Self-Adjusting DBA Algorithm for Next Generation PONs (NG-PONs) to Support 5G Fronthaul and Data Services

Zaouga, Aziza; Sousa, Amaro de; Najjar, Monia; Monteiro, Paulo P.

doi:10.1109/jlt.2020.3044704

Cited by 22 publications

(4 citation statements)

References 27 publications

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“…However, the native transport of digital RoF signals in conventional PON systems, such as EPON and xGPON, presents several issues, in terms of data capacity, latency and jitter, usually not compatible with the fronthaul requirements [52,53]. To overcome these issues, the optical communication group at the Instituto de Telecomunicações, Aveiro has investigated and tested different solutions including mobile fronthaul RoF Transceivers for point-to-point wavelength overlay channel (PtP WDM PON) [53][54][55][56] and Dynamic Bandwidth Allocation (DBA) algorithms for next-generation PONs to support 5G Fronthaul services [57,58]. The Mobile Laboratory Setup based on OAI implemented in infrastructure ORCIP (Optical Radio Convergence Infrastructure for Communications and Power Delivering) [59] is an important tool to study and experimentally validate different X-haul options.…”

Section: Emulation Of Monolithic Baseband Processingmentioning

confidence: 99%

SDR Enabled C-RAN Implementation using OpenAirInterface

Mufutau

Guiomar

Oliveira

et al. 2021

Preprint

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Towards enabling 5G radio access technologies and beyond to meet the requirements for continuous dynamic and diverse services, flexibility and scalability of the cellular network are therefore pertinent. The utilization of software-defined radio (SDR) aided with an open-source platform and virtualization techniques are increasingly exposing the realization of desirable flexibility for radio access network (RAN) while enabling the development of a prototype which can be directed at fostering further mobile network research activities. In this paper, we review OpenAirInterface (OAI) implementation and present an OAI based cloud RAN (C-RAN) testbed with which mobile fronthaul (MFH) solutions can be tested.

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Section: Emulation Of Monolithic Baseband Processingmentioning

confidence: 99%

SDR Enabled C-RAN Implementation using OpenAirInterface

Mufutau

Guiomar

Oliveira

et al. 2021

Preprint

Self Cite

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“…Meanwhile, with the advent of the 5G era, more and more delay-sensitive applications such as vehicular platooning, immersive virtual reality (VR), augmented reality (AR), and e-health are expected to be delivered over future access networks. These delaysensitive applications can have a latency requirement as low as ~1 ms [7]- [9]. As conventional energy consumption reduction methods for access networks (e.g., by adopting power saving mode) usually compromise the latency performance, how to deal with the issue to improve the energy efficiency of access networks while satisfying a ~1 ms latency performance, has become a key enabler to facilitate the healthy development of future access networks.…”

Section: Introductionmentioning

confidence: 99%

Bandwidth Prediction Based Resource Allocation Scheme for Low-Latency and Energy-Efficient PONs With Heterogeneous ONU Propagation Delays

Li,

Lv,

2024

IEEE Photonics J.

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To achieve low-latency and energy-efficient in a 10Gbps Ethernet Passive Optical Network (10G-EPON) having heterogeneous Optical Network Unit (ONU) propagation delays, this paper proposes a resource allocation scheme based on bandwidth requirements prediction and ONU polling sequence scheduling. By (a) predicting the upstream (US) and downstream (DS) bandwidth requirements of each ONU using Long Short-Term Memory (LSTM) networks and (b) arranging the ONU polling sequence and the US and DS transmission start times of each ONU based on the predicted bandwidth requirements and the ONU propagation delays, the proposed scheme minimizes bandwidth waste of the network as well as the overall operating time of each ONU. Therefore, low-latency performance is achieved, and the energy consumption of ONUs is reduced as ONU sleeping time is extended. Simulation results indicate that the proposed scheme reduces network latency and improves energy efficiency effectively. Compared to the conventional Interleaved Polling with Adaptive Cycle Time scheme that adopts the shortest propagation delay first rule and ONU doze mode, the proposed scheme can reduce the average one-way packet delay by at least 28.9%, and can reduce the per bit energy consumption by 73.7% when concerning data transmitted under the 1ms latency constraint.

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“…Also, in [19], the authors proposed a novel wavelength and bandwidth allocation algorithm that could minimize the required number of active wavelength channels, considering the high burstiness and delay requirement of the fronthaul data transmission. Besides, to jointly support 5G fronthaul and best-effort data services in the same PON channel, authors in [20] proposed a novel dynamic bandwidth allocation (DBA) algorithm for NG-PON networks, reducing the management coordination requirement. A wavelength and bandwidth preemption mechanism was further introduced in [21] to reduce the latency in a fixed and mobile converged TWDM-PON.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

Cheng,

Shao,

Ding

et al. 2024

IEEE Photonics J.

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In next-generation centralized or cloud radio access networks (C-RANs), time and wavelength division multiplexed passive optical network (TWDM-PON) has been well recognized as a promising candidate to build the mobile fronthaul. Considering the stringent bandwidth efficiency, latency, and cost requirements in C-RAN, an efficient bandwidth and wavelength allocation scheme is highly desirable for TWDM-PON-based fronthaul. Especially for the massive multiple input multiple outputs (mMIMO) enabled beamforming scenario, the additional radio resource is required to be jointly allocated with bandwidth and wavelength resources in TWDM-PON. In this paper, we formulate the joint allocation problem into an integer linear programming mathematical model and propose a deep reinforcement learning (RL)-based joint allocation scheme with an energy-efficient architecture for the TWDM-PON-based mMIMO fronthaul network. The proposed scheme couples the heuristic radio resource allocation algorithm with the RL-based wavelength allocation model to optimize the fronthaul bandwidth, radio resource, and wavelength utilization efficiencies jointly in the downstream direction. Simulation results show that the proposed scheme achieves a high bandwidth efficiency and high radio resource block utilization simultaneously across different traffic loads and, meanwhile, reduces the wavelength usage compared with the benchmark.

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Self-Adjusting DBA Algorithm for Next Generation PONs (NG-PONs) to Support 5G Fronthaul and Data Services

Cited by 22 publications

References 27 publications

SDR Enabled C-RAN Implementation using OpenAirInterface

SDR Enabled C-RAN Implementation using OpenAirInterface

Bandwidth Prediction Based Resource Allocation Scheme for Low-Latency and Energy-Efficient PONs With Heterogeneous ONU Propagation Delays

Deep Reinforcement Learning Based Joint Allocation Scheme in a TWDM-PON-Based mMIMO Fronthaul Network

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