April 14, 2014 Virtualization of LTE Desislava Dimitrova University of Bern D. Dimitrova, LTE Virtualization 2 April 14, 2014 Outline Target Basic RAN virtualization MCN concepts approach RANaaS Profiling D. Dimitrova, LTE Virtualization 3 April 14, 2014 Target Can we make use of the concept of cloud computing to optimize the performance and management of the LTE cellular network? D. Dimitrova, LTE Virtualization April 14, 2014 Basic concepts D. Dimitrova, LTE Virtualization 5 April 14, 2014 Basic concepts The mobile network - LTE core Radio Access Network IP MME eNodeB SGW PGW D. Dimitrova, LTE Virtualization 6 April 14, 2014 Basic concepts Traditional eNodeB – implementation on specialized hardware optimized for signal processing D. Dimitrova, LTE Virtualization 7 April 14, 2014 Basic concepts RAN virtualisation: network function virtualisation concept moving eNodeB functionality to software, creating a virtual component Advantages: RAN sharing between operators Technology-independent service provisioning Optimisation of physical resource use Optimised handovers (radio) Load balancing (computational) D. Dimitrova, LTE Virtualization April 14, 2014 MCN approach D. Dimitrova, LTE Virtualization 10 April 14, 2014 MCN approach eNodeB = Base Band Unit + Remote Radio Head BBUs Data Centre Optical fronthaul BBUs can be grouped in data centres BBUs can be commonly managed Can BBU run on GPP? D. Dimitrova, LTE Virtualization 11 April 14, 2014 MCN approach RANaaS – enable the offering of RAN to mobile operators as a service running in a datacenter(s) RANaaS lifecycle Deployment & Runtime management input: needed computational resources Computational resource depends on the radio use We need mapping! Design Disposal (destroy) Runtime management (scale up/down) Deployment (load components for a client) Provisioning (customise) D. Dimitrova, LTE Virtualization April 14, 2014 Profiling D. Dimitrova, LTE Virtualization 13 April 14, 2014 Profiling A: Sensitive on radio resource usage For ensuring LTE deadlines & computational needs B: Profiling of higher layers Sensitive on number of UE For computational needs C: Profiling of PHY layer Profiling of hardware impact Shared infrastructure Load variation D. Dimitrova, LTE Virtualization 14 April 14, 2014 Profiling Approach: OAI emulation tool for LTE radio PHY layer profiling with dlsim & ulsim tools Higher layers profiling with oaisim tool Changing set of hardware configurations D. Dimitrova, LTE Virtualization 15 April 14, 2014 Profiling dlsim tool ./dlsim -a –P -D -B100 -n1000 -m24 -s20 eNB TX function statistics (per 1ms subframe) OFDM_mod time :146.611662 us (100 trials) DLSCH modulation time :835.020654 us (100 trials) DLSCH scrambling time :463.029904 us (100 trials) DLSCH encoding time :528.482113 us (100 trials) |__ DLSCH turbo encoding time :215.060981 us (900 trials) |__ DLSCH rate-matching time :147.702422 us (900 trials) |__ DLSCH sub-block interleaving time :104.207302 us (900 trials) FFT Modulation Scrambling Turbo encoding eNB tx D. Dimitrova, LTE Virtualization 16 April 14, 2014 Profiling A PHY processing - individual components 5MHz Full use, eNB tx 500 Processing time, us 450 400 350 300 250 200 150 100 50 0 0 9 10 16 17 24 27 MCS Index OFDM mod eNB mod eNB scr eNB coding Total D. Dimitrova, LTE Virtualization 17 April 14, 2014 Profiling A LTE frame processing deadline – 3ms Between HARQ processes Factors: eNB and UE processing + transmission delay Modulation and coding scheme PRB (frequency) allocation D. Dimitrova, LTE Virtualization 18 April 14, 2014 Profiling A eNodeB PHY layer transmission Processing time, us eNB tx 1400 1200 5MHz min use 1000 5MHz full use 800 10MHz min use 600 10MHz full use 400 20MHz min use 200 20MHz full use 0 0 9 10 16 17 MCS Index 24 27 D. Dimitrova, LTE Virtualization 19 April 14, 2014 Profiling A eNodeB PHY layer reception eNB rx Processing time, us 6000 5000 5MHz min use 4000 5MHz full use 3000 10MHz min use 2000 10MHz full use 20MHz min use 1000 0 20MHz full use 0 9 10 16 17 MCN Index 24 27 D. Dimitrova, LTE Virtualization 20 April 14, 2014 Profiling B Higher layers: MAC, RLC, PDCP eNB processing Processing time, us 600 500 400 300 PDCP 200 MAC 100 0 1 2 3 4 5 6 7 8 9 Number of UEs 10 15 20 25 30 D. Dimitrova, LTE Virtualization 21 April 14, 2014 Profiling C use impact – load factor Procesing time tx, us tx rx 450 900 400 800 350 700 300 600 250 500 200 400 150 300 100 200 50 100 0 MCS 9 MCS 16 MCS index MCS 24 Processing time rx, us Hardware 0 D. Dimitrova, LTE Virtualization 22 April 14, 2014 Profiling C Hardware use impact – core scheduling Processing time, us 5MHz DL transmission 1000 900 800 700 600 500 400 300 200 100 0 5MHz 2GHz 5MHz 4GHz 5MHz 6GHz 5MHz 8GHz 5MHz 10GHz 0 9 10 16 MCS index 17 24 27 D. Dimitrova, LTE Virtualization 23 April 14, 2014 Profiling C of the hardware = nmon stats 07:34 07:34 07:34 07:34 07:34 07:34 07:34 07:34 Wait% 23:34 23:34 23:34 23:34 23:34 23:34 23:34 23:34 23:34 23:34 0 23:34 0 23:34 20 23:34 20 23:34 40 23:34 40 23:34 60 23:34 Sys% Wait% 80 60 23:34 User% 23:34 Wait% 23:34 80 Sys% 23:34 User% 100 23:34 100 Sys% CPU 2 openair 08/04/2014 23:34 CPU 1 openair 08/04/2014 07:34 07:34 07:34 07:34 0 07:34 0 07:34 20 07:34 20 07:34 40 07:34 40 07:34 60 07:34 60 07:34 80 07:34 80 User% 07:34 Wait% 07:34 Sys% 23:34 User% 100 23:34 100 CPU 2 openair 09/04/2014 07:34 CPU 1 openair 09/04/2014 23:34 Monitoring D. Dimitrova, LTE Virtualization 24 April 14, 2014 Next steps Extended profiling: Exact calculation of the LTE processing deadlines Higher layers profiling needs tuning Profiling for real traffic Monitoring of the hardware top & dstats tools – only life nmon tool Zabbix & VTune tools D. Dimitrova, LTE Virtualization 25 April 14, 2014 Next steps SO – RANaaS unit that manages the BBUs and physical infrastructure Creating of VMs & parameterization Scaling decisions Service Orchestrator Radio utilization Resource mapping Computational needs Decision logic Scaling decisions to the infrastructure VMs configuration D. Dimitrova, LTE Virtualization April 14, 2014 Thank you 26 D. Dimitrova, LTE Virtualization 27 April 14, 2014 Profiling A eNodeB PHY transmission Processing time, us 5MHz eNB tx 500 450 400 350 300 250 200 150 100 50 0 MCS 9 MCS 16 MCS 24 1 2 8 12 16 PRB allocation 20 25 D. Dimitrova, LTE Virtualization 28 April 14, 2014 Profiling A UE PHY layer transmission UE tx Processing time, us 1400 1200 1000 5MHz min use 800 5MHz full use 600 10 MHz min use 400 10 MHz full use 20 MHz min use 200 0 20 MHz full use 0 9 10 16 17 24 27 MCN Index D. Dimitrova, LTE Virtualization 29 April 14, 2014 Profiling A UE PHY layer reception UE rx Processing time, us 4000 3500 3000 5MHz min use 2500 5MHz full use 2000 10MHz min use 1500 10MHz full use 1000 20MHz min use 500 0 20MHz full use 0 9 10 16 17 MCN Index 24 27 D. Dimitrova, LTE Virtualization 30 April 14, 2014 Basic concepts Texas Instruments approach for energy efficiency D. Dimitrova, LTE Virtualization 31 April 14, 2014 RAN Virtualisation Implementing functionality of the eNodeB radio protocol stack in software running on GPP run centrally (in cloud) Network mng L3+ L2 MAC run at eNB Network mng RAN virtualisation L3+ L2 MAC L1 PHY L1 PHY RF RF D. Dimitrova, LTE Virtualization 32 April 14, 2014 RANaaS D. Dimitrova, LTE Virtualization 33 April 14, 2014 Profiling ns3 LENA LTE Specialized Model (open source) Includes – PHY layer functionality (outdoor channel models, CQI, BLER modulation curves, ACM) – MAC layer functionality (buffer status, DL scheduling, channel mapping) – Radio Resource Control (RRC) – X2 implementation supporting handover OPNET LTE Specialized Model (license based) Includes – PHY layer functionality (interference and path loss models, CQI, BLER modulation curves) – MAC layer functionality (buffer status, scheduling, channel mapping) – NAS (admission control, session and location management) D. Dimitrova, LTE Virtualization 34 April 14, 2014 Profiling OpenAirInterface D. Dimitrova, LTE Virtualization
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