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Fog Computing Based Radio Access Networks:

Issues and Challenges

Mugen Peng and Zhongyuan Zhao

({pmg, zyzhao}@bupt.edu.cn)Beijing University of Posts & Telecommunications

2015.10.29

Outline

Background

System Architecture

Edge Caching and Signal Processing

Open Issues

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Two Paths Toward 5G

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From Cloud Computing to Fog Computing

2000 – 2015 2015 – 2030 ? Prof. Mung Chiang (Princeton University) : A network architecture that uses one or a collaborative multitude of end-user clients or near-user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over backbone networks), and control, configuration, measurement and management (rather than controlled primarily by network gateways such as those in LTE core). Source: fogresearch.org

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Fog Computing Based RANs

Fog Computing Based Radio Access NetworksC-RANSmall cell networkDevice-to-Device3G/4G CellularContent Delivery

NetworksBase stations with

caches

U/C Decouple + Cloud + Cache + D2D

Outline

Background

System Architecture

Edge Caching and Signal Processing

Open Issues

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C-RAN to H-CRAN

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Decouple control plane from C-RANs into HPN HPN is used to alleviate the burdens of fronthaul links and

support the seamless coverage

3G/4G 3G/4GInterworking

H-CRAN to F-RAN

Fog Logic Layer8

M. Peng, S. Yan, C. Wang, “Fog Computing based Radio Access Networks: Issues and Challenges”, Accepted by IEEE Network Mag., Mar. 2015. http://arxiv.org/abs/1506.04233

Topology of Fog Logical Layer

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Mesh Topology Vs. Tree-like Topology

T. Biermann et al. "How Backhaul Networks Influence the Feasibility of Coordinated Multipoint in Cellular Networks", IEEE Wireless Com.

• Multicast leads to higher gains in the mesh-like topology than in the tree-like topology

• The probability of two or more flows sharing the same link is high

• Multicast capability compresses the unicast flows to one single flow, thus requiring a lower data rate.

Comparisons of Different RANs

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Outline

Background

System Architecture

Edge Caching and Signal Processing

Open Issues

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What is the main challenge in C-RANs?

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A dilemma in C‐RANs is that the centralized signal processing conflicts with the edge caching

Signal processing is often here• Large‐scale interference 

management • Global optimal resource 

management

Most edge caches are here• Low cost• Low latency

Cloud

RRH

HPN

But here is the challenge • Heavy burden and complicated 

information exchangingmechanism

Conventional Fronthaul

Solutions in F-RANs

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Local Micro‐Clouds Are Needed

Cloud Processing Units

RRH

HPN

Cloud Caches

Local CachesLocal

Processing Units

Local Micro Cloud

Edge Caches• Shared by RRHs in cluster‐

scale• Higher hit ratio and energy 

efficiency

Local Processing Units• Cluster‐scale interference 

management• Lower complexity and 

latency

Key Idea: Meet In the Half Way Through F‐RANs

Backhaul Loading Mitigation Achieved By Local Cluster Caching

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From the Cloud Caches

Cloud content cache

Micro-cloud in T

RRH UserBackhaul Fronthaul Wireless channel

rBH

From the Local Cluster CachesMicro-cloud in T

UserFronthaul Wireless channel

RRHCluster content cache

QoS exponent Content size

To achieve the same delay experience

A constraint on BH

Performance Evaluation of Local Cluster Caching in F-RANs (1)

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Two Important Metrics in F‐RANs: Throughput and Delay

Effective Capacity: A tractable information theoretical metric considering both perspectives

Defined�as�a�log-moment�generation�function Capture�the�maximum�arrival�rate�that�can�be�supported�by�a�wireless�

channel�with�a�specific�QoS guarantee

Under the block fading channel assumption:

Performance Evaluation of Local Cluster Caching in F-RANs (2)

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System Model

Cloud

Micro-cloud

RRH

User

C-RAN cluster T

Cloud content cache C

Local cluster content cache L

Stores�all�the�content�objects

Stores�some�content�objects�

RRHs�are�modeled�as�a�homogenous�PPP�������with�density�������� users�are�modeled�as�a�homogenous�marked�PPP��������������with�density�

R R U nM U

nM denotes�the�type�of�content�Un requires

Receive�SINR:

Performance Evaluation of Local Cluster Caching in F-RANs (3)

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Effective Capacity of A Typical User：

where

Average Effective Capacity of A Typical Cluster：

where

and

Hit ratio

popularity

Cluster Caching-Based Resource Allocation (1)

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Two important factors: The�conditions�of�radio�access�links

Where�to�get�the�content(Local/Cloud)

RRH�allocation

Resource�Block�(RB)�allocation�

An example

U1 U2 U3 U4 U5

S2 S3 S1 S2 S1

The�contents�required�by�users

S1 S2 S3

RB2 RB1 RB2

RB�allocation�for�each�content

RRH�allocation�in�each�RB

RRH1 RRH2 RRH3 RRH4

RB1 S2 S2 S2 S2

RB2 S1 S1 S2 S1

Main�problems:

RB�and�RRH�allocations�

are�coupled�tightly

Centralized�strategy�is�

not�applicable:

• Based�on�local�

information

• Global�optimization�

is�NP-hard

Cluster Caching-Based Resource Allocation (2)

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RRH allocation:

Increment of effective capacity when Rk serves Sjm

Power consumption

RB allocation:

Effective capacity of contents using RBi

Power consumption

Hedonic coalition formation

Merge and split algorithm

Relationship between utility functionNested coalition formation game

Cluster Caching-Based Resource Allocation (3)

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A nested coalition formation game‐based algorithm:

Merge and Split operation for RB

allocation

Hedonic coalition formation for RRH

allocation

Algorithm converges and D-hp stable

Cluster Caching-Based Resource Allocation (4)

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A suboptimal RRB allocation algorithm :

Shapley�value

expected�marginal�contribution�of�Rj when�it�serves�Si

Utility�function�formulation�of�RB�allocation�based�on�Shapley�value

Interest conflicts of RRH between different RBs arebased on the expected contributions, instead ofaccurate contributions

Can�be�solved�by�using�hedonic�coalition�formation game

Simulation Results

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Proposed�nested�coalition�formation�Alg.�vs.�suboptimal�Alg.�vs�

orthogonal�RB�allocation�vs.�full�RB�reuse�

Effective�capacity�and�energy�efficiency�

Outline

Background

System Architecture

Edge Caching and Signal Processing

Open Issues

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Challenges and Open Issues

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1. Physical layer resource pooling among distributed compressing

2. Edge analytics/sensing, stream mining, and augmented reality

3. Security and privacy of F-RAN4. Distributed data centers and

local storage/computing5. F-RAN architecture for IoT6. Crowd-based network

measurement and inference 7. Client-side network control and

configuration 8. Over The Top (OTT) content

management

1. Performance

Optimization of F-

RANs

2. Edge Caching based

Scheduling

3. F-RANs with SDN for

5G/5G+

Selected Related Publications (1)System Architecture

“Fog Computing based Radio Access Networks: Issues and Challenges”, IEEE Network Mag.“System Architecture and Key Technologies for 5G Heterogeneous Cloud Radio Access Networks”, IEEE Network Magazine “Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges”, IEEE Wireless Communications “Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies”, IEEE Wireless Communications “Self-configuration and self-optimization in LTE-Advanced heterogeneous networks", IEEE Communications Magazine

Channel Estimation“Network Coded Multi-Hop Wireless Communication Networks: Channel Estimation and Training Design", IEEE J. Sel. Areas Commun.“Channel Estimation for Two-Way Relay Networks in the Presence of Synchronization Errors”, IEEE Transactions on Signal Processing“Training design and channel estimation in uplink cloud radio access networks”, IEEE Signal Processing Letters

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Selected Related Publications (2)

Cell Association in C-RANs and Radio Resource Allocation“Ergodic capacity analysis of remote radio head associations in cloud radio access networks”, IEEE Wireless Communications Letters“Contract-based interference coordination in heterogeneous cloud radio access networks”, IEEE Journal on Selected Areas in Communications“Resource allocation optimization for delay-Sensitive traffic in fronthaul constrained cloud radio access networks”, IEEE Transactions on Vehicular Technology“Device-to-Device underlaid cellular networks under Rician fading channels”, IEEE Transactions on Wireless Communications“Resource allocation optimization for delay-Sensitive traffic in fronthaul constrained cloud radio access networks”, IEEE Systems Journal

Survey Paper“Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization”, IEEE Communications Survey & Tutorial.

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Thanks！

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