high quality of experience (QoE) § Critical component for Operators § High impact business for the operators Operators must ensure optimal RAN performance 24x7
Tier-1 operator in the U.S. § Studied portion of RAN for over a year § 13,000+ base stations serving over 2 million users § 1000s of trouble tickets § Significant effort by the operator to resolve them
incorrectly diagnosed § Source of disagreements § Which team should solve this problem? § Wasted efforts § Known root-causes § Recurring problems Need more fine-grained information for diagnosis
(P-GW) Mobility Management Entity (MME) Home Subscriber Server (HSS) Internet Control Plane Data Plane User Equipment (UE) § Logging everything ideal, but impossible
(P-GW) Mobility Management Entity (MME) Home Subscriber Server (HSS) Internet Control Plane Data Plane User Equipment (UE) § Logging everything ideal, but impossible Radio bearer
(P-GW) Mobility Management Entity (MME) Home Subscriber Server (HSS) Internet Control Plane Data Plane User Equipment (UE) § Logging everything ideal, but impossible Radio bearer GTP tunnel
(P-GW) Mobility Management Entity (MME) Home Subscriber Server (HSS) Internet Control Plane Data Plane User Equipment (UE) § Logging everything ideal, but impossible Radio bearer GTP tunnel EPS Bearer
plane procedure logs provide necessary fine-grained information for efficient diagnosis 3 step approach to leverage rich bearer-level traces for RAN performance diagnosis
layer Throughput Quality Traffic Volume Connection Counts Mobility Model performance metrics at bearer level using classification bin parameters as features
layer Throughput Quality Traffic Volume Connection Counts Mobility Model performance metrics at bearer level using classification bin parameters as features Event Metrics
layer Throughput Quality Traffic Volume Connection Counts Mobility Model performance metrics at bearer level using classification bin parameters as features Event Metrics Non-Event/Volume Metrics
layer Throughput Quality Traffic Volume Connection Counts Mobility Model performance metrics at bearer level using classification bin parameters as features Classification Models Event Metrics Non-Event/Volume Metrics
layer Throughput Quality Traffic Volume Connection Counts Mobility Model performance metrics at bearer level using classification bin parameters as features Classification Models Regression Models Event Metrics Non-Event/Volume Metrics
users really care about diversity, a 29% overhead for each PRB exists on average because of resources allocated to physical downlink control channel, physical broadcast channel and reference signals. The physical layer has a BLER target of 10%. Account for MAC Sub-layer Retransmissions The MAC sub- layer performs retransmissions. We denote the MAC eciency as MAC . It is computed as the ratio of total rst transmissions over total transmissions. We compute MAC using our traces. The predicted throughput due to transmit diversity is calculated as: tputRLCdi = (1.0 MAC ) ⇥ 0.9 ⇥ (1 0.29) ⇥ 180 ⇥ PRBdi ⇥ lo 2(1 + SINRdi )/ TxTimedi PRBdi denotes the total PRBs allocated for transmit diversity. TxTimedi is the total transmission time for transmit diversity.
users really care about diversity, a 29% overhead for each PRB exists on average because of resources allocated to physical downlink control channel, physical broadcast channel and reference signals. The physical layer has a BLER target of 10%. Account for MAC Sub-layer Retransmissions The MAC sub- layer performs retransmissions. We denote the MAC eciency as MAC . It is computed as the ratio of total rst transmissions over total transmissions. We compute MAC using our traces. The predicted throughput due to transmit diversity is calculated as: tputRLCdi = (1.0 MAC ) ⇥ 0.9 ⇥ (1 0.29) ⇥ 180 ⇥ PRBdi ⇥ lo 2(1 + SINRdi )/ TxTimedi PRBdi denotes the total PRBs allocated for transmit diversity. TxTimedi is the total transmission time for transmit diversity. MAC efficiency
users really care about diversity, a 29% overhead for each PRB exists on average because of resources allocated to physical downlink control channel, physical broadcast channel and reference signals. The physical layer has a BLER target of 10%. Account for MAC Sub-layer Retransmissions The MAC sub- layer performs retransmissions. We denote the MAC eciency as MAC . It is computed as the ratio of total rst transmissions over total transmissions. We compute MAC using our traces. The predicted throughput due to transmit diversity is calculated as: tputRLCdi = (1.0 MAC ) ⇥ 0.9 ⇥ (1 0.29) ⇥ 180 ⇥ PRBdi ⇥ lo 2(1 + SINRdi )/ TxTimedi PRBdi denotes the total PRBs allocated for transmit diversity. TxTimedi is the total transmission time for transmit diversity. MAC efficiency # Physical Resource Blocks
users really care about diversity, a 29% overhead for each PRB exists on average because of resources allocated to physical downlink control channel, physical broadcast channel and reference signals. The physical layer has a BLER target of 10%. Account for MAC Sub-layer Retransmissions The MAC sub- layer performs retransmissions. We denote the MAC eciency as MAC . It is computed as the ratio of total rst transmissions over total transmissions. We compute MAC using our traces. The predicted throughput due to transmit diversity is calculated as: tputRLCdi = (1.0 MAC ) ⇥ 0.9 ⇥ (1 0.29) ⇥ 180 ⇥ PRBdi ⇥ lo 2(1 + SINRdi )/ TxTimedi PRBdi denotes the total PRBs allocated for transmit diversity. TxTimedi is the total transmission time for transmit diversity. MAC efficiency # Physical Resource Blocks Transmission time
users really care about diversity, a 29% overhead for each PRB exists on average because of resources allocated to physical downlink control channel, physical broadcast channel and reference signals. The physical layer has a BLER target of 10%. Account for MAC Sub-layer Retransmissions The MAC sub- layer performs retransmissions. We denote the MAC eciency as MAC . It is computed as the ratio of total rst transmissions over total transmissions. We compute MAC using our traces. The predicted throughput due to transmit diversity is calculated as: tputRLCdi = (1.0 MAC ) ⇥ 0.9 ⇥ (1 0.29) ⇥ 180 ⇥ PRBdi ⇥ lo 2(1 + SINRdi )/ TxTimedi PRBdi denotes the total PRBs allocated for transmit diversity. TxTimedi is the total transmission time for transmit diversity. MAC efficiency # Physical Resource Blocks Transmission time Link-adapted SINR
0.12 0.14 0.16 0.18 0 5 10 15 20 25 Probability SINR Loss (dB) § Problematic for some cells § To understand why, computed loss of efficiency Actual throughput SINR vs computed using parameters
0.12 0.14 0.16 0.18 0 5 10 15 20 25 Probability SINR Loss (dB) § Problematic for some cells § To understand why, computed loss of efficiency Actual throughput SINR vs computed using parameters Finding Insight: Link adaptation slow to adapt!
§ Models can be built on-demand automatically § Full automation for next generation networks: § Need to build 1000s of models § Need to keep the models updated § Need real-time diagnosis
million users, over a period of 1 year § Leveraging bearer-level traces could be the key to automating RAN diagnosis § Proposed bearer-level modeling § Unearthed several insights § Fully automated diagnosis needs more effort
anandpiyer
daisukelab_cs
satoyutosa
toshiseisaku
kuri8ive
maguro27
usaito
kitachan_black
yag_ays
masadooon
yoshipon
deibel
reseachconf
moore
holman
keathley
skipperchong
morganepeng
keavy
roundedbygravity
denniskardys
maggiecrowley
tanoku
eitanlees
chrislema
