US 20190319868A1 ( 19) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2019 /0319868 A1 Svennebring et al. ( 43 ) Pub . Date : Oct. 17 , 2019 ( 54 ) LINK PERFORMANCE PREDICTION (52 ) U . S . CI. TECHNOLOGIES CPC .. .. H04L 43/ 0882 (2013 . 01 ); H04W 24 /08 ( 2013 . 01 ) (71 ) Applicant : Intel Corporation , Santa Clara , CA (57 ) ABSTRACT (US ) Various systems and methods for determining and commu nicating Link Performance Predictions (LPPs ), such as in ( 72 ) Inventors : Jonas Svennebring , Sollentuna (SE ) ; connection with management of radio communication links, Antony Vance Jeyaraj, Bengaluru ( IN ) are discussed herein . The LPPs are predictions of future network behaviors /metrics ( e . g . , bandwidth , latency , capac (21 ) Appl . No. : 16 /452 , 352 ity , coverage holes , etc . ) . The LPPs are communicated to applications and /or network infrastructure, which allows the applications/ infrastructure to make operational decisions for ( 22 ) Filed : Jun . 25 , 2019 improved signaling / link resource utilization . In embodi ments , the link performance analysis is divided into multiple layers that determine their own link performance metrics, Publication Classification which are then fused together to make an LPP. Each layer (51 ) Int . Cl. runs different algorithms, and provides respective results to H04L 12 / 26 ( 2006 .01 ) an LPP layer /engine that fuses the results together to obtain H04W 24 / 08 (2006 .01 ) the LPP . Other embodiments are described and / or claimed . 700 Spatio - Temporal History Data Tx1 : C1 TIDE _ 1, DE _ 2 . .. Txt : C2 T2 DE _ 1 , DE _ 2 , . .. win Txs : C3 122 T : DE _ 1, DE _ 2 , .. TN DE _ 1 , DE _ 2 .. TxN : CN CELL LOAD MODEL 710 Real- Time Data 744 704 Patent Application Publication Oct. 17 , 2019 Sheet 1 of 19U S 2019 /0319868 A1 150 100 D WHA! .IN - 155 CN 142 155 1407 CLOUD BACKEND LAYER 1135 DATA . 136a 1366 DATA 1366 DATA 1366 130 ACCESS /EDGE NODES LAYER 132 103 103 739 1200 GATEWAYS / 121a INTERMEDIATE NODES LAYER 1210 96- - - 105 -wy ENDPOINTS / SENSORS/ THINGS 110 111- 10) LAYER Figure 1 112 Patent Application Publication Oct. 17 , 2019 Sheet 2 of 19U S 2019 /0319868 A1 PredictionLayer225-N 206 www 206 PredictionLayer225-3 Figure2 Environment100 LPPlayer202 206 PredictionLayer225-2 206 PredictionLayer225-1 LPPservice200 Patent Application Publication Oct. 17 , 2019 Sheet 3 of 19U S 2019 /0319868 A1 3401 TEE330 306 POWER RADIOFRONTEND MODULE315 CIRCUITRY335 !-.-.-.-.-.-.-.3Figure NETWORK CONTROLLER PMIC325 306 RADIOFRONTEND MODULE315 BASEBAND CIRCUITRY310 APPLICATION CIRCUITRY305 MEMORY CIRCUITRY320 306 POSITIONING CIRCUITRY345 USERINTERFACE CIRCUITRY350 . 300 Patent Application Publication Oct . 17 , 2019 Sheet 4 of 19 U S 2019 /0319868 A1 400 Mm4 wwww MEApprules Mgmt431b4310 &Reqts.Lifecycle ww OperationsSupportSystem422 MECorchestrator421 Mm3 MECPLATFORMMANAGER431 Mm6 VIM432 MEManagement430 wwww w wwwwwwwwwwwww w ME Mgmt.431a Platform Element . Mm2 Amy . -Mmgumum . 437-10 mMp2 MECHost136-1 variousAPIS453-1 MECLPPAPI451-1 Handling MECPlatform437-1 Mm8 LPP-IS4521 437-1d . ServiceRegistry 437-16 VirtualizationInfrastructure438-1 Figure4 . FilteringRules WWWWWWWWWWW DataPlane439-1 Control W . WA Awit frommMx UserAppLCMproxy425 . Mp1 437-1a VA MECApp436-1 WA . Mp3, . +Mp2 437-2¢ * . DNS MECHost136-2 variousAPIs453-2 MECLPPAPI451-2 Handling * . LPP-S4522 437-20 * . ServiceRegistry * VirtualizationInfrastructure438-2 , 437-26 MECPlatform437-2436-2 * UEApp(s) Filtering Rules Control DataPlane439-2 WWW 405 * CFSPortal 406 , UE420 437-2a MECApp 402 401 Patent Application Publication Oct. 17 , 2019 Sheet 5 of 19U S 2019 /0319868 A1 500 5067 MESH DEVICES / PROCESSOR 502 509 - 511517 FOG 564 INSTRUCTIONS + 510 570 512 CLOUD 501 TRUSTED EXECUTION ENVIRONMENT 590 NETWORK 560 INTERFACE 516 wanitarian abain i wai ght line nainen nainen sairai no irisinin adinin meno diretamente parashikon something MEMORY 504 SENSORS 521 www INSTRUCTIONS EXTERNAL 582 w INTERFACE 518 ACTUATORS 522 STORAGE 508 . LOGIC /MODULES 583 POS 545 www . ww ws com www www more wow . we were more BATTERY 524 INPUT DEVICE 586 PMIC 526 POWER BLOCK OUTPUT DEVICE 528 584 Figure 5 Patent Application Publication Oct. 17 , 2019 Sheet 6 of 19U S 2019 /0319868 A1 winninn weini winnine wwii 605- DataCollectionLayer600 653 - Processed Data652 ????????????????????????????????????????????????????????????????????????????????? EncryptionEngine 624VVV Figure6 ComputeNode602 Pre-Processing EngineandData Reduction623 LPPSApp620 Data651 Patent Application Publication Oct. 17 , 2019 Sheet 7 of 19U S 2019 /0319868 A1 Tx:Ci Txt:C2 Txt:C3 Txn:CN - -700 CELLLOADMODEL710 Figure7 . 744 T2DE_1,2. TNDE_1,2. Tu|DE_1,2. T3DE_1,2. Real-TimeData See Spatio-TemporalHistoryData . 704 1 722 702 Patent Application Publication Oct. 17 , 2019 Sheet 8 of 19U S 2019 /0319868 A1 838B !&i 4 .1. 11 htty * •* - - - -' - - ., -' - ' - - 8328 ' . ' ' - ' '* - - * -: * * * * * *- -', 838A Figure8 ]tttt1 ** 832A 821 Patent Application Publication Oct. 17 , 2019 Sheet 9 of 19U S 2019 /0319868 A1 matenmann 900 Cell Map 901 Cell Transition Prediction Model 902 Cell Transition Predictions 903 T1 : CIDX T2 : CIDY TN : CIDN . Figure 9 sauPatent Applicationorice Publicationalta Octca. 171390 , 2019 Sheetsur 10mes of 19 USesamowoman 2019 /0319868 A1 1000 Previous 1004 1001 -1021 1003 1023 Current 1002 1022 1003 1024 1027 1025 1005 - 1026 1006 1007 1028 1008 1009 Figure 10 Patent Application Publication Oct. 17 , 2019 Sheet 11 of 19 US 2019 /0319868 A1 1116 1104 TOAREA CN bw bw bw .|bw Spatio-Temportal HistorySequence Models . Spatio-Temporal History PredictedBW/timeslot bw CsCz bwbw bw TM|bw SequenceModel To T2 1102 Figure11 1100 CellSequence andanawe Prediction CellBWPrediction m a 11061106 meoma PredictedSequence Ilalal.ON www Short-Term History 1 UES111,121 Patent Application Publication Oct. 17 , 2019 Sheet 12 of 19 US 2019 /0319868 A1 -1202 DataFusionModel 1203[Neural Network) 1200 BWPredictionModel 1201@T3 Figure12 w BWPredictionModel 1201@T2 MYYYYYYY BWPredictionModel 1201@F1 Patent Application Publication Oct. 17 , 2019 Sheet 13 of 19 US 2019 /0319868 A1 1305 1310 LPPSProvider 1 Figure13 request(registration) registered(hash_key,domainname) 1300 LPPSprovisioned ACK(LPPversion,timesettings) LPPSConsumer 1308 LPPSprovisioning 1303) Patent Application Publication Oct. 17 , 2019 Sheet 14 of 19 US 2019 /0319868 A1 LPPSProvider M1405 Figure14 1400 -request(hash_key) -reply(LPP_data) LPPSConsumer 14037 - - - - - - Patent Application Publication Oct. 17 , 2019 Sheet 15 of 19 US 2019 /0319868 A1 1505A LPPSProvider M1505B M1505C Figure15 -subscribe(hash_key) -unsubscribe(hash_key)— -notify(LPP_data) : -LinkStatus() 1500 -notify(LPP_data) -notify(LPP_data) - : LPPSConsumer 4 15034 1508 1510 Patent Application Publication Oct. 17 , 2019 Sheet 16 of 19 US 2019 /0319868 A1 ????????? Endpoint MIMI minning jenni Procedure1400ofFigure14or Procedure1500ofFigure15 Figure16 tell(hash_key,domainname) 1600 LPPSProvider LPPSConsumer 1603 Patent Application Publication Oct. 17 , 2019 Sheet 17 of 19 US 2019 /0319868 A1 Endpoint 11708 171713 request(get_settings) response(settings) reply(set_settings) 705 Figure17 1700 LPPSProvider 1710T request(get_time) response(lpps_time) LPPSConsumer - 17037 Patent Application Publication Oct. 17 , 2019 Sheet 18 of 19 US 2019 /0319868 A1 Probn P-values LPPN Typen Times Figure18 1800m Probo P-valueo LPPO Typeo Timeo Patent Application Publication Oct. 17 , 2019 Sheet 19 of 19 US 2019 /0319868 A1 1900 OBTAIN PREDICTED PERFORMANCE METRICS FROM INDIVIDUAL PREDICTION LAYERS OF A PLURALITY OF A PLURALITY OF PREDICTION LAYERS 14905 FOR EACH LINK IN A RADIO COMMUNICATIONS NETWORK DETERMINE A LINK PERFORMANCE PREDICTION FOR THE LINK BASED ON THE OBTAINED PREDICTED PERFORMANCE METRICS 4915 GENERATE A LINK PERFORMANCE PREDICITION NOTIFICATION INDICATING THE DETERMINED LINK PERFORMANCE PREDICTION 6920 SEND THE LINK PERFORMANCE PREDICITION NOTIFICATION TO LPPS CONSUMER AND /OR ONE OR MORE ENDPOINTS * * 4925 FOR EACH LINK IN A RADIO COMMUNICATIONS NETWORK VO 61930 C ENDEND Figure 19 US 2019 /0319868 A1 Oct . 17 , 2019 LINK PERFORMANCE PREDICTION infrastructure equipment in accordance with various TECHNOLOGIES embodiments . FIG . 4 depicts an example Multi -access Edge Computing (MEC ) system architecture in which any one or TECHNICAL FIELD more of the techniques ( e . g ., operations , processes , methods , [0001 ] Embodiments described herein generally relate to and methodologies ) discussed herein may be performed data processing , network communication , and communica according to various embodiments . FIG . 5 depicts example tion system implementations, and in particular, to techniques components of a computer platform in accordance with for determining and utilizing link performance predictions various embodiments . (LPPs ) and /or link quality predictions (LQPs ) . [0007 ] FIG . 6 depicts example data collection layer in accordance with various embodiments . FIG . 7 illustrates an BACKGROUND example cell load prediction layer according to various embodiments . FIG . 8 illustrates an example scenario involv [0002 ] Emerging wireless network technologies ( e . g . , ing intra -cell characteristic predictions according to various Fifth Generation (5G ) or New Radio (NR ) ) are expected to embodiments . FIG . 9 illustrates an example cell transition provide increased improvements in data rates, device den prediction layer according to various embodiments . FIG . 10 sity , latency , and power consumption . Some new services illustrates