5G Cloud-RAN and Fronthaul 5G-KS 2018 (IITM Research Park)

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1 5G Cloud-RAN and Fronthaul 5G-KS 2018 (IITM Research Park) RaviKanth Pasumarthy, AVP Technology Vinesh Varghese, Director Technology

2 5G Use-cases & Requirements Ultra Reliable Low Latency Communication (urllc) Massive MTC (mmtc) Traffic Safety & Control Remote Manufacturing Remote Healthcare Self Driving Car Industrial Application & Control Media Everywhere Enhanced Mobile BroadBand (embb) Tracking Smart Meter Fleet Management Smart City Features Ultra Reliable Very Low Latency Very High Availability IMT-2020 Requirements User plane Latency < 1 ms Control plane Latency < 20 ms Reliability % Features UHD Broadcast Very High Bandwidth Widespread Coverage 3D Video, 4K,8K UHD Gigabytes in a Second IMT-2020 Requirements Downlink / Uplink Peak Data Rate 20 Gbps / 10 Gbps User plane latency: 4ms AR/VR High Speed Train Downlink Peak Spectral Efficiency 30 bits/s/hz Uplink Peak Spectral Efficiency 15 bits/s/hz Features Massive numbers Small Data Volumes Low Cost High Battery Life Smart Home/Building IMT-2020 Requirements Connection Density 1 million devices per sq.km Battery life > 10 Years Transforming and linking multiple industries with Telecom Automotive Transport Society Healthcare Factory Utilities Public Safety Multimedia Autonomous Vehicle Online Predictive Maintenance Infotainment Platnooning Intelligent Transport Systems UAV-based surveillance Smart airport Fleet Management Railway Signalling Smart Education Smart Agriculture Smart Grid Remote surgery Wearables Remote consultations / Telemedicine Task automation Predictive Maintenance Mission-critical control Smart metering Smart Utility Mgmt (Water, Gas Metering, power, pollution ) Smart Traffic Mgmt (traffic routing, parking, monitoring..) Mission-critical PTT Mission-critical video (high upload) Mission-critical sensors (drones, smoke detector, security camera) Augmented Reality / Virtual Reality Gaming 2

Key Principles of 5G Network Unification of multiple technologies and network evolution to connect multiple verticals New Air-interface mmwave Massive MIMO and beamforming Next Gen Core Multi RAT

Artificial Intelligence Big-data and Analytics Network Automation Security New Radio (NR) Network Elements Programmable Networks Transport Management Flexible numerology allows multiplexing of

3 Key Principles of 5G Network Unification of multiple technologies and network evolution to connect multiple verticals New Air-interface mmwave Massive MIMO and beamforming Next Gen Core Multi RAT Support MEC Support Network Slicing Virtualized / Cloud- RAN SDN/NFV based Networks Distributed deployment Fronthaul (ideal or nonideal) Mid-haul / Backhaul Resource differentiation Synchronization Artificial Intelligence Big-data and Analytics Network Automation Security New Radio (NR) Network Elements Programmable Networks Transport Management Flexible numerology allows multiplexing of services with qualify and latency requirements and also large SCS for mmwave Spectrum - Allocation of higher frequency bands ensures additional spectrum and wide bandwidth availability, ensuring support for very high data-rates Adaptable air-interface - scalable sub-carrier spacing, variable slot-lengths, scalable TTI, minimize control overhead, short PUCCH (for latency) and long PUCCH (for coverage), advanced channel coding techniques, flexible HARQ Self-contained slot structure (TDD) to reduce latency adaptable UL/DL switching, data/ack in same slot, SRS in every slot etc Ultra-lean design to enhance network energy performance minimizing always-on signals, reduce periodicity of PSS/SSS/PBCH Shortened TTI and processing reduces latency Support of carrier aggregation of upto 16 carriers, Beam-centric design enabling usage of beamforming and massive number of antennas to improve performance Service Based Architecture (SBA) stateless, open, flexible and realization using VNFs, enabling movement and scaling of AFs dynamically CUPS: Separation of control and user-plane SDN: Improved QOS model for packet flow and policies. Helps defining service chaining for SDN based data flow NFV: Orchestration and Virtualization (NFV) de-couple logical function from hardware Slicing logical end-2-end networks tailed to customer needs MEC Support for low-latency services and offloading of data at EDGE. It provides Computing resources, Caching, Low latency and less traffic through core to meet the requirements for use-cases Exposure Functions, APIs, Common API Framework to enable external interworking with 3GPP New 3GPP accesses: wire line-wireless convergence, satellite access. Also allows subscribe to events and have analysing and optimizing network performance and behaviour wrt services being offered 3

4 5G Transport Architecture Terminology SDN/NFV & MANAGEMENT CONTROL & ORCHESTRATION RRU DU ecpri CU/MEC F1 NG CN Aricent FH-TSS 4G - CPRI Aricent FH-TSS UNI Aricent FH-TSS UNI UNI Aricent FH-TSS RoE ecpri ecpri Midhaul RoE Backhaul Backhaul RoE Fronthaul UNI Time Server Latency ~100us ~ us Ref: T-TUT-HOME-2018-MSW-E 1.5 to 10ms UP latency embb 4ms, URLLC 0.5ms Coverage FH 1-20km, typically p2p Fronthaul Midhaul Backhaul MH 20-40km, p2p or p2mp BH upto ~200km, p2mp or mp2mp Network between RRU/RU (Remote Unit) and DU (Distributed Unit) can be CPRI or ecpri or IEEE Network between DU and CU (Centralized Unit) F interface Network between CU and 5G NGC (and EPC) 4

5 5G Cloud-RAN

6 (Typical) RAN Architecture Evolution RRH Ethernet BASE BAND BASE BAND BASE BAND DU BASE BAND fiber fiber BASE BAND BASE BAND BASE BAND F1 CONTROL UNIT CONTROL UNIT CONTROL UNIT TRANSPORT UNIT TRANSPORT UNIT vbbu TRANSPORT UNIT CU Distributed RAN Cloud RAN (e) Cloud RAN Representative figure For typical Macro network deployment, RAN evolution has evolved as Distributed RAN with separate BBU HW per sector connected to RRH via optical interface Cloud-RAN where the BBU is pooled on common (COTS) HW at centralized site and connects to multiple distributed units (RRH) via optical interface (e) Cloud-RAN where CU, DU split is done with standardized interface between CU/DU, and Ethernet as option to connect to RRU With Cloud-RAN based architecture, NFV techniques and data center processing capabilities can be exploited and also enables coordination and centralization in mobile networks 6

7 RAN Split Options RAN -split options helps to reduce the fronthaul requirements and also allow flexible and scalable HW implementations Latency Data-rate DL data 4Gbps 4016Mbps 4000Mbps 4000Mbps 4133Mbps 7a: Gbps 7b: Gbps 7c: Gbps 157.3Gbps RRC PDCP High RLC Low RLC High MAC Low MAC High PHY Low PHY RF Data Option 1 Option 2 Option 3 Option 4 Option 5 Option 6 Option 7 Option 8 RRC PDCP High RLC Low RLC High MAC Low MAC High PHY Low PHY RF Data UL data Latency 3Gbps 3024Mbps 3000Mbps 3000Mbps 5640Mbps 10ms 1-10ms ~100us ~100ms 250us 7a: Gbps 7b: Gbps 7c: Gbps 157.3Gbps c Scenario - 100MHz and 256QAM UL/DL, MIMO layers 8 UL/DL, Number of antenna ports 32,IQ (2*7-16)) UL/DL Latency requirement becomes stringent and data-rates also increase as we move to option-7/option-8 7

8 (Common) RAN Split Options CU Option -2 CU Option -7.x CU Option -8 Most commonly used RAN-Split options are Option-2, Option-6, Option-7.x and Option-8 Option-8 is equivalent to Small-cell type of realization RRC RRM RRC RRM RRC RRM DU PDCP DU PDCP PDCP DU+RU Centralized scheduler possible for options-6 onwards leading to better realization of high-gain coordinated algorithms (like joint scheduling, joint reception, and joint transmission options as part of 5G CoMP) RLC MAC RLC MAC RLC MAC Provides scalable and virtualized architecture options based on CU/DU and RU architecture RU PHY SW RF PHY-high SW RU PHY-low SW RF PHY SW RF Allows for cloud-ran realization with CU running in cloud, and connected to multiple DU DU will also be virtualized and can be scaled-up/down based on the load/traffic/capacity requirements Fronthaul can be based on CPRI or ecpri CPRI CPRI is pre-dominantly used in 4G fronthaul. Max data rate supported in CPRI v7.0 is Gbps (rate 10) For typical LTE scenario of 20MHz, 2x2 DL MIMO, the fronthaul data rate is ~1.96Gbps The IQ data of different AxCs are multiplexed by TDM scheme onto an electrical or optical transmission line, and link is always ON with Constant bit-rate data Specified for point-to-point topology and is more antenna dependent (rather than traffic dependent) ecpri ecpri is used as fronthaul between CU/DU and RU for 5G network, using packet based fronthaul transport network Enables flexible functional decomposition while limiting the complexity of the ere - Supports for Ethernet interface types 10G, 25G, 40G and 100G More traffic dependent rather than antenna dependent, and and Ethernet can handle this with support of statistical multiplexing Enables realization of SDN/NFV based fronthaul 8

9 5G Fronthaul Transport Requirements Handling of very high data-rate requirements Handling of traffic and for different service types (or slices) with varied priorities (include packet prioritization) and quality of service over a unified network Flexibility to scale the bandwidth based on user plane traffic Statistical multiplexing for aggregating traffic from multiple sites Should be traffic dependent and NOT be antenna dependent Support for multiple network architectures Meet stringent Synchronization and Timing requirements for 5G Cost / Performance Trade-off by selecting proper FH/BH suitable for the network deployment Mix of transport technologies optical, packet, microwave Ethernet based solutions provide - Reuse of existing infrastructure, flexibility, statistical multiplexing, flexible routing SDN is a key enabler for converged FH/BH networks in 5G to virtualize the transport network to support slicing and allow a flexible deployment of virtual functions in different places of the network GNSS, 1588, PTP are some of the synchronization options p(d) = offered traffic can be transported without queueing with a probability Ref: 5G transport network requirements for the next generation fronthaul interface 9

10 5G RAN Realization

11 Towards Software Defined Open Network Migration towards Software-defined Open Network philosophy Monolithic, Custom build Solutions Flexible, COTS based Solutions Closed and Proprietary interfaces Open and modular interfaces and flows Built around available network Network defined by Services CU/DU Hardware 5G is accelerating the adoption of commodity HW, disaggregated solution within Telco s. Limited SOC options available for 5G RAN realization, CPU based (x86 or ARM) solution with FPGA used ASIC based solutions for baseband will come into picture once the solutions are verified INTEGRATED DISAGGREGATED OPEN & MODULAR SW Telecom/Datacom Protocols & Application RU Hardware 5G RU designs will be inherently intelligent. Part of PHY runs in RU and also handling for digital beamforming functionality Hardware & Software Software Network Function Abstractions This will also have challenge wrt some of the key considerations of RU design like size, weight, and power HW/ Platform Abstractions Realization of Virtualized cloud native network and moving towards open interfaces White Box HW White Box HW Reduction in overall deployment timelines with disaggregation Pre-tested & Feature Rich HW Platform Independent & Cost Effective Built around usage of open-source and open interfaces in overall solutioning along with 3GPP standards 11

CU/DU Solution Trends Intel Server FPGA ARM based SOC ASIC Power Consumption DSP based SOC Smart-NIC Reference HW solution being proposed in

options that can be explored Scalability Virtualization Deployment / Use-cases xhaul Interfacing Factors influencing CU/DU Solution

Datacentres, with additional that will be spawned to cater to the unique use cases.

12 CU/DU Solution Trends Intel Server FPGA ARM based SOC ASIC Power Consumption DSP based SOC Smart-NIC Reference HW solution being proposed in opensource computing hardware can be used for 5G Network solution till DU Open19, OCP and also Intel Rack Sack Architecture are some of the options that can be explored Scalability Virtualization Deployment / Use-cases xhaul Interfacing Factors influencing CU/DU Solution Synchronization Moving towards Open-Hardware, Open-Software and Open-Interfaces paradigm Existing Central offices being transitioned to Datacentres, with additional that will be spawned to cater to the unique use cases. Reducing the overall TCO is still a priority, Solution around GP processor architectures, still drives the innovation. Solutioning compute, storage, network node elements around the xhaul will be a key driver for innovation. 12

13 INSTANCE INSTANCE INSTANCE INSTANCE INSTANCE INSTANCE INSTANCE INSTANCE SmartNIC/FPGA based acceleration in RAN Fully programmable, FPGA based and enables disaggregated cloud based architecture Optimization at Platform Level and replaces standard NICs HYPERVISOR HYPERVISOR Workload specific Acceleration for better TCO Reducing the load of CPU by offloading to SmartNIC leading to leading to better CPU core utilization vswitch Storage Crypto Compress Security In-line processing of data-plane vswitch Storage Crypto Compress Security Can be programmed and scaled on-demand resulting in a real elastic cloud TRADITIONAL NIC SMART NIC XEON XEON SMARTNIC STORAGE SERVICES SECURITY SERVICES PCIE SNIC SW NETWORK CTRL PLANE SECURITY STORAGE SECURITY Acc & STORAGE Tranport NETWORK CONTROL PLANE NETWORK DATA PLANE NETWORK DATA PLANE FPGA/ASIC 13

14 Programmable data-plane with SmartNIC As VNFs are scale to meet high processing requirements, performance goals for data-plane acceleration can be realized with SmartNICs Distribute and optimize workloads between x86 server and software-reconfigurable, FPGA-based SmartNICs in virtualized environments Performance at lowest power but lacks major flexibility Cores & Optimized SW Match-Action Pipeline Connection tracking Packet Parsing High Performance programmable data planes FPGA Flexibility but at increased cost & power ASIC highest Flexible Architecture Partition to enable high performance, throughput workloads. S ASIC FPGA SOFTWARE ALGORITHMS DATAPLANE PROGRAMMING Programmable SmartNICs help in accelerating critical BB and security workloads and migrating acceleration services 14

15 RU Solution - Solution trends Domain Specific Architecture Enabling Post Moore Era, Domain Specific Architecture Highly Integrated Heterogenous SoC, solution Processing Core (Reduced process Node) for Radio Apps FPGA s for unique digital/acceleration logic SW Programmable Engines, Enabling custom Functions Integrated Data Converters Scalable & high Performance IO Platform SW/Acceleration SW : Common Framework 5G Radio Unit Adaptable Radio Architecture Common SW framework Enabling Scalable RU Product, Reduced Power Envelope (Target within PoE Specs) Better Performance & Smaller Form factor Designs High Through put, low latency Higher Adoption and flexibility leads to lower TCO. 15

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ITSF - TIMING FOR 5G SYNCHRONISATION REQUIREMENTS FOR 5G

ITSF - TIMING FOR 5G SYNCHRONISATION REQUIREMENTS FOR 5G Agenda LTE A-PRO What defines 5G 5G use cases Use cases mapped to capabilities Network Slicing 5G New Radio timeline 5G new interfaces & RAN functional