Heterogeneous Resource Allocation for Hierarchical Software-Defined Radio Access Networks at the Edge 03.03.2015 - 31.12.2016 Prof. Zhu Han Dr. Xianfu Chen Prof. Guoliang Xue Dr. Mehdi Bennis http://www.cwc.oulu.fi/~bennis/ ..Where do we stand.. 5G and the1000x bps/Hz/km2 mmWave + HetNets Bandwidth (10x more Hz) • millimeter Wave - LTE-U, LAA, ASA • mmWave + massive MIMO • • • Smaller antennas for mmWave, seems promising But competition for the DoF offered by antennas Improved SINR via mmWave with high gain antennas, interference becomes negligible? • 5G Effective Density (20x More Loaded BSs/km2): 4G Spectral efficiency (5x more bps/Hz) • • Densifying mmWave cells yields huge gains (SNR plus cell splitting) Can possibly do selfbackhauling! Win-Win More dimensions (massive MIMO?) Interference suppression? (must fight through log) • • Efficient HetNets, more small cells sand WiFi and D2D HetNets + massive MIMO • • Small cells probably not be able to utilize massive MIMO Cost a key challenge Motivation Fact#1 • Exponential traffic growth, limited capacity gains, low ARPU, etc. • Solution: small cells, more antennas (massive MIMO), more spectrum • Bottleneck: Backhaul issues, limited spectrum, scalability, complexity Fact#2 • Trend: from sparse to ultra-dense networks  More cells means more cell edges  Coordination schemes are needed (CRE, ABSF, etc.) -- 40ms for CRE (latency!)  LTE control plane designed for sparse (not dense) deployments  Paradigm shift (Phantom-cell concept, cloud-RAN, FOG/EDGE networking.) This Proposal • Software-defined networking (SDN) to the rescue • Old concept in IT, new paradigm in wireless communication (wireless SDN) • Decoupling control plane and data plane; Abstraction and virtualization are key! • SDN will be instrumental in 5G architecture  In this project focus on SDN at the edge Research Problems • How to design a scalable and optimized SDN-atthe-Edge type of architecture for large-scale radio access networks? • How to adaptively and elastically distribute resources* over space and time, and across various network elements as a function of: – Users’ heterogeneous traffic requirements – Flow urgency, latency, congestion – Fronthaul/backhaul requirements, etc – Ensuring Incentive compatibility when needed • How to optimally decouple control and data planes based on resource usage and latency ? • What are the benefits-costs of SDN-aware RANs?  Research methodologies: learning, game theory, stochastic optimization, matching theory, GibbsMarkov. U-Plane Cloud RAN/SDN Fronthaul *Resources: spectrum, storage, computing, etc. Approach Apps Orchestration & Management Storage SON Spectrum sharing Request Resources Allocate Resources to Small Cells Small cell cluster shared among operators Work Packages Thrust 1: Hierarchical Software Defined RAN At the Edge • Task 1.1: Architecture Design and Abstraction of BS Clusters • Task 1.2: Protocol Design for the Control Network • Task 1.3: Locality and Optimization • Task 1.4: Survivability Thrust 2: Large Scale Elastic Resource Adaptation • Task 2.1: Optimal Resource Adaptation Strategy. • Task 2.2: Enhanced Protocol Design Thrust 3: Learning Techniques for Resource Allocation • Task 3.1: HSDRAN-aware Energy efficiency • Task 3.2: Enhanced Mobility Management • Task 3.3: Dynamic DL/UL TDD Optimization • Task 3.4: Stability, Efficiency, and Optimality Thrust 4: Resource Allocation via Matching Theory • Task 4.1: Extend to Heterogeneous Types of HSDRAN at the edge, such as Vehicular networks • Task 4.2: Extend Resource Allocation to Other Matching Theory Approaches, and Game Theoretic Approaches such as Coalitional Graph Games • Task 4.3: Extend to other Types of Applications such as Popular Content Distribution Steering Group Committee Thus far.. • Mobile data offloading from cellular networks to alternate wireless technologies. • Software defined network (SDN) at the edge: Dynamically route the traffic in a mobile network. • Iterative gather-scatter scheme (Map-reduce) via ADMM. L. Liu, X. Chen, M. Bennis, G. Xue and Z. Han, "A Distributed ADMM Approach for Mobile Data Offloading in Software Defined Network, " IEEE WCNC, New Orleans, LA, Mar. 2015. Plan • • • • FI-FI collaboration FI-USA collaboration Inbound and outbound mobility Collaborate with other projects due to overlap L. Liu, X. Chen, M. Bennis, G. Xue and Z. Han, "A Distributed ADMM Approach for Mobile Data Offloading in Software Defined Network, " IEEE WCNC, New Orleans, LA, Mar. 2015. My book 