Traffic-Estimation-Based Low-Latency XGS-PON Mobile Front-Haul for Small-Cell C-RAN Based on an Adaptive Learning Neural Network

Mikaeil, Ahmed Mohammed; Hu, Weisheng; Hussain, Syed Baqar; Sultan, Amber

doi:10.3390/app8071097

Cited by 20 publications

(10 citation statements)

References 25 publications

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“…In recent years, several PON-related studies have been published. The authors in [12] describe a novel method for low-latency 10-Gigabit-capable symmetric passive optical network (XGS-PON) mobile front-haul for small cell in cloud radio access network (C-RAN) based on traffic estimation. They proposed the adaptive-Learning dynamic bandwidth allocation (DBA), which reduces jitter in optical distribution network (ODN), packet loss ratio, delay, and increasing utilization performance.…”

Section: Related Workmentioning

confidence: 99%

MongoDB Database as Storage for GPON Frames

Holík

Horváth

Oujezský

et al. 2020

Sensors

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This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.

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Section: Related Workmentioning

confidence: 99%

MongoDB Database as Storage for GPON Frames

Holík

Horváth

Oujezský

et al. 2020

Sensors

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“…Sudden demand surges for backhaul capacity from the wireless devices and the corresponding radio nodes, e.g., the LTE enhanced Node Bs (eNBs) and Wi-Fi access points (APs), of one operator may overwhelm the operator's backhaul capacity and result in poor service quality, and ultimately, reduced revenue. Overall, with the advances in the wireless transmission capacities, the backhaul has emerged as a critical bottleneck of novel high-capacity wireless networks, such as small cell networks and 5G networks [1][2][3][4][5][6][7].…”

Section: Motivationmentioning

confidence: 99%

“…. , O} for the constraints in (4), and the Lagrangian dual variables {λ y g : g ∈ G} for the constraints in (5). Then, unfolding all the constraints, we obtain (10) and following a cascade of primal dual decompositions (see [42]), the optimization can be solved via the sequence of projected gradient descent updates:…”

Section: Iterative Solution Via Gradient Descentmentioning

confidence: 99%

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

et al. 2019

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With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

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“…The second paper, authored by Mikaei et al and titled "Traffic Estimation-Based Low-Latency XGS-PON Mobile Fronthaul for Small Cell C-RAN Based on Adaptive Learning Neural Network" [6], proposes and investigates a solution to guarantee low latency in fronthaul networks realized in Passive Optical Network (PON) technology. The proposed technique is based on the prediction of packets arriving at the Optical Network Unit (ONU) buffer from the Remote Radio Unit (RRU) using an adaptive learning neural network function integrated into the Dynamic Bandwidth Allocation (DBA) module at the optical line terminal (OLT).…”

Section: Contributionsmentioning

confidence: 99%