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In the name of God, the most compassionate, the most merciful ... هیچکس تنها نیست 5G ARCHITECTURE VISION Mohammad Movahhedian, Ph.D., MIET, MIEEE www.mci.ir 5G Seminar at ITRC 14 Jun. 2015 Outline Introduction, Motivations & Current Trends of Mobile Networks 5G Challenges & Design Parameters The Vision for 5G Architecture Logical Network Layers: Radio Network & Network Cloud Use of SDN & NFV to Dynamically Deploy & Scale Network Functions Lean Protocol Stack C/U-Plane Split for Independent Coverage & Capacity Support for Multiple Devices & Group Mobility Data-Driven Network Intelligence Issues D2D User-Centric IP Mobility Management A New Proposition for Control Plane Conclusions 2 Introduction & Current Trends of Mobile Networks Introduction Unlike successive evolutionary releases of 3GPP starting from 3GPP-R99 all the way to LTE-A (i.e. 3GPP Rel.10) and even above, 5G is a REVOLUTION in the ICT filed Overall IP traffic is expected to grow 23% annually between 2012-2017; this growth is 66% over the same period for mobile IP traffic With this growth in mobile IP traffic, the primary objective of 5G architecture is to bring a high-capacity, agile, low-cost solution to ensure both user-satisfaction and MSP1 profitability 5G will efficiently enable new ultra reliable, dependable, secure, privacy-preserving, and delay-critical services to everyone & everything 5G is necessitated by new & emerging use-cases, e.g., high-resolution video streaming, tactile Internet, road safety, remote monitoring and real-time control The above use-cases place new requirements on the network related to throughput, E2E latency, reliability and robustness of the network At the same time, ultra-dense small cell deployments and new technologies such as mMIMO2, SDN3, and NFV4 provide a motivation to rethink the fundamental design principles toward 5G 1 Mobile service provider 2 Massive MIMO 3 4 Software defined networking Network function virtualization 4 Current Trends (1/2) A call for a new generation of mobile networks seems necessary due to increased penetration of smart devices, better hardware, better user interface design, compelling services (e.g., video streaming) and desire for anywhere, anytime, anything high-speed connectivity. More than 70% of this data consumption occurs indoors The signaling traffic is increasing 50% faster than data traffic OTT1 players provide services and apps with much higher agility, some of which compete directly with core operator services (e.g., voice, SMS, and MMS). 1 Over-the-top 5 Current Trends (2/2) The battery life of devices and a seamless experience across multiple devices (or a device ecosystem) is also an important issue in end-users’ perspective IoT1 brings a 3rd dimension, i.e. anything, to the 2D connectivity plane (i.e., anytime & anywhere) Smart wearable devices, smart home appliances, smart cars and cognitive mobile objects promise a hyper-connected smart world On the other hand, many of the envisaged applications impose requirements, such as, very low latency and high reliability, that are not easily supported by current networks To cope with such evolving demands, operators are deploying more localized capacity, in the form of small cells to improve capacity and traffic offloading to fixed networks, e.g, via WiFi To optimize network usage for better QoE in a fair manner, mobile networks are equipped with DPI2, caching & transcoding, all of which could increase CAPEX3 & OPEX4 significantly. With the increasing complexity and associated costs, several concepts and technologies that have proved useful to the IT sector are proposed such as NFV & SDN 1 Internet-of-things 2 Deep packet inspection 3 4 Capital expenditure Operational expenditure 6 5G Challenges & Design Parameters Challenge #1: Higher Capacity Spectrum Massive/3D MIMO Enabler New Air-Interface Capacity Use high frequencies and other spectrum options (e.g., pooling, aggregation) Design new air interface, new multiple access scheme and L1/L2 techniques that can be optimized for high frequencies, latency and massive connectivity All optical networks Small cells Optical transmission and switching wherever possible. Local offload Bring communicating endpoints closer together. C/U plane split 3rd party/user deployment models Design principle in 5G context Challenge x 1000 > 70% indoor Address coverage & capacity separately Minimize functionalities performed by access points 8 Challenge #2: Higher Data-Rate Spectrum Massive/3D MIMO Enabler New Air-Interface Data-rate x 10-100 Use high frequencies and other spectrum options (e.g., pooling, aggregation) Design new air interface, new multiple access scheme and L1/L2 techniques that can be optimized for high frequencies, latency and massive connectivity All optical networks Small cells Optical transmission and switching wherever possible. Local offload Bring communicating endpoints closer together. C/U plane split Design principle in 5G context Challenge Address coverage & capacity separately 9 Challenge #3: E2E Latency E2E Latency < 5 ms New Air-Interface Design new air interface, new multiple access scheme and L1/L2 techniques that can be optimized for high frequencies, latency and massive connectivity All optical networks Optical transmission and switching wherever possible. Local offload Caching/prefetching/CDN Design principle in 5G context Enabler Challenge Bring communicating endpoints closer together. 10 Challenge #4: Massive No. of Connections Challenge Massive No. of Connections x 10-100 Use high frequencies and other spectrum options (e.g., pooling, aggregation) Enabler New Air-Interface Local offload NFV/SDN/Cloud Energy Efficient Hardware Energy Management Techniques Design new air interface, new multiple access scheme and L1/L2 techniques that can be optimized for high frequencies, latency and massive connectivity Bring communicating endpoints closer together. Design principle in 5G context Spectrum Minimize number of network layers and pool resources as much as possible Maximize energy efficiency across all network entities 11 Challenge #5: Cost Challenge Local offload C/U plane split Cost Sustainable Bring communicating endpoints closer together. Address coverage & capacity separately Enabler 3rd party/user deployment models Simple access points Energy Efficient Hardware Energy Management Techniques SON Traffic Management Big data-driven NI Minimize number of network layers and pool resources as much as possible Minimize functionalities performed by access points Minimize functionalities performed by access points. Design principle in 5G context NFV/SDN/Cloud Maximize energy efficiency across all network entities Use an intelligent agent to manage QoE, routing, mobility and resource allocation. Redesign NAS protocols, services and service complexity. 12 Challenge #6: QoE Enabler Caching/prefetching/CDN Energy Management Techniques Traffic Management Big data-driven NI QoE Consistent Bring communicating endpoints closer together. Use an intelligent agent to manage QoE, routing, mobility and resource allocation. Redesign NAS protocols, services and service complexity. Design principle in 5G context Challenge 13 The Vision for 5G Architecture 5G Architecture Vision (A Candidate) Operator Services API1 C-plane path U-plane path Radio-access link Backhaul link (wired) Fronthaul link (wireless) OTT2 Data-Driven NI NFV-enabled NW7 Cloud • Extraction of actionable insights from big data • Orchestration of required services and functionalities (e.g., traffic optimization, context-aware QoE8 provisioning, caching, ...) Internet NI3 Lean Protocol Stack CPE4 • Authentication • Mobility management • Radio resource control • NAS11-AS12 integration UPE5 • Gateway • U-plane mobility anchor • OTA10 security provisioning Resource Pooling RRU1 • L1/L2 functions • Low CF14 with NOMA15 • High CF with mMIMO16 RRU2 • L1/L2 functions • High CF with mMIMO for capacity • Connectionless, contentionbased access with new waveforms for MTC13 asynchronous access Macro Cell Small Cell • L1/L2 functions • Super high CF & unlicensed spectrum for local capacity • Dual Connectivity • Switched on, on • Independent C/U demand plane mobility Area Controller C/U Plane Split • Nesting & Relaying to support low-power devices, nomadic cells & group mobility 1 5 9 13 2 6 10 14 Application programming interface Over-the-top player 3 Network intelligence 4 C-plane entity U-plane entity Device-to-device 7Network 8 Quality of experience Anything as a service Over the air 11 Non-Access Stratum 12 Access Stratum • D2D6 under Network Control Machine-type communication Carrier frequency 15 Non-orthogonal multiple-access 16 Massive MIMO 15 5G Architecture Vision Logical Network Layers (RN1 vs. NC2) The proposed architecture consists of 2 logical sub-networks, RN & NC. At RN, different types of BSs and RRUs perform only Layers 1 & 2 functionalities for complexity reduction, thereby making dense deployments affordable to deploy and operate At NC, higher layer functionalities are performed through UPE & CPE, which allows for resource pooling, reducing over-provisioning & under-utilization of network resources The NC is a logical entity with physical realization that can be tailored to meet specific needs. 1 2 Radio Network Network Cloud 16 5G Architecture Vision NI responsible for: • Data collection • Analysis and control over network entities Logical Network Layers (RN vs. NC) Realization of a 5G NC Data Centre 4 NFV-enabled NW Cloud Data Centre 3 Data Centre 1 RRU3 C-plane path U-plane path Backhaul link (wired) Fronthaul link (wireless) Data Centre 2 Small Cell RRU1 CPE Control-plane entity UPE User-plane entity Macro Cell 17 5G Architecture Vision RN Layer Impact of New AirInterface on Data-rate Achievable Rate 𝑅= 𝐵𝑖 𝑖 • More spectrum (esp. at higher freq.) • Carrier aggregation • Full-duplex radio • Cognitive Radio • Dynamic spectrum sharing • NOMA transmission log 1 + 𝑗 𝑘 𝑃𝑗,𝑘 𝑃interference + 𝑁0 • Coordinated scheduling • 3D Beamforming • Interference suppression/utilization • Higher order Modulation • Massive MIMO • Multi-cell cooperation 18 5G Architecture Vision NFV & SDN Exploitation of NFV & SDN concepts allows for the CPE & UPE functions to be deployed quickly as well as network orchestration and scaling to be performed on demand For example in the incidence of a “flash-crowd”, additional resources can be borrowed from other data-centers within the cloud. Spare cloud resources can be lent out when demand is low, whereas additional resources can be rented through IaaS1 business models during peak hours. Furthermore, a broad range of “as a service” business models based on providing specific network functionalities as a service (i.e., XaaS2) could also be envisioned The complete or specific parts of the network could be provided to customers (e.g., network operators, OTT players, enterprises) that have specific requirements, e.g., in a “MNaaS3”, “RNaaS4”, “UPEaaS5”, “CPEaaS6”, “NIaaS7”, etc. model Besides the XaaS business models that could be facilitated, the flexibility of a cloud, coupled with SDN and NFV technologies, also makes the network easier, faster, and cheaper to deploy and manage 1 4 2 5 infrastructure as a service Anything as a service 3 Mobile Network as a service Radio Network as a service UPE as a service 6 CPE as a service 7 NI as a service 19 5G Architecture Vision Lean Protocol Stack Virtualization converts the interfaces between network functionalities into interfaces between software units As one indication of NFV, the AS & NAS protocols within the C-plane can be integrated As another implication, the connection establishment procedure can be significantly simplified by requiring a handshake only between the peer entities of a single protocol. As a 3rd indication, the Bearer-based QoS management could also be replaced by simple IP marking On the U-plane side, however, virtualization is more difficult due to the sheer volume of data handled on this plane, but mostly feasible by 2020 via advancements in technology The U-plane stack can potentially become leaner by removing ciphering as this is implemented by TLSoIP1. Generally E2E security solutions are more efficient than encryption of segments over a path, but make some intelligent content-centric mechanisms (e.g., DPI2 & Caching) dysfunctional. 1 2 Transport layer security over IP Deep packet inspection 20 5G Architecture Vision C/U Planes Split Coverage & capacity are provided separately in the proposed architecture Macro-cells are responsible for the provisioning of coverage on the licensed lower frequencies , integrating NOMA1 and SIC2 for capacity enhancement Small cells offers localized capacity on licensed & unlicensed bands on both low and high frequencies, both at indoors and as outdoor hotspots, using mMIMO To save energy, small cells can dynamically be switched on and off, inspired by the highly variable traffic loads served by small cells Separation of coverage & capacity will enable independent mobility of C & U planes which in turn requires simultaneous connectivity support to multiple BSs at the UE side. 1 2 Non-orthogonal multiple access Successive (Serial) interference cancelation 21 5G Architecture Vision Support for Multiple Devices & Group Mobility via Relaying & Nesting To support group mobility, e.g. in moving vehicles, all transmissions within the group are aggregated at one or more entities (that have higher capabilities and resources) and relayed to the NC via wireless backhaul On the other hand, small wearable devices (with limited resource and battery) connect to the network non-transparently, via one or more devices with higher resource/capability In this way, huge number of devices with a diverse range of capabilities can be connected to the network in a scalable and efficient manner 22 5G Architecture Vision Data-Driven NI The 5G candidate architecture could allow the NC to collect various data types, i.e., usercentric, NW-centric and context-centric data The NC then is able to use intelligent algorithms for efficient resource management, mobility management, local offload decisions (e.g. via D2D), QoE management, traffic routing, etc. Moreover, providing APIs to the NC can be used for commercial purposes such as selling knowledge on “NCaaS1 ” to OTT players to optimize their QoS to the end users 1 Network conditions as a service 23 Issues Current NW Intermediate step Legacy CN Legacy RAN MME S/P-GW CN Cloud Migration via appropriate interfaces between legacy and new clouds Future NW Operator Services API OTT NFV-enabled NW Cloud BBU RAN Cloud Internet NI CPE UPE • Traffic management • OTA security • Seamless mobility • Device/ID management • Device discovery • Finding the right balance between network cloudification/ distribution RRU1 Minimum functionalities? • Deployment & spectrum • Discovery & measurement • Network synchronization C-plane path U-plane path Radio-access link Backhaul link (wired) Fronthaul link (wireless) RRU2 Support different radio interfaces Macro Cell Small Cell Support for V2X communication Support cognitive mobile objects 24 5G Architecture Vision Modeling Parameters Simulation parameter Area under simulation # of Macrocells Value 750m x750m (dense urban area) 7 Inter-cite distance 500m # of sectors per cell/users per sector 3 -- 30 Users' speed 3 Km/h Operating freq./BW for macro-cells # of transmit antennas at macro-cells (TX power/antenna gain) for macro-cells # of small cells per sector Operating freq./BW for small-cells # of TX antennas per small cell (TX power/antenna gain) for small-cells # of antennas at user terminal side 2 GHz-- 20 MHz 2 (49 dBm, 14 dBi) 12 20 GHz -- 1 GHz 64 (30 dBm, 5dBi) 4 25 5G Architecture Vision Modeling Results Throughput vs. time (Source: IEEE) Classified UE ratio (in terms of data-rate) vs. time (Source: IEEE) 26 5G Architecture Vision Network-Controlled D2D D2D techniques aiming at increasing the coverage & capacity have been existing for decades e.g., in ad hoc mode in IEEE 802.11, but have never become mainstream The main purpose of considering them in the context of 5G is to clarify whether & to what extent D2D transfers can be integrated into 5G networks The adoption of D2D in 5G networks is driven by 4 main use-cases: 1. Safety applications & disaster scenarios 2. Novel commercial ProSe1 scenarios 3. Network traffic offloading 4. Industrial automation & M2M2 communication 2 important questions: 1) why should we expect D2D to be successfully ushered into cellular networks? 2) Are small-cells & D2D alternative or complementary to each other? Here, in the context of 5G, we consider a network-controlled, in-band and underlay D2D 1 2 Proximity service Machine to machine 27 5G Architecture Vision Network-Controlled D2D Proposed System Model AC1 User #1 eNB User #2 1- Status report: Propagation is good 2- Policy: Use I2D 3- Content request: I need content c1 4- Indication: Use I2D for this user 6- Policy: From now on, D2D can be used for c1 8- Indication: Use D2D for this user 1 Area Controller 5- Resource decision: Use resource r1 in I2D mode 7- Content request: I need content c1 9- Resource decision: Use resource r2 in D2D mode 28 5G Architecture Vision Network-Controlled D2D Scheduling Through MMPF1 No START Calculate the priority score for user 𝑢(𝑘) for all k: 𝐴𝑐ℎ𝑖𝑒𝑣𝑎𝑏𝑙𝑒 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑢 (𝑘) 𝑆𝑢(𝑘) = 𝑑𝑎𝑡𝑎 𝑑𝑜𝑤𝑛𝑙𝑜𝑎𝑑𝑒𝑑 𝑏𝑦 𝑢(𝑘) Result 𝑢(𝑘) has the highest score Content needed by 𝑢(𝑘) is available via D2D? Yes Schedule RB “𝑟𝑗 ” for transferring 𝑏 → 𝑢(𝑘) via I2D where 𝑏 is a macro/micro BS covering 𝑢(𝑘) with the best RSSI2 Schedule RB “𝑟𝑖 ” for transferring 𝑢(𝑘) → 𝑢(𝑘) Where 𝑢(𝑘) is the closest user to 𝑢(𝑘) END 1 2 Multi-modal proportional fairness Received signal strength indicator 29 5G Architecture Vision Network-Controlled D2D Modeling Parameters Simulation parameter Value Service Area (Km2) # of Macrocells # of Microcells # of macro-eNodeBs Macro eNB inter-sire distance (m) # of users in the area 12.34 57 228 19 500 3420 # of users uniformly distributed within 50 meters of each micro-eNB (power/antenna height) for macroeNB (power/antenna height) for microeNB (power/antenna height) for UE (Centre frequency/BW) # PRBs per subframe MIMO size Peopagation loss for Macrocells Peopagation loss for Microcells Peopagation loss for D2D # of total content items # of content categories Total simulation time max. allowable distance for D2D Content Class Software updates Video Viral 10 (43 dBm, 25m) (30 dBm, 10m) (23 dBm,1.5 m) (2.6GHz, 10MHz) 50 2x2 13.5 + 20𝑙𝑜𝑔10 𝑓𝑐 + 39𝑙𝑜𝑔10 𝑑 22.7 + 26𝑙𝑜𝑔10 𝑓𝑐 + 36.7𝑙𝑜𝑔10 𝑑 27 + 20𝑙𝑜𝑔10 𝑓𝑐 + 22.7𝑙𝑜𝑔10 𝑑 21 3 (S/W updates, Video, Viral) 30 sec. Variable between 10-100 m # of content items Size (Mb) Deadline (sec.) Request Rate (Copies/ms) 10 10 1 12 3 3 4 1 1 0.1 0.1 50 30 5G Architecture Vision Network-Controlled D2D Modeling Results Result 1: Amount of transferred data through each paradigm. (Source: IEEE) Result 2: Amount of transferred data for each paradigm when the number of microcells is reduced by 50%. (Source: IEEE) Result 3: Data transferred through each paradigm for different 31 content classes. (Source: IEEE) 5G Architecture Vision Terminal-Centric IP Mobility Management One of the architecture pillars of mobile network is IP mobility management, currently based on CENTRALIZED data-path management The considerable amount of IP traffic growth on mobile terminals (66% every year over 2012-17), calls for a suitable IP mobility architecture In the legacy network architectures, a mobile terminal’s traffic is routed via a centralized node in the core network (e.g. the PDN-GW in LTE architecture) The architecture for the next generation of mobile networks needs to take into account the distributed nature of IP routing via provision of an intelligent mobile data-path management 32 5G Architecture Vision IP Anchor Terminal-Centric IP Mobility Management Actual data-path Legacy vs. Optimum IP Routing Architecture Peer 2 Optimum data-path Peer 3 Core NW cloud Peer 1 PDN-GW/S-GW Internet Cloud Access NW Cloud Base Stations User Terminal 33 5G Architecture Vision IP Anchor Terminal-Centric IP Mobility Management Data-path IP Mobility Distribution over Location of IP Anchors Peer 2 Peer 3 Core NW cloud RGW PDN-GW/S-GW Remote Network/ISP Peer 1 Internet Cloud XGW1 XGW2 Access NW Cloud Base Stations 34 5G Architecture Vision IP Anchor Terminal-Centric IP Mobility Management Data-path Per-flow Based IP Mobility Management Peer 3 Peer 2 Core NW cloud PDN-GW/S-GW Peer 1 Internet Cloud XGW1 XGW2 Access NW Cloud Base Stations Flow 1 using IP1 Flow 2 using IP2 Flow 3 using IP3 35 5G Architecture Vision IP Anchor Terminal-Centric IP Mobility Management Data-path IP Mobility Management Based on Communication Layers Peer 3 Peer 2 Using MPTCP Using SIP Peer 1 Internet Cloud Using Access Anchoring XGW1 XGW2 Access NW Cloud Base Stations Flow 1 using IP1 Flow 1 using IP2 Flow 1 using IP3 36 5G Architecture Vision Terminal-Centric IP Mobility Management UE-Centric IP Mobility Management START Mobility support required? No No mobility solution chosen Yes Fixed IP required? Yes Go for core anchoring No L4+ solution available? Yes Go for L4+ solution No Remote anchoring available? Yes Go for RGW No Access anchoring available? Yes Go for XGW END No 37 5G Architecture Vision A New Proposition for Control-Plane Another bottleneck of today’s mobile network architectures is its complex & non-agile control-plane NFV (i.e., Cloudification) & SDN (i.e. programmability) based network architecture with hierarchical network control capabilities are proposed to allow for different grades of performance and complexity in offering CN services and provide service differentiation NFV guarantees lower CAPEX1 & OPEX2 and creates the capabilities for MSPs to launch new services in the time scales that an OTT3 player would do. In an SDN-based architecture, complex CPFs4 are removed from forwarding elements and placed behind a logically centralized controller, enabling control differentiation of different flows The hierarchy of controllers allow for locally optimized control decisions and acts as a new dimension in service provisioning 1 2 Capital expenditure Operational expenditure 3 Over-the-top 4 Control-plane functions 38 5G Architecture Vision A New proposition for Control-Plane A clarifying example Coherence time @ 2 GHz Control functionality: Scheduling of a wireless resource based on CQIs1 received from users 90 ms for 3 Km/h 1.1 ms for 250 Km/h Coherence time @ 5GHz 36 ms for 3 Km/h 0.43 ms for 250 Km/h Considering 5-10 ms one-way delay of backhaul links The CQIs become outdated for high-mobility users when using centralized control for scheduling An interworking set of hierarchical controllers is required 1 Channel quality indicators 39 5G Architecture Vision CA: Control Application NF: Network Function Control Interface A New Proposition for Control-Plane Hierarchical Control in 4 Layers CA • • • • • NF CA L4: Network Controller NF CA E2E QoS provisioning Application-aware route establishment Service chaining Mobility management Policy & charging NF CA L3: RAN Controller NF • • C-RRM HetNet management NF CA CA L2: BS Controller • • CA Wireless resource management & scheduling Adaptive PHY-layer packet creation Higher Hierarchical Layer NF CA L1: UE Controller • D2X discovery & connection control 40 5G Architecture Vision A New Proposition for Control-Plane CMaaS1 CMaaS: In the proposed hierarchical architecture, the MSPs have the option to perform handoff and route management together and deploy different CAs for different flows/users CMaaS may be offered by MSPs in one of the 3 forms: 1- For MSP’s services: The flows that have more stringent delay and packet-loss requirements will be given a CM-CPF2 with a higher operational cost; other flows will have cheaper CPFs 2- A paying user will always have a higher-grade CM-CPF regardless of its flow-type 3- For OTT services, only the paying OTTs will have a higher grade CM-CPF Therefore CMaas allows MSPs to adaptively adjust their operational costs with respect to flow/user type and hence introducing new/agile revenue paths 1 2 Connectivity management as a service Connectivity management control-plane function 41 Conclusions Conclusions The primary objective of 5G architecture is to bring a high-capacity, agile, low-cost solution through a number of innovations in RAN, Core and Transport sub-networks Ultra-dense small cell deployment on licensed/unlicensed spectrum and under split C/Uplane architecture can tackle capacity concerns NFV & DSN offer flexible network deployment & operation with integrated AS & NAS For QoE & network planning purposes, the network data can intelligently be facilitated for optimized use of network resources D2D can profitably be integrated within cellular networks and has a complementary role alongside the small cells, particularly when the I2D infrastructure deployment is reduced Due to the accelerating increase of mobile access in the overall IP transport, the centralized mobility approach should be replaced by a terminal-centric distributed approach An all-SDN network architecture with hierarchical network capabilities can be used to allow for different grades of performance & complexity and hence offer 5G service differentiation 43 Contact: [email protected]