Integrating Fronthaul and Backhaul Networks: Transport Challenges and Feasibility Results

Gonzalez-Diaz, Sergio; García‐Saavedra, Andrés; Oliva, Antonio de la; Costa‐Pérez, Xavier; Gazda, Robert; Mourad, Alain; Deiß, Thomas; Mangues‐Bafalluy, Josep; Iovanna, Paola; Stracca, Stefano; Leithead, Phillip

doi:10.1109/tmc.2019.2948641

Cited by 15 publications

(13 citation statements)

References 14 publications

(28 reference statements)

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“…Similarly, constraint ( 6) is used to bound the maximum computational capacity supported by the CU (κ 0 ). Constraint (7) guarantees that all the traffic served by a RRH, regardless of the slice to which the traffic belongs, equals the traffic forwarded over the paths from the CU to the RRH, as shown in (2). Constraint (8) states that the flow from each RRH r to CU is bounded by the capacity of the links of the paths, denoted as ω i,j (b/s).…”

Section: A Mip Problem Formulationmentioning

confidence: 99%

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Ojaghi

Adelantado

Antonopoulos

et al. 2022

IEEE Systems Journal

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5G mobile networks are envisioned to substantiate new vertical services with diverse performance requirements. Slicing in the Radio Access Network (RAN) promises an efficient solution for these diversified needs of 5G networks, which foresees the separation of the Base Station (BS) functionality between the Central Unit (CU) and the distributed Remote Radio Heads (RRHs). In this paper, we formulate a Mixed Integer Programming (MIP) framework that maximizes the throughput by jointly selecting the optimal Functional Split (FS) and the routing path from a connected User Equipment (UE) to the CU, while satisfying the agreed Service Level Agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the service requirements connected through a Fronthaul/Backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a trade-off between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing State-of-the-Art (SoA) up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.

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Section: A Mip Problem Formulationmentioning

confidence: 99%

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Ojaghi

Adelantado

Antonopoulos

et al. 2022

IEEE Systems Journal

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“…Once a RRH-user assignment is made by the algorithm, exact small-scale fading can be obtained online via pilot-based methods to enable the maximum ratio transmission beamforming by the RRHs. It is assumed the capacity of the backhaul and fronthaul links are large enough to support needed data flow of all scheduled users simultaneously [66]. Infinite buffer size is assumed for both RRHs and users.…”

Section: A Simulation Setupmentioning

confidence: 99%

Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

et al. 2022

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This paper is concerned with a cognitive cloud radio access network (CRAN) with a special attention to efficient and reliable transmission of big data. In particular, the paper focuses on optimizing performance of secondary users (SUs) in the downlink. Existing approaches either try to maximize the number of accepted SUs or the sum data rate of accepted SUs. The first approach unfairly favors users with small data requests, whereas the second approach allocates most resources to users with better channel conditions. In contrast, this paper develops a novel approach that favors big data requests while simultaneously maintaining a certain degree of fairness among SUs. To this end, we first introduce a novel objective function that allows us to jointly optimize deadline-aware time scheduling, spectrum allocation, SU selection, and remote radio head (RRH) allocation for SUs. Second, we demonstrate that finding the global optimum solution for the formulated problem entails the enumeration of all colorful independent sets on a generalized interval graph which is known to be NP-hard. Third, we propose a dynamic programming (DP) approach which yields the global optimum solution at a reduced computational cost. Fourth, we analyze the complexity of the proposed DP approach and numerically compare its performance against existing baseline algorithms. Numerical and simulation results revealed that our solution favors big data users while incurring only a small degradation in the fairness index. Our proposed solution is practical for small-to-medium size networks. Furthermore, it offers a benchmark against which any new sub-optimal low-complexity algorithm can be compared to determine how far from the global optimum its performance is.

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“…The authors in [19] select splits to minimize inter-cell interference; [20] and [21] consider adaptive splits; [22] optimizes the splits for RAN slicing; and our previous work [7] optimized jointly the routing and splits. This is very important as vRANs use a shared packet-based network instead of dedicated CPRI links, and we refer the reader to our pertinent feasibility study [9]. The above works do not consider multiple CUs, nor the need to determine their location; yet, this is a key issue in vRAN design.…”

Section: Related Workmentioning

confidence: 99%

“…However, the CU-DU assignment decisions are affected by the functional split of each BS. While initially the vRAN fronthaul was designed using point-to-point connections, the new Xhaul architecture consists of heterogeneous packetswitched links that share multiple flows [9]. This approach is cost-effective, yet compounds the vRAN design problem as one has, additionally, to decide the routing paths.…”

Section: Introduction a Backgroundmentioning

confidence: 99%

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

Murti

Ayala‐Romero

García‐Saavedra³

et al. 2021

IEEE Trans. Wireless Commun.

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Virtualized radio access networks (vRAN) are emerging as a key component of wireless cellular networks, and it is therefore imperative to optimize their architecture. vRANs are decentralized systems where the Base Station (BS) functions can be split between the edge Distributed Units (DUs) and Cloud computing Units (CUs); hence they have many degrees of design freedom. We propose a framework for optimizing the number and location of CUs, the function split for each BS, and the association and routing for each DU-CU pair. We combine a linearization technique with a cutting-planes method to expedite the exact problem solution. The goal is to minimize the network costs and balance them with the criterion of centralization, i.e., the number of functions placed at CUs. Using data-driven simulations we find that multi-CU vRANs achieve cost savings up to 28% and improve centralization by 77%, compared to single-CU vRANs. Interestingly, we see non-trivial trade-offs among centralization and cost, which can be aligned or conflicting based on the traffic and network parameters. Our work sheds light on the vRAN design problem from a new angle, highlights the importance of deploying multiple CUs, and offers a rigorous optimization tool for balancing costs and performance.

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Integrating Fronthaul and Backhaul Networks: Transport Challenges and Feasibility Results

Cited by 15 publications

References 14 publications

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Globally Optimal Resource Allocation and Time Scheduling in Downlink Cognitive CRAN Favoring Big Data Requests

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

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