Software-defined Infrastructure for Advanced Wireless Testbeds - - PowerPoint PPT Presentation

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WINLAB FALL 2016 RESEARCH REVIEW

Software-defined Infrastructure for Advanced Wireless Testbeds

December 2nd, 2016 Ivan Seskar

Department of ECE Rutgers, The State University of New Jersey seskar (at) winlab (dot) Rutgers (dot) edu

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WINLAB

Technical Challenges

Faster Cellular Radios Access ~1-10 Gbps ~1000x capacity Low- Latency/ Low-Power Access Network For Real- Time IoT

New Spectrum & Dynamic Spectrum Access Next-Gen Mobile Network

Wideband PHY Cloud RAN arch Massive MIMO mmWave (60 Ghz) Multi-Radio access HetNet (+WiFi, etc.) … Custom PHY for IoT New MAC protocols RAN redesign Light-weight control Control/data separation Network protocol redesign …. 60 Ghz & other new bands New unlicensed/shared spectrum Dynamic spectrum access Spectrum sharing techniques Non-contiguous spectrum Network/DB coordination methods …. Mobile network redesign Convergence with Internet Clean-slate Mobile Internet Software Defined Networks Open wireless network APIs Cloud services & computing Edge cloud/fog computing Virtualization, NFV ….

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WINLAB

Wireless Network Softwarization

“Use software programming to design, implement, deploy, manage and maintain wireless network equipment, components and services”

Goals:

Increase re-usability Rapid re-design of network and service architectures Optimize processes in networks Reduce costs Bring added value to infrastructures

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WINLAB

Softwarization in Wireless Networks

In radio access networks - Agility in spatial, temporal and frequency dimensions enabling:

 Fine-grained physical layer/network programmability  Flexibility in spectrum management  Dynamic provisioning  Heterogeneous deployments.

In mobile edge networks - Extend softwarization from the conventional data center to the edge of wireless networks:

 Enable on demand service deployment at the most effective

locations based on application requirements

 Automate service establishment/maintenance mechanisms (in a

timely fashion)

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WINLAB

Capacity, Capacity, Capacity

1000 = 𝑐𝑗𝑢𝑡/𝑡𝑓𝑑 𝑙𝑛2 = Spectral efficiency Cell density Bandwidth Research Viewpoint Industry Viewpoint Air Interface Spectrum Networking

Giuseppe Caire: “Massive MIMO: implementation issues and impact on network optimization” 2016 Tyrrhenian International Workshop on Digital Communications (TIW16) Qian (Clara) Li,Huaning Niu, Apostolos (Tolis) Papathanassiou, and Geng Wu: “5G Network Capacity” IEEE vehicular technology magazine, March 2014

~1-2x ~25x ~20x ~1000x

4G

~3-5x ~5-10x ~1-2x

5G

~1000x

× 𝑑𝑓𝑚𝑚 𝑙𝑛2 𝑐𝑗𝑢𝑡/𝑡𝑓𝑑/𝐼𝑨 𝑑𝑓𝑚𝑚 × 𝐼𝑨 10 × 10 × 10

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WINLAB

Capacity, Capacity, Capacity (cont’d)

Academia Industry Spectral Efficiency/Air Interface Massive MIMO: Serve 10-20 users per sector with 100-200 antennas per BS

• Coordinated Multipoint Tx/Rx
• 3-D/Full-Dimensional MIMO
• New Modulation and/or
• Coding Schemes

Cell Density/Networking Small Cells & Heterogeneous SoNs: From 300m to 90m cell radius

• n average
• Cell Densification
• WLAN Offloading
• Integrated MultiRAT Operation
• Device-to-Device
• Joint Scheduling, Nonorthogonal Multiple Access
• Information and Communication Technology

Coupling Bandwidth/Spectrum mmWaves: From 2-6 GHz to 20-60 GHz

• More Licensed and Unlicensed Spectrum,

mmWaves

• Licensed Shared Access
• Unlicensed Spectrum Sharing

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WINLAB

5G Spectrum Coverage

Mérouane Debbah, “5G: Can we make it by 2020?” 2016 Tyrrhenian International Workshop on Digital Communications (TIW16)

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WINLAB

Basestation Architecture Evolution

Power Amplifier Baseband Transport Control & Mgmt.

Traditional Design

Core Network Power Amplifier Baseband Transport Control & Mgmt. Core Network

Remote Radio Head (RRH) Baseband Unit (BBU)

Current Design Cloud Radio Access Network (CRAN)

Power Amplifi er Power Amplifi er Power Amplifi er Baseband Transport Control & Mgmt. Baseband Transport Control & Mgmt. Baseband Transport Control & Mgmt.

Core Network Core Network

FRONTHAUL

• Common Public Radio

Interface (CPRI)

• Open Base Station

Architecture Initiative (OBSAI)

• Open Radio Equipment

Interface (ETSI-ORI)

BACKHAUL

• S11,R4,R6

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WINLAB

METIS-II Key 5G Architecture Paradigms

RAN Protocol Stack Considerations 5G RAN – a Harmonized and Integrated Landscape of AIVs A Logical CN/RAN Split with evolved Interfaces Moving Functionality from Core Network to RAN: Mobility and Paging in 5G Functionality on a Faster Time Scale: Agile Traffic Steering and Resource Mgmt RAN Enablers for Network Slicing Physical Architecture and Possible Function Splits

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WINLAB

METIS-II: RAN Support for Network Slicing

CN Domain RAN Domain

PDCP RRC

Possibly common radio / spectrum

Service Service Service Service

MUX

MUX MUX

PDCP RRC PDCP RLC RRC PDCP RLC RRC

Possibly common lower MAC

(but with slice-specific and/or service-specific behaviour)

RLC RLC

MUX MUX

Possibly slice-specific MAC scheduler Possibly slice-specific MAC scheduler

Possibly common PHY

(but with slice-specific and/or service-specific behaviour)

Service Service Service Service

MUX MUX

Completely independent realization of network slices in the core network Likely individual logical protocol instances for different services, highly tailored to these. Possibly slice-specific processing of services Likely multiple slices and the services therein multiplexed into common instances for lower MAC, PHY, and sharing the same radio. Note that MAC

• r PHY functions may still be highly

slice- or service tailored

Example network slice (E2E logical network) It is foreseen that network slices will be used to form logical E2E networks for particular business constellations The 5G RAN should

• be slice-aware
• Offer means for slice

isolation and protection

• Provide means for

efficient resource reuse Key questions are yet the assignment of devices to slices and multi-slice connectivity.

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WINLAB

METIS-II: Function Splits

PDCP RLC (asynch.) RLC (synch.) MAC (e.g. RRM) MAC (e.g. HARQ) FEC Scrambling

Modulation, Layer mapping, Precoding

Resource element mapping & IFFT Resource element mapping & IFFT Resource element mapping & IFFT D/A Conversion D/A Conversion D/A Conversion Antenna Antenna Antenna M0 Coaxial cable M1 I/Q samples in time domain (e.g. CPRI) M2 I/Q samples in frequency domain M3 Coded user data M6-M4 Uncoded user data (without H- ARQ retransmissions) S1*

Scaling with bandwidth and # of antennas Scaling with user data rates Requiring low-latency fronthaul Relaxed latency requirements

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WINLAB

Typical Fronthaul BW Requirements

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WINLAB

METIS-II: Deployment Scenarios

Scenario 1

Standalone access nodes Each node with one or more (co- located) air interfaces Non-ideal backhaul* (optional) Non-ideal backhaul* Non-ideal backhaul*

Central Cloud / Aggregation point Site A: BB- Processing + RF

RF

(e.g. LTE-A radio)

RF (optional

e.g. novel 5G radio)

RF

(e.g. novel 5G radio)

RF (optional

e.g. LTE-A radio)

Site B: BB- Processing + RF

Scenario 2

Central baseband processing unit for high number of access nodes Ideal fronthaul Ideal fronthaul

Central Cloud / Central BB- Processing Site A: Optional BB- Processing + RF

RF

(e.g. LTE-A radio)

RF (optional

e.g. novel 5G radio)

RF

(e.g. novel 5G radio)

RF (optional

e.g. LTE-A radio)

Site B: Optional BB- Processing + RF

Scenario 3

Local baseband processing unit for low to medium number of access nodes Ideal fronthaul Ideal fronthaul

RF

(e.g. LTE-A radio)

RF (optional

e.g. novel 5G radio)

RF

(e.g. novel 5G radio)

RF (optional

e.g. LTE-A radio)

Local BB- Processing

Non-ideal backhaul*

Site A: Optional BB- Processing + RF Site B: Optional BB- Processing + RF Central Cloud / Aggregation point Local BB- Processing

Non-ideal backhaul*

Scenario 4

Self-back/fronthauling scenario Wireless self- back/fronthau l

RF

(e.g. LTE-A radio)

RF (optional

e.g. novel 5G radio)

Non-ideal backhaul (Ideal fronthaul) Site B taken from Scenario 1 - 3

Site B: (Optional) BB- Processing + RF

RF

(e.g. novel 5G radio)

RF (optional

e.g. LTE-A radio)

RF

(e.g. novel 5G radio)

RF (optional

e.g. LTE-A radio)

Site D: (Optional) BB- Processing + RF

Wireless self- back/fronthaul

Site C: (Optional) BB- Processing + RF

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WINLAB

Example: OpenAirInterface eNodeB and UE

Challenge : Efficient LTE implementation that uses general-purpose x86 processors (GPP) for base-band processing

 front-end, channel decoding, phy procedures, L2 protocols

 Key elements:

 Real-time extensions to Linux OS

 x86-64 multicore arch

 Real-time data acquisition to PC  SIMD optimized integer DSP

 64-bit MMX  128-bit SSE2/3/4  256-bit AVX2  iFFT/FFT, Channel Estimation, Turbo Decoding

 SMP Parallelism

 Master-worker model

Courtesy: Navid Nikaein, Eurecom/Open Air Interface

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WINLAB

OAI Roadmap: Toward Software-defined 5G Network

 Cloud-native 5G networks

 Phase 1: Stateless through distributed shared memory, multitenancy  Phase 2: Mircoservice Architecture and NFV  Supported projects: FP7 MCN, FUI ELASTIC

 Network Orchestration

 Approach 1) Openstack and heatstack orchestrator  Approach 2) Juju modeling for service-oriented deployment

(https://jujucharms.com/q/oai)

 Supported project: FP7 MCN, FP7 FLEX, Canonical partnership program

 Network Programmability  network slicing

 Agent-controller protocol and southband API in support of SDN+MEC

– agents: in charge of network function monitoring and programmability – Network controller: network abstraction (network state graphs), network application

• realtime, standalone mode or as a plugin

 Supported projects: H2020 Coherent, H2020 Q4Health, ETSI MEC PoC

Courtesy: Raymond Knopp, Euricomm

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WINLAB

Fronthaul At Scale: ORBIT Testbed Massive-MIMO



40 USRP X310s



Available FPGA resources:



RF 2 x UBX-160 (10 MHz - 6 GHz RF, 160 MHz BB BW)



2 x 10G Ethernet for fronthaul/interconnect



Four corner movable mini-racks (4 x 20 x 20 -> 1 x 80 x 80)



> 500+ GPP Cores/CloudLab Rack



Number of GPU platforms



32x40G SDN aggregation switch Resource Type Number DSP48 Blocks 58K Block Rams (18 kB) 14K Logic Cells 7.2M Slices (LUTs) 1.5M

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WINLAB

Fronthaul (M1) Requirements for Massive MIMO

Current BW Requirement Single-Ethernet/ Single-host Limit ???

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WINLAB

City Scale: CRAN Expanded

Massive CPU,GPU,FPGA Cloud

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WINLAB

Rules have officially become effective as of January

14, 2016.

 But: new forms and reporting WEB site are not yet up

(will take a few more weeks according to “reliable sources”)

 Perfect fit: intended to foster innovation  Has significant implication on wide area

experimentation especially with SDRs and non- traditional RF front-ends.

 (the rule also includes two other experimental licenses: the Medical

Testing License and the Compliance Testing License)

Program Experimental License (cont’d)

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WINLAB

“Missing Link”: Outdoor Deployable SDR Wireless Units?

Local processing SDR front-end RF “Firewall” Modest power amplifier (GENI) Rack Wideband Antenna (Open Programmable) COTS BS/AP

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WINLAB

www.winlab.rutgers.edu www.orbit-lab.org www.geni.net wiser.orbit-lab.org wimax.orbit-lab.org www.openairinterface.org metis-ii.5g-ppp.eu

More Info @