellite-Based Hybrid FSO/RF Vehicular Networks: Modeling and Analysis

Nguyen

et al. 2021

IEEE Access

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Recent years have witnessed a growing interest in Internet access from space assisted by low earth orbit (LEO) satellite networks. In the domain of the last-mile access for the Internet of Vehicles (IoV), hybrid free-space optical (FSO)/radio-frequency (RF) communication has recently attracted worldwide research efforts. While the transmission control protocol (TCP) is the most widely deployed transport protocol on the Internet, its performance in the error-prone environment of LEO satellite-assisted hybrid FSO/RF vehicular networks is not well understood. This paper develops a comprehensive analytical model based on the cross-layer approach for TCP performance, considering the FSO and RF satellite fading channels, modeled by the Gamma-Gamma and Nakagami-m distributions, respectively. The error-control solutions, including the Reed-Solomon (RS) code and Selective repeat automatic repeat request (SR-ARQ), are also employed. Numerical results quantitatively demonstrate the impact of transmission errors at lastmile links and different parameters/settings of error-control solutions on the TCP performance. The paper also supports the selection of proper TCP variants for the considered networks.INDEX TERMS Satellite networks, hybrid FSO/RF last-mile, Internet of vehicles, Transmission control protocols, Error-control solutions.

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“…The estimation scheme in [8] (respectively, [9], [10]) uses the 6-DMC (respectively, Maximum Likelihood Estimation (MLE), M2M-OSA, an extension of the opportunistic splitting algorithm (OSA)) based estimation scheme, whereas in our work, we use the SRC S based estimation scheme [18]. A satellite random access (RA) MAC protocol is proposed in [11], wherein an estimate of the number of Return Channel Satellite Terminals (RCSTs) is computed and used in throughput maximization. The length of the current frame in the model in [11] depends on the number of collisions in the previous frames, whereas in our model, the length of each frame is fixed and constant.…”

Section: Related Workmentioning

confidence: 99%

Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks

Kadam,

Y.,

Prasad

et al. 2019

Preprint

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TCP-Based M2M Traffic via Random-Access Satellite Links: Throughput Estimation

Cited by 24 publications

References 31 publications

Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks

Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks

TCP Performance Over Satellite-Based Hybrid FSO/RF Vehicular Networks: Modeling and Analysis

Rapid Node Cardinality Estimation in Heterogeneous Machine-to-Machine Networks

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