5G RAN: Functional Split Orchestration Optimization

Matoussi, Salma; Fajjari, Ilhem; Costanzo, Salvatore; Aitsaadi, Nadjib; Langar, Rami

doi:10.1109/jsac.2020.2999685

Cited by 44 publications

(30 citation statements)

References 22 publications

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“…Consider the distribution of each wireless channel (downlink) achievable rate follows (31) and backhaul link capacities are as listed previously. Suppose that the available bandwidth in each AP increases by a step size of 10 MHz, where θ k = 1/2 and σ k = 0.6.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, no outage (rate loss) is considered. For each downlink, the transmission rate and the allocated bandwidth are connected to each other as r p k = δ p k t p k , where δ p k is the spectral efficiency of the downlink of path p to serve user k. Furthermore, suppose Consider the distribution of each wireless channel (downlink) achievable rate follows (31) and backhaul link capacities are as listed previously. Suppose that the available bandwidth in each AP increases by a step size of 10 MHz, where θ k = 1/2 and σ k = 0.6.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The joint routing in the backhaul network and resource allocation RAN is studied in a number of more recent papers [27]- [34]. In [27] and [31], the user demand requirements are deterministic and known. On the other hand, in [28], [30], [32]- [34], the traffic of users is maximized as much as the network is able to support, regardless of user demand statistics.…”

Section: Introductionmentioning

confidence: 99%

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Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Reyhanian

Farmanbar

Luo

2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Resource reservation is an essential step to enable wireless data networks to support a wide range of user demands. In this paper, we consider the problem of joint resource reservation in the backhaul and Radio Access Network (RAN) based on the statistics of user demands and channel states, and also network availability. The goal is to maximize the sum of expected traffic flow rates, subject to link and access point budget constraints, while minimizing the expected outage of downlinks. The formulated problem turns out to be non-convex and difficult to solve to global optimality. We propose an efficient Block Coordinate Descent (BCD) algorithm to approximately solve the problem. The proposed BCD algorithm optimizes the link capacity reservation in the backhaul using a novel multipath routing algorithm that decomposes the problem down to link-level and parallelizes the computation across backhaul links, while the reservation of transmission resources in RAN is carried out via a novel scalable and distributed algorithm based on Block Successive Upper-bound Minimization (BSUM). We prove that the proposed BCD algorithm converges to a Karush-Kuhn-Tucker (KKT) solution. Simulation results verify the efficiency and the efficacy of our BCD approach against two heuristic algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Reyhanian

Farmanbar

Luo

2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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“…Compared to other virtualization platforms, such as VMs, docker containers are a more appropriate environment for hosting the eNodeB application, as they do not require a virtualization layer. They also provide other advantages such as running the application directly on the kernel, using less memory (compared to the VMs), and making runtime executions more efficient [27]. Therefore, there is little performance degradation by deploying the application in such an environment.…”

Section: ) Cloudificationmentioning

confidence: 99%

Toward a Live BBU Container Migration in Wireless Networks

Schiller¹,

Ajayi²,

Weber

et al. 2022

IEEE Open J. Commun. Soc.

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Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processors) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances.Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.

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“…In [31], Chatterjee et al present a radio resource orchestration framework for 5G applications where network slices are dynamically re-assigned to avoid inefficiencies and SLA violations. Relevant to our work are the works of Morais et al [32] and Matoussi et al [33], which present frameworks to optimally disaggregate, place and orchestrate RAN components in the network to minimize computation and energy consumption while accounting for diverse latency and performance requirements. Although the above works all present orchestration frameworks for NextG systems, they are focused on orchestrating RAN resources and functionalities, rather than network intelligence, which represents a substantially different problem.…”

Section: Related Workmentioning

confidence: 99%

OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN

Bonati¹,

Polese²,

Melodia³

2022

Preprint

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The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence via innovative data-driven algorithms. How to practically realize this vision, however, is largely an open problem. For a given network optimization/automation objective, it is currently unknown how to select which data-driven models should be deployed and where, which parameters to control, and how to feed them appropriate inputs. In this paper, we take a decisive step forward by presenting and prototyping OrchestRAN, a novel orchestration framework for next generation systems that embraces and builds upon the Open Radio Access Network (RAN) paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-Real-time (RT) RAN Intelligent Controller (RIC) and allows Network Operators (NOs) to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-RT, e.g., for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location (e.g., in the cloud, or at the edge) to achieve intents specified by the NOs while meeting the desired timing requirements and avoiding conflicts between different data-driven algorithms controlling the same parameters set. We show that the intelligence orchestration problem in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype Orches-tRAN and test it at scale on Colosseum, the world's largest wireless network emulator with hardware in the loop. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.

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5G RAN: Functional Split Orchestration Optimization

Cited by 44 publications

References 22 publications

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Resource Reservation in Backhaul and Radio Access Network with Uncertain User Demands

Toward a Live BBU Container Migration in Wireless Networks

OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN

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