Zero-jitter differentiated traffic scheduling algorithm in 5G fronthaul hybrid network

Wu, Luyi; Gan, Chaoqin; Xu, Zhongsen; Hui, Jianqiang

doi:10.1016/j.ijleo.2022.169558

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…Without a scheduling algorithm in place, the switch defaults to the FIFO scheme, forwarding packets based on their arrival times. This default mechanism results in high interpacket delay variations, reaching a jitter of 3 µs [22]. This value falls significantly short of meeting the fronthaul requirements of 64 ns.…”

Section: B Proposed Congestion Control and Resource Allocation (Ccra)...mentioning

confidence: 99%

“…This section provides an evaluation of the performance of the proposed CCRA scheme, focusing on E2E delays and jitter. These parameters are computed using Equations 1 to 9 and based on the studies [14], [21] and [22]. The metrics are implemented using the Java programs.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…IEEE 1914.3 [19] and IEEE P802.1CM [20] propose the mechanism and standards to carry the multiple timesensitive traffic streams over the Ethernet fronthaul networks. Like our proposed scheme, the studies CFIT [21] and DTSA [22] implemented the fronthaul scenarios based on the 3GPP [7] and IEEE standards [19] [20]. A study [21] proposes a scheduling algorithm named CFIT to transmit multiple common public radio interface (CPRI) streams over a single switching network.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, a considerable number of conflicting slots are assigned to non-multiple flows, causing increased congestion, and subsequently leading to heightened end-to-end delays and jitter. In a recent study [22] another scheduling algorithm, DTSA has been proposed to prioritize and allocate network resources depending on traffic characteristics. This algorithm exhibits low jitter only when high-capacity optical links are utilized with multiple wavelength calculations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Improvement of Time-Sensitive Fronthaul Networks in 5G Cloud-RANs Using Reinforcement Learning-Based Scheduling Scheme

Waqar,

Mustafa,

Jabeen

et al. 2024

IEEE Access

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The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (CRANs) are considered the prominent solution to meet the stringent requirements of fifth generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio units (RRHS). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in CRANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated a significant enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.

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Section: B Proposed Congestion Control and Resource Allocation (Ccra)...mentioning

confidence: 99%