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Magus: Minimizing Cellular Service Disruption during Network Upgrades

Xing Xu*, Ioannis Broustis#, Zihui Ge#, Ramesh Govindan*,

Ajay Mahimkar#, N.K. Shankaranarayanan#, Jia Wang#

*University of Southern California, #AT&T Labs-Research

{xingx,ramesh}@usc.edu

{broustis, gezihui, mahimkar, shankar, jiawang}@research.att.com

December 3, 2015

ACM CoNEXT 2015, Heidelberg, Germany

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© 2015 AT&T Intellectual Property. All rights reserved. AT&T, the AT&T logo and all other AT&T marks contained herein are trademarks of AT&T Intellectual Property and/or AT&T affiliated companies. The information contained herein is not an offer, commitment, representation or warranty by AT&T and is subject to change.


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Talk Outline

• Problem statement

• Solution strategies

• Validation using LTE Testbed

• Proposed solution: Magus

• Evaluation using operational network data

• Conclusion


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Importance of Good Cellular Services

Smartphones & other mobile devicesplay a central role in our lives

• Communications

• Social networks

• Web-browsing

What provides the connectivity?

• Cellular network!

Cellular networks are constantly upgraded to meetneeds of users

• e.g. software, hardware, power systems

• May take cellular base station offline

• Potential for temporary service disruption


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Service Disruption due to Planned Upgrades


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Plan to take offline



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Some users re-attachSome lose service



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OverloadedLost service


• Network configuration is suboptimal


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OverloadedLost service



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Users migrate


• Network configuration is suboptimal

• Re-configure neighbors to minimize disruption

• Increase coverage


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Problem Statement

• Planned upgrades which take a base station offline can potentially degrade or disrupt service

• Neighboring base stations can be reconfigured to minimize service disruption

• What are the reconfiguration solution strategies?

• Can we take proactive action, even before a base station is taken offline?

• What should we optimize?


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Reconfiguration Solution Strategies • Reconfiguration type• Model-based: Do what-if analysis for different configurations using a cellular

system model, find optimum, then make network tuning adjustments

• Feedback-based: Make network tuning adjustments, measure performance, and then iterate.

Magus


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Reconfiguration Solution Strategies

• SON (Self-Organizing Networks): Feedback-based, Reactive reconfiguration

• Magus: Model-based, Proactive reconfiguration

Magus

SON


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Comparison of Different Solutions:Benefits of Proactive Reconfiguration

Uti

lity

-15 -10 -5 0 5 10 15

Proactive (Magus) Reactive No Tuning

Upgrade

Proactive Reconfiguration

Reactive Reconfiguration

TimeWe have chosen a Utility Function that balances total sector performance with fair treatment of disadvantaged users


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Exploring Reconfiguration using LTE Testbed

LTE testbed

• Indoor Office deployment

• 5 Cavium eNodeBs

• 10 Sierra Wireless UEs

• Aricent EPC

• 2.6 GHz Band 7 FDD(experimental license)

• Remotely controllable

Case study

• 3 eNodeBs serving 5 UEs


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Taking down this cell


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MAGUS: Solution Design

Challenges Magus Approach

What can we reconfigure to recover coverage in a sector

Tune Antenna Tilt and Transmission Power of neighboring base stations

How can we take proactive action? Predictive model for coverage and capacity, for different power & tilt

How do we get an accurate model? Use operational network data for sector coverage, configuration and user distribution

What is a suitable metric for overall service quality?

Use sum log rate metric which balances performance and fairness

How do we search the solution space efficiently?

Use a heuristic algorithm


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Operational Data for Predictive Model

Path loss data

• Operational data for path loss in cellular network

• Enables computation of channel quality (SINR) and estimate of rate

Base Station information

• Location

• Transmit power

• Antenna pattern & tilt

UE distribution

• Distribution of users across grids

Path loss change after antenna uptilt

Path loss example: Sector facing north-west


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Predictive Model – Grid-Level Analysis

Grid information

• Received Powers

Rpa , RPb , RPc …

• Serving eNodeB

argmax(RPs) = a

• SINR

SINR=RPa/(RPb + RPc + N)

eNodeB information

• Grids served

• Users served

g1+g2+… = N

• Throughput

Tmax = f(SINR)

T=Tmax / N

g1 g2 g3 …

At grid:- Rpa , RPb , RPc

- serving eNB- SINR

At eNodeB:- covered grids- # of UEs

(c)

(b)

(a)


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Metrics: Overall Service Quality & Mitigation of Service Disruption

Overall Service Quality

• Maximize rate?

• Maximize coverage?

• Balance of rate and coverage (fairness)

Metric for Utility:

• Utility 𝑈 = 𝑘 log 𝑟𝑘 , where 𝑟𝑘 is rate enjoyed by user k.

• Similar to commonly deployed proportional fair scheduler

• Balances total sector performance with fair treatment of disadvantaged users

Metric for measuring value of Magus approach

• % of Lost Utility that is Recovered


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Performance Improvement Measured withRecovery Ratio

Before

Upgrade

Magus

Recovery ratio = % of Lost Utility which is Recovered

= 𝑈𝑀𝑎𝑔𝑢𝑠 − 𝑈𝑢𝑝𝑔𝑟𝑎𝑑𝑒 𝑈𝑏𝑒𝑓𝑜𝑟𝑒 − 𝑈𝑢𝑝𝑔𝑟𝑎𝑑𝑒


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Evaluation on Operational Data• We evaluated Magus on 3 different markets in different parts of the country

• For each market, we evaluated 3 different areas: Rural, Suburban, Urban

• For each market & area, we evaluated 3 upgrade scenarios

• Offline sectors: 1 central sector, 3 co-located sectors, 4 separated sectors

• Reconfiguration tuning options: Power-only. Tilt-only, Joint power & tilt

Each pixel represents a grid (100m x 100m area)Grids served by the same sector are painted in the same color

~150~50


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Evaluation – Recovery Ratio

• Tilt only tuning < power only tuning < joint tuning

• Best recovery performance in suburban areas

• Urban: interference-limited, overlapping sectors, limited tuning potential

• Rural: large cell radius, difficult for neighbors to cover affected area

• Suburban: sweet spot, neighboring sectors can help to cover the affected area more effectively

Rural Suburban Urban

Power Tuning 18.3% 56.5% 17.1%

Tilt Tuning 8.4% 37.7% 8.8%

Joint Tuning 37.0% 76.4% 20.1%


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Gradual Tuning in Magus

• Take down target cell gradually, doing tuning in stages

• Reduce synchronized handovers


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Evaluation – Benefits of Gradual Tuning• Gradual process for taking sector offline

• Reconfigure with correct sequence of steps

• Reduce simultaneous handovers

Fewer synchronized handovers

Utility never goes below the final configuration

No GradualGradual


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Conclusion and Future Work

Magus: model-based, proactive reconfiguration for planned upgrades

• Predictive model using operational data

• Grid-level analysis:

– SINR at each grid

– Downlink throughput

• Benefits

– Quickly minimize service disruption

– Fewer synchronized handovers

Future work – there is lots !!

• Field trial and deployment of Magus

• Optimization algorithm

• Joint optimization: cross-technology/carriers

• Predictive model for other network events (e.g., radio network congestion)


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THANK YOU!

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