Power-Efficient Baseband-Function Placement in Latency-Constrained 5G Metro Access

Zorello, Ligia Maria Moreira; Sodano, Marco; Troia, Sebastian; Maier, Guido

doi:10.1109/tgcn.2022.3152839

Cited by 21 publications

(5 citation statements)

References 23 publications

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“…In [21] and [22], service-oriented CU and DU placement are done with the help of Reinforcement Learning. In [23], the baseband function placement strategy is proposed with the help of an optimization model and heuristic to minimize the power consumption in the network. However, in [21]- [23], only fixed functional split options are considered between CU and DU.…”

Section: Related Workmentioning

confidence: 99%

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Sen

Franklin

2023

IEEE Access

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5G and Beyond 5G (B5G) are undergoing numerous architectural changes to enable higher flexibility and efficiency in mobile networks. Unlike traditional mobile networks, baseband functions in 5G are disaggregated into multiple components -Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). These components can be placed in different geographical locations based on the latency sensitivity and available capacity in the network. Processing baseband functions in a centralized location offer various advantages (known as centralization benefit in RAN) to mobile network operators, which have been a point of interest for several research works. However, achieving maximum centralization is challenging due to various factors such as limited capacity in the midhaul network, delay requirement of different functional splits and network slices, etc. In this work, we aim to address these challenges by proposing a slice-aware baseband function placement strategy. Our primary objective is to maximize the degree of centralization in the network by appropriate selection of functional split. To achieve this objective, we jointly consider functional split, traffic split, different placement options for baseband functions, and network slice-specific requirements. We also consider the minimization of active processing nodes in cloud infrastructure of different levels (edge and regional) to provide additional resource efficiency. To this end, we formulate an optimization model using Mixed Integer Linear Programming (MILP) and compare its performance with different baseline techniques. We show that the proposed model achieves 6.5% more degree of centralization than the state-of-the-art while placing baseband functions in the network. To tackle the high computational complexity of the MILP model, we also present a polynomial-time heuristic algorithm for solving the problem in large-scale scenarios. We show that although the optimization model achieves around 4% more degree of centralization than the heuristic, the heuristic solves the problem in a reasonable amount of time, making it suitable for real deployment scenarios.

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Section: Related Workmentioning

confidence: 99%

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Sen

Franklin

2023

IEEE Access

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“…In addition to the aforementioned components, the RU also comprises RF transceivers and power amplifiers. There are also common site infrastructures including cooling, monitoring, alarm, power supply, and conversion systems [11]. The energy consumption of the radio network also depends on the kind of functional split implemented as the more centralized the network functions are at the CU the lesser the energy consumption of the RU, even though it has its associated latency and transport network challenges.…”

Section: A Power Consumption Components Of O-ranmentioning

confidence: 99%

“…The work in [24] investigated the problem of CU-DU mapping in NG-RAN in order to minimize energy costs without affecting the quality of experience (QoE) of the users. To achieve their objective, they proposed a heuristic algorithm that will minimize the number of inter-CU user handovers by ensuring that neighboring DUs within user proximity are mapped to the same CU and also ensuring [23] 2022 ✓ e-constraint method [24] 2020 ✓ Heuristic algorithm [14] 2021 ✓ Lagragian decomposition simulated annealing [25] 2021 ✓ MILP [26] 2022 ✓ Adversarial bandit learning [27] 2021 ✓ Q-learning, SARSA [28] 2020 ✓ Deep RL [11] 2022 DCDP&UA MILP, heuristic algorithm [17] 2021 ✓ MILP, graph-based heuristic [29] 2020 ✓ Heuristic algorithm [30] 2018 ✓ analytical solution based on Constraint programming [31] 2022 ✓ MILP, heuristic algorithm [32] 2018 IOEE analytical solution based on Bender's decomposition [33] 2022 ✓ Deep RL [34] 2022 ✓ Heuristic algorithm [35] 2022 ✓ Deep Q-networks [36] 2022 ✓ Deep deterministic policy gradient (DDPG) [37] 2022 ✓ BILP that the number of active CUs in the CU pool is minimized while considering three functional splits options.…”

Section: A Dynamic Resource Allocation and Network Function Placement...mentioning

confidence: 99%

“…The following paragraphs present the few works that have adopted this approach of energy optimization in O-RAN. In this regard, the work in [11] considered the problem of power consumption in 5G RAN by optimizing the placement of CUs and DUs in the network in an energy-efficient manner while considering different functional splits, capacity, and latency as constraints. Two optimization strategies; MILP and a reduced complexity heuristic algorithm were proposed for small and large network topologies, respectively.…”

Section: B Dynamic Cu and Du Placement And User Association (Dcdpandua)mentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficiency of Open Radio Access Network: A Survey

Abubakar¹,

Onireti²,

Sambo³

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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The Open Radio Access Network (O-RAN) architecture has been identified as a promising technology for enhanced network deployment, innovation, improved competition, and reduction of capital and operating expenses (CAPEX/OPEX) of 5G and beyond networks because of its open interfaces, disaggregated network entities and functions, virtualization of network hardware and software, and intelligent control. However, the effect of this improved technology on the energy consumption of the RAN needs to be carefully investigated, so that the many advantages that can be obtained from the O-RAN are not overwhelmed by increased energy consumption. Hence, in this paper, we investigate the O-RAN from an Energy efficiency (EE) perspective by reviewing the state-of-the-art power consumption models, and EE techniques that have been proposed to minimize the energy consumption of O-RAN. In addition, the challenges associated with the optimization of the EE of O-RAN and opportunities for further research are highlighted.

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“…Several recent works have focused on the 5G and B5G RAN provisioning that is supported by the resources (computation and connectivity) provided by the underlying access and metro networks. For instance, the authors in [ 11 ] tackled the problem of DU/CU placement in the access and metro networks for power consumption minimization that are subject to functional split, latency, and capacity requirements. Targeting more advanced B5G scenarios, the authors in [ 12 ] considered functional split, traffic split, different placement options for virtual functions, and network slice-specific requirements in a joint provisioning problem.…”

Section: Introductionmentioning

confidence: 99%

Context-Based e2e Autonomous Operation in B5G Networks

Wang,

Ruiz,

Velasco

2024

Sensors

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The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.

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Power-Efficient Baseband-Function Placement in Latency-Constrained 5G Metro Access

Cited by 21 publications

References 23 publications

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Energy Efficiency of Open Radio Access Network: A Survey

Context-Based e2e Autonomous Operation in B5G Networks

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