Fog Computing Based Radio Access Networks: Issues and Challenges

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1 Fog Computing Based Radio Access Networks: Issues and Challenges Mugen Peng and Zhongyuan Zhao ({pmg, Beijing University of Posts & Telecommunications

2 Outline Background System Architecture Edge Caching and Signal Processing Open Issues 2

3 Two Paths Toward 5G 3

4 From Cloud Computing to Fog Computing ? 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 4

5 Fog Computing Based RANs Fog Computing Based Radio Access Networks C-RAN Small cell network Device-to-Device 3G/4G Cellular Content Delivery Networks Base stations with caches U/C Decouple + Cloud + Cache + D2D

6 Outline Background System Architecture Edge Caching and Signal Processing Open Issues 6

7 C-RAN to H-CRAN 3G/4G Interworking 3G/4G Decouple control plane from C-RANs into HPN HPN is used to alleviate the burdens of fronthaul links and support the seamless coverage 7

8 H-CRAN to F-RAN M. Peng, S. Yan, C. Wang, Fog Computing based Radio Access Networks: Issues and Challenges, Accepted by IEEE Network Mag., Mar Fog Logic Layer 8

9 Topology of Fog Logical Layer Mesh Topology Vs. Tree-like Topology 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. T. Biermann et al. "How Backhaul Networks Influence the Feasibility of Coordinated Multipoint in Cellular Networks", IEEE Wireless Com. 9

10 Comparisons of Different RANs 10

11 Outline Background System Architecture Edge Caching and Signal Processing Open Issues 11

12 What is the main challenge in C-RANs? Signal processing is often here Large scale interference management Global optimal resource management Conventional Fronthaul Cloud But here is the challenge Heavy burden and complicated information exchanging mechanism Most edge caches are here Low cost Low latency RRH HPN A dilemma in C RANs is that the centralized signal processing conflicts with the edge caching 12

13 Solutions in F-RANs Local Micro Clouds Are Needed Local Processing Units Cluster scale interference management Lower complexity and latency Edge Caches Shared by RRHs in clusterscale Higher hit ratio and energy efficiency Local Processing Units Local Micro Cloud Cloud Processing Units Local Caches RRH Cloud Caches HPN Key Idea: Meet In the Half Way Through F RANs 13

14 Backhaul Loading Mitigation Achieved By Local Cluster Caching From the Cloud Caches Cloud content cache Micro-cloud in T Backhaul Fronthaul Wireless channel RRH r BH User QoS exponent From the Local Cluster Caches Micro-cloud in T Content size Cluster content cache Fronthaul RRH Wireless channel User To achieve the same delay experience A constraint on BH 14

15 Performance Evaluation of Local Cluster Caching in F-RANs (1) Two Important Metrics in F RANs: Throughput and Delay Effective Capacity: A tractable information theoretical metric considering both perspectives Definedasalog-momentgenerationfunction Capturethemaximumarrivalratethatcanbesupportedbyawireless channelwithaspecificqos guarantee Under the block fading channel assumption: 15

16 Performance Evaluation of Local Cluster Caching in F-RANs (2) System Model Cloud Cloud content cache C Storesallthecontentobjects Local cluster content cache L Micro-cloud Storessomecontentobjects RRH ReceiveSINR: User C-RAN cluster T RRHsaremodeledasahomogenousPPPwithdensity R R usersaremodeledasahomogenousmarkedpppwithdensity U M n M denotesthetypeofcontentu n requires n U 16

17 Performance Evaluation of Local Cluster Caching in F-RANs (3) Effective Capacity of A Typical User: where Average Effective Capacity of A Typical Cluster: where Hit ratio and popularity 17

18 Cluster Caching-Based Resource Allocation (1) Two important factors: Theconditionsofradioaccesslinks Wheretogetthecontent(Local/Cloud) An example Thecontentsrequiredbyusers U 1 U 2 U 3 U 4 U 5 RRHallocation ResourceBlock(RB)allocation Mainproblems: RBandRRHallocations arecoupledtightly S 2 S 3 S 1 S 2 S 1 RBallocationforeachcontent S 1 S 2 S 3 RB 2 RB 1 RB 2 RRHallocationineachRB RRH 1 RRH 2 RRH 3 RRH 4 RB 1 S 2 S 2 S 2 S 2 Centralizedstrategyis notapplicable: Basedonlocal information Globaloptimization isnp-hard RB 2 S 1 S 1 S 2 S 1 18

19 Cluster Caching-Based Resource Allocation (2) RRH allocation: Hedonic coalition formation Increment of effective capacity when R k serves S jm Power consumption RB allocation: Effective capacity of contents using RB i Power consumption Relationship between utility function Merge and split algorithm Nested coalition formation game 19

20 Cluster Caching-Based Resource Allocation (3) 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 20

21 Cluster Caching-Based Resource Allocation (4) A suboptimal RRB allocation algorithm : Shapleyvalue expectedmarginalcontribution ofr j whenitservess i UtilityfunctionformulationofRBallocationbasedonShapleyvalue Interest conflicts of RRH between different RBs are based on the expected contributions, instead of accurate contributions Canbesolvedbyusinghedoniccoalitionformation game 21

22 Simulation Results ProposednestedcoalitionformationAlg.vs.suboptimalAlg.vs orthogonalrballocationvs.fullrbreuse Effectivecapacityandenergyefficiency 22

23 Outline Background System Architecture Edge Caching and Signal Processing Open Issues 23

24 Challenges and Open Issues 1. Physical layer resource pooling among distributed compressing 2. Edge analytics/sensing, stream mining, and augmented reality 3. Security and privacy of F-RAN 4. Distributed data centers and local storage/computing 5. F-RAN architecture for IoT 6. 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+ 24

25 System Architecture Selected Related Publications (1) 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 25

26 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. 26

27 Thanks! Questions & Comments 27