Remote radio head (RRH) deployment in flexible C-RAN under limited fronthaul capacity

Huang, Bo-Syuan; Chiang, Yi-Han; Liao, Wanjiun

doi:10.1109/icc.2017.7996634

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…Partial-centralization [69] can also be used to decrease the fronthaul traffic, where the DUs integrate not only the radio function but also the baseband functions. The work in [88] assumes that there are two types of DUs; a primitive one and a DU with layer-1 functions, and then formulates an objective function to determine at which site (from available ones) and how many nodes of each type are to be deployed based on a trade-off between the deployment cost and the fronthaul capacity. We note that the authors assume a fixed limited-capacity fronthaul.…”

Section: A Minimizing Fronthaul Communicationmentioning

confidence: 99%

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Ammar¹,

Adve²,

Shahbazpanahi³

et al. 2021

Preprint

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Densification of network base stations is indispensable to achieve the stringent Quality of Service (QoS) requirements of future mobile networks. However, with a dense deployment of transmitters, interference management becomes an arduous task. To solve this issue, exploring radically new network architectures with intelligent coordination and cooperation capabilities is crucial. This survey paper investigates the emerging user-centric cell-free massive multiple-input multiple-output (MIMO) network architecture that sets a foundation for future mobile networks. Such networks use a dense deployment of distributed units (DUs) to serve users; the crucial difference from the traditional cellular paradigm is that a specific serving cluster of DUs is defined for each user. This framework provides macro diversity, power efficiency, interference management, and robust connectivity. Most importantly, the user-centric approach eliminates cell edges, thus contributing to uniform coverage and performance for users across the network area. We present here a guide to the key challenges facing the deployment of this network scheme and contemplate the solutions being proposed for the main bottlenecks facing cell-free communications. Specifically, we survey the literature targeting the fronthaul, then we scan the details of the channel estimation required, resource allocation, delay, and scalability issues. Furthermore, we highlight some technologies that can provide a management platform for this scheme such as distributed software-defined network (SDN) and self-organizing network (SON). Our article serves as a check point that delineates the current status and indicates future directions for this area in a comprehensive manner.

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Section: A Minimizing Fronthaul Communicationmentioning

confidence: 99%

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Ammar¹,

Adve²,

Shahbazpanahi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Using ILP techniques, Holm et al formulate a problem for optimizing cells assignments to different CU pools by taking into consideration fiber length and statistical multiplexing gain. Huang et al study the CU placement problem considering the PHY‐RF split and the intra‐PHY‐layer split at the base stations. An optimization problem is formulated having the objective of minimizing the total deployment cost of the mobile network while satisfying the traffic demands.…”

Section: Related Workmentioning

confidence: 99%

CU placement over a reconfigurable wireless fronthaul in 5G networks with functional splits

et al. 2019

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Mobile network operators are currently facing a tremendous increase in the level of data traffic. Although cell size reduction is one of the most common ways used to accommodate such traffic demand, densely deployed small cells also dramatically increase the level of intercell interference. By centralizing baseband signal processing at powerful computing infrastructures, called centralized unit (CU) pools, cloud radio access network (C-RAN) enables advanced coordination algorithms to be employed in dense small cell networks. In C-RAN, due to stringent bandwidth and latency requirements at the fronthaul links, the optical fiber, thanks to its bandwidth and latency characteristics, continues to be the most prevalent fronthaul medium option. Nevertheless, the optical fiber is one of the fronthaul options, while C-RAN (physical layer radio frequency [PHY-RF] split) is one of the functional splits that can be defined each coming with different fronthaul requirements.In this paper, we formulate and solve a dynamic CU placement problem for mobile networks as an integer linear programming (ILP) problem. In the considered network, CU pools are placed at the edges of the network, and a reconfigurable millimeter wave (MMW) wireless fronthaul links are used in order to provide decentralized units (DUs) with connectivity. We study the impact of different functional splits on the placement cost and on the acceptance ratio using different substrate networks. Lastly, we propose and evaluate a CU placement heuristic algorithm using a numerical simulator. The results reveal that the optimal functional split selection can lead to significant resource utilization benefits in the RAN.Int J Network Mgmt. 2020;30:e2086. wileyonlinelibrary.com/journal/nem to the distributed radio access network (D-RAN) architecture, in which coaxial cables are substituted by optical fibers, and a digital interface is used to carry the in-phase/quadrature (I/Q) samples between the baseband unit and the radio unit. However, since in the D-RAN architecture a Long-Term Evolution (LTE) base station, called eNB, cannot share its frequency resources with other eNBs. These scarce frequency resources will be underutilized most of the time in a day due to the spatiotemporally fluctuating traffic demand. In order to tackle the aforementioned problem, the cloud RAN (C-RAN) 3 architecture has been recently proposed. C-RAN is capable of reducing the total cost of ownership (TCO) of mobile networks while, at the same time, optimizing network capacity, coverage, and power consumption. Such goals are achieved in C-RAN by separating baseband units, termed centralized units (CUs) from the radio units, termed distributed units (DUs) and combining CUs in powerful computing infrastructures, termed CU pools. * CU pools can run on a number of general-purpose of-the-shelf servers deployed in centralized locations.The separation of the signal processing functionalities in the RAN protocol stack between the CU pool and the DU is known as functional split. 5 Traditionally, in the ...

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“…The authors in [6] proposed precoders to maximize energy efficiency. Precoders were proposed in [7], [8] to minimize power consumption by flexible functional partitioning between RRHs and the BBU. In [9] and [10], the authors used a point-to-point compression [11] to achieve the maximum fronthaul capacity; while in [12] and [13], the authors maximized the sum rate via optimizing the quantization noise from an informationtheoretic perspective.…”

Section: Introductionmentioning

confidence: 99%

Precoder and Spatial Compression Filter Designs for Uplink Cloud Radio Access Networks

2021

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This work considers an uplink cloud radio access network (C-RAN), and proposes a MIMO spatial compression filtering and which compressed symbols to upload at RRH side as well as MIMO precoding at UE (user equipment) side. The error rate performance of this proposed scheme is analyzed and closedform analytical results are obtained. Based on the analytical results, we propose solutions for the spatial compression filter, compressed symbols and UE precoder. Fronthaul loading can be significantly reduced by the proposed MIMO spatial compression filter and compressed symbols; meanwhile this proposed scheme can greatly outperform conventional schemes under the same settings of fronthaul loading. Moreover, when the UE has multiple antennas, the performance can further be significantly improved by using the proposed UE precoder. Simulation results are provided to show the accuracy of the analytical results as well as the advantages of the proposed solutions in terms of error rate performance and reduced fronthaul loading.

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Remote radio head (RRH) deployment in flexible C-RAN under limited fronthaul capacity

Cited by 11 publications

References 5 publications

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

CU placement over a reconfigurable wireless fronthaul in 5G networks with functional splits

Precoder and Spatial Compression Filter Designs for Uplink Cloud Radio Access Networks

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