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Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

Mugen Peng, Yaohua Sun, Xuelong Li, Zhendong Mao, Chonggang Wang

arXiv:1604.00607v1cs.IT

TL;DR

C-RAN seeks to address escalating mobile traffic and network expenditure through centralized, virtualized radio access. This paper surveys architectures and techniques across the physical and upper layers, together with open issues. It reports C-RAN benefits including a 10% to 15% per kilometer capital-expenditure reduction versus traditional LTE networks and near-300% throughput gain in a China Mobile uplink LTE field trial.

  • Problem

    C-RAN must provide high spectral and energy efficiency while overcoming constrained fronthaul, processing latency, and large-scale coordination challenges.

  • Method

    The paper comprehensively surveys C-RAN architectures, fronthaul compression, large-scale collaborative processing, channel estimation, resource allocation, and open issues.

  • Results

    The survey reports a 10% to 15% per kilometer capital-expenditure reduction versus traditional LTE networks and near-300% throughput gain in a China Mobile uplink LTE field trial.

  • Takeaways & Limitations

    C-RAN provides a framework for centralized sharing and collaboration, but its potential advantages depend on addressing fronthaul capacity and latency constraints.

Abstract

from arXiv · show

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.

I. INTRODUCTION

C-RAN addresses rapidly growing mobile traffic and rising expenditure by centralizing baseband functions, while constrained fronthaul and large-scale processing create important technical challenges. This survey organizes recent C-RAN architectures, key techniques, and open issues across network layers.

  • Motivation and architecture: C-RAN is motivated by escalating mobile traffic, high spectral-efficiency requirements, and problematic operating expenditure and energy consumption.The paper links these pressures to the need for advanced intelligent wireless network architectures.
  • Motivation and architecture: C-RAN separates distributed RRHs from a centralized, virtualized BBU pool to share storage and computing resources.The BBU pool supports large-scale collaborative processing, cooperative radio resource allocation, and intelligent networking.
  • Benefits and challenges: 10% to 15% per kilometer capital-expenditure reduction is reported for C-RANs compared with traditional LTE networks.Centralized processing also enables flexible resource allocation, load balancing, and simplified network upgrading and maintenance.
  • Benefits and challenges: Constrained fronthaul can bottleneck C-RAN capacity, causing interference when large-scale collaborative processing works inefficiently.The paper identifies fronthaul compression and large-scale decoding as significant technical challenges.
  • Survey scope: The survey covers system architectures, fronthaul compression, large-scale collaborative processing, channel estimation, and cooperative radio resource allocation.It also summarizes open issues and organizes techniques across the physical and upper layers.

II. SYSTEM ARCHITECTURES

C-RAN architectures evolved from integrated base stations to separated RRHs and centralized, virtualized BBU pools. Industry and academic designs address functional-split tradeoffs, centralized processing benefits, and operational evolution toward cloud-based architectures.

  • Architecture evolution: Cellular architecture evolved from integrated radio and baseband processing in 1G and 2G to RRH–BBU separation in 3G and 4G.The later migration of BBUs into shared pools produced the general C-RAN structure.
  • C-RAN general architectures: In 4G beyond, BBUs migrate into a virtualized pool shared across cell sites, while RRHs connect through fronthaul links.This arrangement forms the general C-RAN architecture.
  • C-RAN general architectures: Centralized large-scale collaborative processing reduces air-conditioning energy, operation and maintenance costs, and adapts to non-uniform traffic through BBU-pool load balancing.The centralized structure also makes collaborative processing easier to implement and can improve spectral efficiency.
  • Industry architectures: C-RAN was first proposed as wireless network cloud by IBM in 2010, followed by architectural elaboration by China Mobile Research Institute in 2011.ZTE subsequently proposed solutions addressing fiber scarcity.
  • Industry architectures: Industry architectures differ in the degree of functional split to trade implementation complexity against large-scale collaborative-processing gains.The paper surveys architectures proposed by industry and academia separately.

1) Functional Split of C-RANs:

C-RAN functional splits distribute processing between RRHs and the centralized BBU pool, trading fronthaul burden and local complexity against collaborative-processing gains. The section also identifies fronthaul capacity, latency, reliability, and adaptability as central architectural constraints.

  • Functional splits: Fully centralized C-RANs move PHY, MAC, and network functions to the BBU pool, minimizing RRH digital processing but increasing fronthaul data-rate requirements.This split supports centralized processing while making fronthaul capacity a major cost and deployment constraint.
  • Functional splits: Partially centralized architectures keep PHY functions in RRHs and upper-layer functions in the BBU pool, alleviating fronthaul burden but limiting LSCP gains.Only traditional distributed CoMP gains can be efficiently supported when PHY processing remains at individual RRHs.
  • Functional splits: A split retaining most PHY and MAC functions in RRHs can largely eliminate fronthaul burden, but it obtains only control-plane resource-management gains.In the cited architecture, only control-plane functions remain in the BBU pool.
  • Functional splits: Flexible functional splitting selects an operating point between full centralization and local execution, balancing SE/EE gains against fronthaul capacity and LSCP requirements.RANaaS is presented as enabling this adjustable split rather than fixing processing locations.
  • Architectural challenges: Capacity-constrained fronthaul and signal-processing latency can undermine C-RAN advantages, motivating architectural enhancements for convergence, advanced techniques, and 5G requirements.The survey highlights flexible, reconfigurable, and enhanced architectures as responses to these constraints.

1) Flexible C-RANs:

Flexible C-RAN research extends centralized architectures with configurable processing, advanced radio techniques, heterogeneous-network integration, edge execution, and higher-capacity transport. These enhancements target capacity, latency, reliability, and fronthaul constraints while supporting evolving 5G requirements.

  • Flexible C-RANs: Software-defined C-RAN architectures can run on general-purpose processors and be flexibly configured to operate as different networks.This configurability addresses the fixed nature of traditional centralized architectures.
  • Advanced radio techniques: Massive MIMO can allow one RRH to serve hundreds of terminals through transmit precoding or receive combining, although centralized operation greatly increases fronthaul data volume.The cited work links the capacity gain to coordinated antenna processing and identifies fronthaul as the associated constraint.
  • Enhanced architectures: H-CRAN integrates heterogeneous networks and C-RANs through a cloud-computing entity called Node C, while F-RAN moves collaboration and resource-management functions toward fog access points or smart UEs.F-RAN can also deliver some packets directly through edge devices.
  • C-RANs toward 5G: China Mobile field trials reported uplink LTE throughput gains of up to near 300% using C-RANs, while dense RRHs supported massive connections.The paper notes that a gap remained relative to stated 5G requirements despite this demonstrated potential.
  • C-RANs toward 5G: C-RAN enhancements are needed for low latency and high reliability because fully centralized processing can suffer long latency and reduced reliability under constrained fronthaul.Decentralization through remote clouds, cloudlets, or localized processing is presented as an alternative.
  • Fronthaul compression: Quantized I/Q fronthaul traffic can reach multiple Gbps for a single UE and overwhelm practical fiber links, motivating compression methods that reduce transmitted-signal dimensions or exploit signal correlation and sparsity.The survey classifies approaches into quantization-based compression, compressive sensing, and spatial filtering, with distributed source coding exploiting inter-RRH correlation.

A. Uplink Compression

Uplink fronthaul compression reduces the data transferred from distributed RRHs to the centralized BBU pool under limited-capacity links. The surveyed approaches exploit separate-link processing, inter-RRH correlation, signal sparsity, spatial filtering, and multi-hop forwarding.

  • A. Uplink Compression: Each RRH compresses its received baseband signal before forwarding it to the BBU pool, where joint decoding uses the quantization values from all RRHs.The surveyed uplink methods include point-to-point compression, distributed source coding, compressed sensing, and spatial filtering.
  • A. Uplink Compression: Distributed source coding suits closely correlated RRH signals, while compressed sensing exploits uplink sparsity and point-to-point or spatial filtering favor low implementation complexity.These methods trade compression structure against implementation requirements and signal characteristics.
  • A. Uplink Compression: Robust distributed compression compensates performance loss caused by imperfect knowledge of the joint signal statistics, while layered transmission and compression outperform single-layer strategies under robustness and fronthaul constraints.The reported gains are attributed mainly to layered transmission rather than layered compression.
  • A. Uplink Compression: When quantization noise levels are proportional to background noise levels, Wyner-Ziv coding approaches a cut-set-like sum-capacity upper bound within a constant at sufficiently high SQNR.A similar result is reported for single-user compression under a diagonally dominant channel condition.
  • A. Uplink Compression: Distributed compressed sensing and recovery exploit uplink signal sparsity to compress fronthaul loading while preserving recoverability through the aggregate measurement matrix.The analysis shows that the restricted isometry property can still be satisfied.
  • A. Uplink Compression: Multi-hop fronthaul requires alternatives to multiplex-and-forward because dense RRH deployments can cause significant performance loss; decompress-process-and-recompress is proposed for this setting.The surveyed work extends compression beyond direct RRH-to-BBU connections.

1) Compression under Static Channels:

Static-channel downlink studies examine how precoding, compression, and message sharing should be coordinated when fronthaul capacity is limited. The survey emphasizes network-aware compression and hybrid strategies as practical alternatives to separated designs.

  • 1) Compression under Static Channels:: Cluster-limited joint designs can suffer performance degradation from inter-cluster interference when multiple mutually interfering RRH clusters operate simultaneously.The limitation arises because the original optimization does not account for inter-cluster interference.
  • 1) Compression under Static Channels:: Pure compression forwards compressed precoded analog signals, whereas pure message sharing sends each user’s message to multiple RRHs over the fronthaul.Both strategies place joint precoding at the BBU pool but differ in what is transmitted over the fronthaul.
  • 1) Compression under Static Channels:: Network-aware compression generally outperforms separate precoding and compression, while hybrid pure compression and message sharing is more beneficial under finite fronthaul capacity.In uplink, network-aware methods include distributed source coding and spatial filtering; in downlink, joint multivariate compression is preferred.
  • 1) Compression under Static Channels:: Network-aware compression requires full and ideal CSI for access and wireless fronthaul links, making CSI acquisition an open challenge in uplink systems.This requirement limits the practicality of otherwise information-efficient compression designs.

A. Precoding with Perfect CSIs

Perfect-CSI precoding studies target interference mitigation and spectral- and energy-efficiency improvements, while balancing centralized processing against complexity and fronthaul limitations. The surveyed methods include linear precoding, RRH selection, compression coordination, and sleep-mode operation.

  • A. Precoding with Perfect CSIs: Dirty paper coding achieves the capacity region but has excessive complexity for large-scale C-RANs, making linear precoding a lower-complexity alternative.Few large-scale C-RAN studies consider dirty paper coding because of this complexity.
  • A. Precoding with Perfect CSIs: Sparse precoding patterns guide joint RRH selection and linear precoding, achieving significant performance gains with low computational complexity.The method selects cooperating RRHs through regularized convex optimization rather than distance or channel-quality rules alone.
  • A. Precoding with Perfect CSIs: Under limited fronthaul, compression can affect system performance more than precoding: dirty paper coding outperforms linear precoding only at intermediate transmit power.The comparison is reported for joint downlink precoding and compression.
  • A. Precoding with Perfect CSIs: Full-CSI acquisition becomes challenging in large C-RANs because many channel parameters create estimation errors, quantization errors, and long feedback delays.The BBU pool may support hundreds of RRHs, increasing the CSI acquisition burden.
  • A. Precoding with Perfect CSIs: Compressive CSI acquisition combines instantaneous CSI for a subset of links with statistical CSI for the others to reduce the channel-compression dimension and overhead.The mixed CSI is used for coordinated beamforming.
  • A. Precoding with Perfect CSIs: Stochastic coordinated beamforming can reduce CSI acquisition overhead by about 40% while providing performance close to full-CSI operation.Its required sample count nevertheless increases rapidly with network size.

3) Robust Precoding Design:

Robust C-RAN design addresses imperfect CSI, energy consumption, RRH selection, clustering, and per-RRH resource constraints. The surveyed optimization methods use sparsity and iterative approximations to make these coupled problems tractable.

  • 3) Robust Precoding Design:: Robust multicast beamforming uses group sparsity to guide RRH selection while minimizing energy under imperfect CSI and non-convex QoS constraints.The formulation includes an infinite number of non-convex quadratic QoS constraints.
  • 3) Robust Precoding Design:: Fr onthaul and RRH sleep modes can reduce power consumption when traffic is low, provided QoS requirements remain satisfied.Group sparsity is needed to switch off RRH groups rather than individual elements.
  • 3) Robust Precoding Design:: Joint clustering and beamforming creates sparse network-wide beamforming vectors whose nonzero entries identify serving RRHs.Reweighted l1-norm approximations convert the non-convex design into a sequence of convex weighted power-minimization problems.
  • 3) Robust Precoding Design:: Per-RRH power and fronthaul constraints motivate network-utility maximization with non-convex l0-norm approximations and scheduling or beamforming optimization.Static clustering methods incorporate RRH traffic load and UE channel conditions.
  • 3) Robust Precoding Design:: Green C-RAN design jointly selects RRHs and minimizes beamforming power, but the resulting group-sparse formulation is NP-hard.Weighted l1/l2-norm regularization is used to induce group sparsity in the beamformers.
  • 3) Robust Precoding Design:: Because fronthaul capacity is constrained and CSI is non-ideal, LSCP and compression should be jointly designed using efficient algorithms such as reweighted l1-norm approximations.The survey also identifies compressive CSI acquisition, stochastic beamforming, and sparsity-guided RRH selection as complexity-reduction tools.

V. CHANNEL ESTIMATION AND TRAINING DESIGN

C-RAN channel estimation must recover CSI across radio-access and fronthaul links despite training overhead, constrained fronthaul, and large-scale computation. The surveyed designs trade estimation accuracy against spectral efficiency, overhead, and complexity.

  • C-RAN channel estimation must obtain CSI for both radio-access and wireless-fronthaul links at the BBU pool.These links jointly support data transmission and beamforming design.
  • A. Superimposed Training Based Channel Estimation in C-RANs: Superimposed training lets RRHs overlay training sequences on received signals to estimate individual CSIs at the BBU pool.The scheme uses source training followed by RRH-superimposed training in two phases.
  • A. Superimposed Training Based Channel Estimation in C-RANs: Superimposed training trades channel-estimation accuracy and training overhead against shared transmit power and sensitivity to unknown data symbols.Sharing power between training and data can reduce transmission rate and estimation efficiency.
  • A. Superimposed Training Based Channel Estimation in C-RANs: Superimposed-segment training combines superimposed ACL training with individual segment training for the other link to balance data rate and overhead.Iterative maximum-a-posteriori estimation can improve the individual ACL and WFL CSI estimates.
  • 1) Balance between Performance and Overhead:: Large-scale C-RAN estimation makes conventional LMMSE matrix inversion unaffordable because its complexity is cubic in covariance-matrix dimension.Matrix polynomial expansion reduces the estimator's complexity to square complexity by approximating the matrix inverse.

2) Estimation in Large-scale C-RANs:

The section contrasts channel-estimation approaches that conserve bandwidth with approaches that improve accuracy, while emphasizing computational and fronthaul constraints in large-scale C-RANs. It also introduces centralized and decentralized resource-allocation formulations for non-convex C-RAN optimization.

  • C. Non-Training-Based Channel Estimation in C-RANs: Training-based estimation provides reliable CSI but consumes overhead, whereas non-training-based estimation improves spectral efficiency at the cost of high computation and ambiguity.Semi-blind estimation combines training and data samples to improve accuracy over either purely training-based or blind estimation.
  • C. Non-Training-Based Channel Estimation in C-RANs: Semi-blind C-RAN estimation uses least-squared initialization and quasi-Newton maximum-likelihood refinement to preserve bandwidth efficiency while improving accuracy.The data symbols are treated as Gaussian-distributed nuisance parameters in the joint likelihood formulation.
  • D. Lessons Learned: Frontal loading and channel-estimation errors remain important open issues because constrained fronthaul affects both estimation design and performance.The paper identifies fronthaul-aware estimation as an urgent future challenge.
  • Radio Resource Allocation: Multi-dimensional radio-resource allocation jointly considers power or precoding, user scheduling, and RRH clustering, producing generally non-convex optimization problems.Centralized methods can jointly monitor and optimize these dimensions when full CSI is available at the BBU pool.
  • Radio Resource Allocation: Centralized resource allocation uses Lagrange duality, WMMSE, and norm approximations to transform selected non-convex formulations into convex problems.WMMSE addresses precoding-related non-convexity, while l1-norm approximations handle sparse RRH selection formulations.
  • Radio Resource Allocation: Full-CSI centralized allocation burdens the fronthaul and computation, motivating decentralized RRH decisions based on local CSI.Game-based approaches are presented as a way to reduce global-CSI transmission requirements.

2) Game Model based Optimization:

Game-based optimization distributes C-RAN coordination by allowing RRHs or network entities to form coalitions, negotiate contracts, or match preferences. These models address fronthaul burdens and coordination, but matching-based RRH association remains unresolved.

  • 2) Game Model based Optimization:: Coalition games partition RRHs into cooperative clusters for distributed coordination in uplink and downlink C-RANs.Coalitions can form virtual multi-antenna arrays or support interference-alignment-based cooperation.
  • 2) Game Model based Optimization:: Game-based optimization is motivated by the heavy fronthaul burden of obtaining global CSI for centralized clustering and resource allocation.RRHs can instead make distributed or selfish decisions using local information.
  • 2) Game Model based Optimization:: Contract games provide incentives for agreement under asymmetric information, with the BBU pool acting as principal and another network entity as agent.A contract-based approach is described for interference coordination between a C-RAN and macro base stations in H-CRANs.
  • 2) Game Model based Optimization:: Matching games offer tractable preference-based matching, but their application to C-RAN RRH association remains unexplored.Unlike one-BS association, C-RAN users may associate with multiple RRHs, making low-complexity matching difficult.

B. Dynamic CRRA with Queue-Awareness

Dynamic C-RAN resource allocation must adapt to changing traffic and channels while controlling interference and delay. The survey identifies equivalent-rate, Lyapunov, and MDP approaches, but practical fronthaul and state-overhead constraints complicate delay-aware design.

  • C-RANs require fast adaptation to user arrivals, departures, and bursty traffic while controlling inter-RRH interference through cooperative PHY processing.Centralized management supports coordination, but dynamic operation must respond to changing traffic demands.
  • B. Dynamic CRRA with Queue-Awareness: Existing cross-layer studies commonly assume infinite queue backlogs and stationary channels, optimizing SE, EE, or fairness based only on CSI.Such CSI-only policies cannot guarantee good delay performance under time-varying channels and random traffic arrivals.
  • B. Dynamic CRRA with Queue-Awareness: Delay-aware cross-layer optimization must jointly use CSI and queue-state information while modeling both queue dynamics and physical-layer behavior.Fr onthaul capacity, state-reporting overhead, and parallel implementation add practical complexity.
  • B. Dynamic CRRA with Queue-Awareness: The surveyed delay-aware approaches comprise equivalent-rate, Lyapunov optimization, and Markov decision process methods.These approaches form a roadmap of ongoing attempts rather than an exhaustive classification.
  • B. Dynamic CRRA with Queue-Awareness: The equivalent-rate approach converts average delay constraints or objectives into equivalent average-rate constraints using queuing or large-deviation theory.One cited formulation relates delay requirements, arrival rate, and service rate through a packet-flow queueing model.

1) The Equivalent Rate Approach:

Dynamic cross-layer resource optimization in C-RANs uses equivalent-rate, Lyapunov, and MDP approaches to balance delay, queue stability, performance, and computational complexity. These methods differ in tractability, adaptability, and achievable delay performance.

  • 1) The Equivalent Rate Approach:: Equivalent-rate constraints transform delay-aware cross-layer optimization into a pure PHY-layer problem with potentially simple control policies.The approach uses CSI with weighting shifts induced by delay requirements.
  • 2) The Lyapunov Optimization Approach:: Lyapunov optimization stabilizes queues while optimizing performance metrics or satisfying constraints across layers.The resulting control policies adapt to both CSI and queue-state information.
  • 3) The MDP Approach:: MDP formulations model delay-aware optimization as infinite-horizon average-cost problems over system states combining CSI and queue-state information.Their state-space cardinality grows exponentially with the number of queues, making general solutions complicated.
  • 3) The MDP Approach:: MDP methods still face an open tradeoff between optimality and complexity because suboptimal value functions can degrade performance.Exact value-function calculation is extremely complicated and applies only to simple scenarios.
  • Comparison of Approaches: MDP-based algorithms achieve better delay performance over a wide operating regime, while equivalent-rate and Lyapunov methods provide lower-complexity solutions.The comparison concerns subcarrier and power allocation algorithms in an OFDMA system.

A. Edge Cache in C-RANs

Edge caching and big-data mining can use storage and historical demand information to reduce fronthaul burden and support proactive C-RAN operation. Their deployment raises challenges in cache updates, data fetching, association, fronthaul load, and incomplete data processing.

  • A. Edge Cache in C-RANs: Edge devices can cache frequently requested data proactively, alleviating fronthaul burden and enabling UEs to serve surrounding UEs.RRHs and UEs are identified as potential edge-cache locations.
  • A. Edge Cache in C-RANs: Edge-cache strategies must balance update frequency, QoE, fronthaul consumption, incomplete popularity information, data fetching, and RRH association.Higher update frequency improves QoE but consumes more fronthaul resources.
  • B. Big Data Mining in C-RANs: Big-data mining can extract patterns from subscriber, cell, and core-network data to enhance self-organizing C-RAN capabilities.Historical content requests can help predict video interests and guide edge caching.
  • B. Big Data Mining in C-RANs: Big-data applications face fronthaul burdens from transmitting collected data and computational difficulties with sparse, uncertain, and incomplete data.These conditions require advanced data-mining algorithms.
  • C. Social-Aware D2D in C-RANs: Social-aware D2D in C-RANs favors operation without BS assistance because RRHs are mainly deployed for capacity in special zones.Selecting between D2D and C-RAN modes remains critical.
  • D. CR in C-RANs: Cognitive radio can improve spectrum utilization by enabling RRHs to identify temporarily unused assigned spectrum, but implementation complexity and cost remain challenges.Combining cognitive radio with carrier aggregation can enlarge RRH operating bandwidth.

E. SDN with C-RANs

SDN brings centralized control and programmability to C-RANs, enabling flexible BBU–RRH mappings and pooled virtualized resources. However, centralized control introduces failure, placement, and scalability concerns, while broader C-RAN research remains subject to open problems.

  • E. SDN with C-RANs: SDN enables flexible BBU–RRH mapping that adapts to traffic volume and user mobility.Multiple RRHs can be mapped to one BBU for high-mobility regions, while one-to-one mapping can serve static users.
  • E. SDN with C-RANs: SDN-based virtualization pools BBU hardware resources and offers them as service slices to BBU virtual instances.Infrastructure and service managers divide resource provisioning and consumption responsibilities.
  • E. SDN with C-RANs: A single failed central controller could collapse the whole C-RAN, making controller architecture a critical reliability concern.The passage identifies multiple-controller placement as another design issue.
  • E. SDN with C-RANs: Controller placement affects processing latency and other network metrics, while limited controller service capability creates scalability challenges.These issues become especially important in large-scale C-RANs with multiple controllers.
  • G. Testbed and Trial Test: China Mobile trial tests reported 40-100% uplink LSCP gains in weak-coverage areas with reference signal received power below -95 dBm.A GPP-based testbed supported TD-LTE, FDD-LTE, and GSM with performance similar to traditional DSP/FPGA systems.
  • VIII. CONCLUSION: The survey organizes C-RAN research around architectures, fronthaul compression, large-scale collaborative processing, channel estimation, cooperative radio resource allocation, and open issues.The conclusion emphasizes the need for further investigation, including partially centralized architectures with edge cloud computing.
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