Artificial Intelligence in Mobile Communication Networksmaucher.pages.mi.hdm-stuttgart.de/ai/res/mobileNetworks.pdf · •provides access to 14k technical documents (Ericsson)

AI in Mobile Networks1 © Joachim Charzinski 2018–2019

Artificial Intelligencein Mobile Communication NetworksJoachim Charzinski


My Personal AI Hype Cycle

basic idea of hype cycles: Gartner Group

time

visi

bilit

y

technologytrigger

peak ofinflated

expectations

trough of disillusionment

slope ofenlightenment

plateau of productivity

1990 Workshop

on ANN

1989theory of ANN

1991: teletraffic theory is cooler

2000: and self-organizing network

protocols are even cooler!

2010s: Oops, meanwhile

AI is being used everywhere

2004: optimize IP routing

ANN artificial neural networks

1993: expert systems... (boring)

2006 automatic

SPIT detection2010s: and lots of what I did

before is now called AI



Outline

1. Size and complexity

2. Evolution of AI solutions in networks

3. Examples from network management

4. Examples from real-time processing

AI in Mobile Networks

5 © Joachim Charzinski 2018–2019

How come?

• Mobile networks are BIG and

• ~840 operators worldwide have the same problems

• network operators’ business approaches:

– move NOCs to low-wage countries (India, Romania, ...)

– use Artificial Intelligence to support network operations

– use vendors’ managed services offerings

§ and let them use AI in turn ...

NOC network operations center



Preliminaries: Mobile Network Standards

• GSM (“2G”)

– since ca. 1990, 13kbit/s CS voice

– 8 time slots in each 200kHz channel

• GPRS (“2.5G”)

– since ca. 2000, some 10s of kbit/s PS data

– can use multiple time slots per frame

• UMTS (“3G”)

– used since ca. 2003

– up to 384kbit/s CS or PS services

– 5 MHz bandwidth, CDMA

• HSPA (“3.5G”)

– started ca. 2006 with HSDPA

– adaptive modulation and coding

– improving UMTS to 7, 14, 28, 42 kbit/s

LTE long-term evolution

LTE-A LTE advanced

MIMO multiple input multiple output

OFDM orthogonal frequency division multiplexing

PS packet switching

UMTS universal mobile telecommunications system

• LTE (“3.9G”, “4G”)

– used since ca. 2011, PS only

– OFDM and MIMO

§ 1200 subcarriers in 20 MHz

§ “up to 100 Mbit/s”

• LTE-A (“4G+”)

– used since ca. 2015

– adds carrier aggregation to LTE

– up to 100 MHz total bandwidth

• 5G

– higher-order MIMO

– adaptive beam forming

– more bandwidth, more base stations

– cognitive radio

CDMA code division multiple access

CS circuit switching

GPRS general packet radio service

GSM global system for mobile communications

HSPA high-speed packet access


BIG(very rough numbers for a German operator)• ~45M subscribers (active SIM cards)

– fault management, hotlines– churn– fraud

• ~200k radio cells (LTE + UMTS + GSM)• ~30k base station locations• ~840 other mobile networks + 1000s of fixed networks• Internet peering and transit to ~60k other ASs

AS autonomous system

AI in Mobile Networks8 © Joachim Charzinski 2018–2019 based on data from

https://www.destatis.de/DE/ZahlenFakten/LaenderRegionen/Regionales/Gemeindeverzeichnis/Gemeindeverzeichnis.html



Network operation is a BIG issue

estimated network management events per German operator• 5M alarms per month

– more than 150000 per day

• 110k trouble tickets per month– more than 3500 per day

• 9k change requests per month– 3000 per day

• total world market size (gsmaintelligence.com, 11/2019): – ~840 network operators– >1 TUSD annual revenue– > 9.3 G active SIM cards

data scaled from 100M to 45M users (neglecting economy of scale) from: http://archive.ericsson.net/service/internet/picov/get?DocNo=23/28701-FGB101127&Lang=EN&HighestFree=Y


Network Technology is

• real-time decisions for many parameters– under delayed information → causality issue → prediction required

LTE / LTE-A• 1200 / ~5700 carrier frequencies (subcarriers) in 6 frequency bands• 4x4 MIMO

– 16 correlated transmission paths between smartphone and base station

5G• more subcarriers, more frequency bands• higher-order MIMO• smart antennas and beam-forming• in-band relaying• cooperative radio LTE Long-Term Evolution (“4G”)

LTE-A LTE Advanced (“4G+”)MIMO Multiple Input Multiple Output


2. EVOLUTION OF AI SOLUTIONS IN NETWORKS

AI in Mobile Communication Networks


Logic and Data

Logictrainedexplicitlyprovided

Dat

aex

plic

itly

prov

ided

train

ed

ExpertSystems

(~early 1990s)

incidentdetection

(~late 1990s)

supervisedlearning

(~early 2000s)

unsupervisedlearning(~2010s)

if

then

dates indicate when technologies started being broadly used in networks

if (R>287) then


Expert Systems ruled in the 1990s

Jürgen M. Schröder, Wissensbasierte Diagnose lokaler Netze im Wirkbetrieb, Dissertation, Universität Stuttgart, 1993 https://en.wikipedia.org/wiki/MicroVAX#/media/File:Microvax_3600_(2).jpg


Expert Systems

• intelligence provided via explicit logical rules• experts provide

– structure of knowledge– logical rules– contents of knowledge (data)

§ expanded during operation by recording experts’ decisions and diagnoses

• applications– fault management (part of network management)– automatic network control

§ capacity allocation and antenna tilt based on time of day / vacations / events§ long-term planning of capacity upgrades

• augmented by automatic observation– “plug and play” configuration– neighbor detection and configuration– network load prediction– handover prediction

eNodeB LTE base stationPCI physical cell identity


Self-learning Systems

• data automatically collected and evaluated• experts often still provide

– structure of knowledge– logical rules

• applications in network management (slow time scale)– self-organizing networks, “plug and play”– automatic neighbor configuration for base stations– automatic selection of PCI values

§ 1 out of 504 values, locally unambiguous

– automatic detection of PCI collisions– use cell phone data for coverage maps -> save drive tests– prediction of mobility

§ network load fluctuations (scale: hourly)

• applications in scheduling– prediction of handovers (scale: 10s of ms)

PCI physical cell identity


3. EXAMPLES FROM NETWORK MANAGEMENT



a typical second in fault management

• high call blocking ratio in Stuttgart area• 120 voice ports not working at MSC Stuttgart • SDH Multiplexer stg-5 lost signal on port 17• SDH Multiplexer muc-3 lost signal on port 8• ... (and 100000 other alarms) ...• WDM ADM ulm-3 lost signal on wavelengths 1, 17, 29• WDM ADM muc-8 lost signal on wavelengths 2, 18, 30

• ...• excavator destroyed optical fiber in Günzburg

ADM Add-Drop MultiplexerMSC Mobile Switching CenterSDH Synchronous Digital HierarchyWDM Wavelength Division Multiplexing

I‘m on a strictfiber diet.



Root cause identificationAlarm consolidation• expert systems used to consolidate alarms and identify root cause

• knowledge used:

– network graph → dependency graph (fault here implies fault there)

– configuration data

– expanded by observing experts’ actions during operation

• in production use since 1990s

• evolution [Vinberg et al, 2001]

– supervised learning from human labeled data

– artificial neural network learns to recognize previously encountered alarm situations

• explicit dependencies still very valuable for logic



Fraud

• SIM cloning attempts

• VAS fraud (see below)

• SMS spam through SS7 networks

• interconnection billing and charging

MNO mobile network operator

SIM subscriber identity module

SMS short message service

SS7 signaling system #7

VAS value-added service

0. set up VAS

1. getpostp

aid

SIM

fro

mM

NO

MNO

2. use

VA

S

3. M

NO

pays

for

VA

S u

sage

4. fraudster runs

away with money

5. MNO sends bill

and tries to collect money


Fraud management

• traditional expert systems apply rules and hard limits– e.g., hard limit on number of calls or SMS per minute

• evolution: automatic user baselining/profiling systems for outlier detection– learn individual users’ typical behavior patterns– significant deviation from baseline triggers alarm– mismatch of location and activity data triggers alarm

§ e.g., location = UK and sending SMS from Syria

• see also: AI in network security (IDS, IPS)

IDS intrusion detection systemIPS intrusion prevention systemSMS short message serviceUK United Kingdom

for references see appendix


Initial configuration

• set up base station parameters• Physical Cell ID

– short (1 out of 504) value for fast lookups in UEs– only local significance

§ must be different from all surrounding values – self-configuration

• neighbor detection– find out / verify location– set up X2 connections between LTE base stations

• security bootstrapping– configure credentials and security associations

ID identifierUE user equipment

remember: ca. 30k base station locations


Churn Management

• 20 – 30% of subscribers change operator every year– roughly 10M subscribers for a large German network operator

• observe subscribers• predict churn probability• AI application using supervised learning

– millions of user records– long-term observation required

§ automatic labeling by churn/no churn observation

– automatic identification and extraction of relevant classifiers from available data

– helps call center to optimize offers based on estimated churn probability

for references see appendix

MNO 1

MNO 2

MNO 3


Service chatbot

• service technicians need backend support during troubleshooting• chatbot automates part of backend • provides access to 14k technical documents (Ericsson)• business models:

– sell to operators– use for own managed service offerings

• challenge: multi-vendor environments

I. Morris, Humans Beware: Ericsson Readies Machines to Run the Network, Lightreading, Feb. 2018


Predictive maintenance

• failures often preceded by performance degradation– famous example: EDFA

§ increased bit error rate from ageing laser

• application: recognize upcoming failures early– order spare parts– schedule servicing to low-impairment time slot

• AI technology using supervised learning– observe performance parameters– keep track of age– learn patterns and predict failure probability– failures provide automatic data labeling

BER bit error rateEDFA Erbium-doped fiber amplifier

R. Le Maistre, Huawei Commits Up to $20B for Annual R&D, Fleshes Out AI Pitch, Lightreading, Feb. 2018

time

BE

R


Load prediction

• mobility leads to hourly and daily load shifts• AI technology using supervised learning

– observe demand (automatically labeled data)– learn patterns and predict demand– automatically allocate and de-allocate resources correspondingly

• now also used for sleep modes to save energy– reduce effective base station density during low traffic

day night

illustration of principle using map fromhttps://emf3.bundesnetzagentur.de/karte/default.aspx see also: Capka, Boutaba 2004


Fixed Network Optimization

• optimization of IP interface metrics or MPLS paths to reduce traffic load• increased resilience• typical methods

– simulated annealing– genetic algorithms

slide from J. Charzinski, Admission Control versus Dimensioningfor Modern Network Services, MMBnet workshop, Hamburg, Sep. 2007

MPLS Multi-protocol label switching


4. EXAMPLES FROM REAL-TIME PROCESSING



Mobility between cells during a call• fast handover required to prevent sudden loss of signal• network does not know exact position of phone

– no access to GPS and GPS not available / not reliable indoor, in tunnels, etc– delay between UE measurements and base station decision

• AI technology– observe signal strength– learn patterns and predict handover events→ faster, more reliable handover decisions

GPS global positioning system

map created with gpsvisualizer.com

R. Käallman (1995), Method for Making Handover Decisionsin a Radio Communications Network, U.S. Patent No. 5,434,950

delay → causality issue

also important for vertical and

inter-technology handovers


LTE Radio Basics

• adaptive coding and modulation

• OFDM downlink transmission, single carrier FDMA in uplink

FDMA frequency division multiple accessOFDM orthogonal frequency division multiplexingQAM quadrature amplitude modulation

up to 20 MHz (1200 subcarriers)

15 kHz

……

QPSKr=1/2

16-QAMr=1/2

16-QAMr=3/4

64-QAMr=3/4

64-QAMr=1

0011

0010

0001

0000

0111

0110

0101

0100

1011

1010

1001

1000

1111

1110

1101

1100

16-QAM: 4 bit per symbol

64-QAM: 6 bit / symbol 256-QAM: 8 bit / symbol


LTE eNodeB Scheduler

• goal: optimize spectral efficiency• input

– channel quality estimates per user and subcarrier group – amount of capacity required per user– pre-allocated capacity (policies) per user, e.g. for phone calls

• decision: – when to use... – which subcarriers (frequencies)– what modulation and coding scheme– for which user– considering policies– considering inter-cell interference

• today: assignment in blocks of 12 subcarriers to limit complexity

eNodeB LTE base station (“evolved node B”)QAM quadrature amplitude modulationTTI transmission time interval

every TTI (1 ms)out of 1200 (at 20 MHz)from 16 to 256 QAM, low to high code rateup to ca. 1000 users per cellper phone callca. 5 – 10 neighbor cells

……

LTE-A:up to 6000 subcarriers

+ 8x8 MIMO


5G Base Station Scheduler

similar to LTE but

• more subcarriers and frequency bands

• massive MIMO

• adaptive beam forming

• many more base stations– in-band relaying

MIMO multiple input multiple output

…… …… …… ……

factor 50 more parameters

to optimize than in LTE

even more, this example is just 8x8!

even more, these are

just 8 antenna elements


5G CQI RRM optimization

• task: select appropriate Modulation and Coding Scheme according to– CQI– cell– other parameters

• AI technology:– offline training (supervised learning)

§ training data provided by simulations– additional data provided by field measurements

CQI channel quality indicatorRAN radio access networkRRM radio resource management

QPSKr=1/2

16-QAMr=1/2

16-QAMr=3/4

64-QAMr=3/4

64-QAMr=1

R. Agrawal: Machine Learning for 5G RAN, https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180807/Documents/Ran_ML.pdf


5G Massive MIMO scheduling

• task: find optimal allocation of antennas and MIMO parameters per user • too complex for exhaustive search• AI technology:

– offline training (supervised learning)§ training data provided by simulations

– quickly find a solution close to the optimum

MIMO multiple input multiple outputR. Agrawal: Machine Learning for 5G RAN, https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180807/Documents/Ran_ML.pdf



5G Beam Pattern Optimization

• task: find optimal allocation of antennas and beam forming parameters

– maximize signal to noise ratio by

§ directing main lobe to user

§ attenuating interference sources

– per user and selected subcarriers

• too complex for exhaustive search

• AI technology:

– learn

§ propagation models (determined by cell topology)

§ optimum depending on user traffic

– interdependence with user scheduling

MIMO multiple input multiple outputR. Agrawal: Machine Learning for 5G RAN,

https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180807/Documents/Ran_ML.pdf



Indoor Location

• problem: coarse GPS precision when indoor

– only ca. 50m accuracy

– big difficulties in height dimension

• AI technology approach:

– supervised learning

§ at many points

§ measure signal strength

§ from all small cells

§ train artificial neural network

– Nokia claims accuracy of ~ 1 m

GPS global positioning system

A. Silver: 3 Ways Nokia Is Using Machine Learning in 5G Networks, IEEE Spectrum, June 2018https://spectrum.ieee.org/tech-talk/telecom/wireless/3-ways-nokia-is-using-machine-learning-in-5g-networks

but what is the business model?

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Cognitive / Cooperative Radio

• alternative to fixed allocation of spectrum– sense RF channel usage and channel quality– observe other users– decide– learn and update model

• currently moving from cognition based approaches to multi-agent based negotiation– including optimization and game theory

• 2019 event: DARPA SC2 challenge– see references

RF radio frequency

see references

image source: https://www.bundesnetzagentur.de/DE/Sachgebiete/Telekommunikation/Unternehmen_Institutionen/Frequenzen/OeffentlicheNetze/Mobilfunknetze/mobilfunknetze-node.html


CONCLUSIONSAI in Mobile Communication Networks


Conclusions

• AI has been in use in communication networks for a long time.

• scale of problem → automation of network management

• complexity of problem → self-learning systems – vendors want to write and maintain less code

• real-time requirements → machine learning as a performant way of supporting optimization of 10k parameters every ms

• causality issues due to delay in available information→ machine learning can preview situations and trigger appropriate actions– handover between cells

– choice of modulation and coding scheme

• some new application areas still in hype state


REFERENCES AND FURTHER READING



Press releases and coverage

• A. Silver, 3 Ways Nokia Is Using Machine Learning in 5G Networks, IEEE Spectrum Tech Talk, June 2018, available at https://spectrum.ieee.org/tech-talk/telecom/wireless/3-ways-nokia-is-using-machine-learning-in-5g-networks

• R. Le Maistre, Huawei Commits Up to $20B for Annual R&D, Fleshes Out AI Pitch, Lightreading, Feb. 2018, available at https://www.lightreading.com/artificial-intelligence-machine-learning/huawei-commits-up-to-$20b-for-annual-randd-fleshes-out-ai-pitch/d/d-id/740446

• I. Morris, Humans Beware: Ericsson Readies Machines to Run the Network, Lightreading, Feb. 2018, available at https://www.lightreading.com/artificial-intelligence-machine-learning/humans-beware-ericsson-readies-machines-to-run-the-network/d/d-id/740766

• I. Morris, Orange Hails AI Progress in 5G With IBM, Nokia, Lightreading, Apr. 2018, available at https://www.lightreading.com/artificial-intelligence-machine-learning/orange-hails-ai-progress-in-5g-with-ibm-nokia/d/d-id/742360

• A. Weissberger, Nokia & China Mobile collaborate on 5G and AI; Nokia & Tencent on 5G in China, IEEE ComSoc Tech Blog, July 2018, available at http://techblog.comsoc.org/2018/07/10/nokia-china-mobile-collaborate-on-5g-and-ai-nokia-tencent-on-5g-in-china/

• DARPA Spectrum Collaboration Challenge, available at https://www.spectrumcollaborationchallenge.com/

PHY physical layerMAC media access controlRRC radio resource control

https://spectrum.ieee.org/tech-talk/telecom/wireless/3-ways-nokia-is-using-machine-learning-in-5g-networks

https://www.lightreading.com/artificial-intelligence-machine-learning/huawei-commits-up-to-$20b-for-annual-randd-fleshes-out-ai-pitch/d/d-id/740446

https://www.lightreading.com/artificial-intelligence-machine-learning/humans-beware-ericsson-readies-machines-to-run-the-network/d/d-id/740766

https://www.lightreading.com/artificial-intelligence-machine-learning/orange-hails-ai-progress-in-5g-with-ibm-nokia/d/d-id/742360

http://techblog.comsoc.org/2018/07/10/nokia-china-mobile-collaborate-on-5g-and-ai-nokia-tencent-on-5g-in-china/

https://www.spectrumcollaborationchallenge.com/


Alarm Consolidation

• A. Vinberg et al., Method and Apparatus for System State Monitoring using Pattern Recognition and Neural Networks, U.S. Patent No. 6,327,550, Dec. 2001, available at https://patents.google.com/patent/US6327550B1/en

• L. Xu, M. Chow, A Classification Approach for Power Distribution Systems Fault Cause Identification, IEEE Trans. Power Systems Vol. 21 No. 1, Feb. 2006, available at https://repository.lib.ncsu.edu/bitstream/handle/1840.2/614/chow_2006_ieee_transactions_power_systems_53.pdf?sequence=1&isAllowed=y

• M. Steinder, A. Sethi, A survey of fault localization techniques in computer networks, Science of Computer Programming Vol. 53 No. 2, Nov. 2004, pp. 165–194, available at https://www.sciencedirect.com/science/article/pii/S0167642304000772

https://patents.google.com/patent/US6327550B1/en

https://repository.lib.ncsu.edu/bitstream/handle/1840.2/614/chow_2006_ieee_transactions_power_systems_53.pdf?sequence=1&isAllowed=y

https://www.sciencedirect.com/science/article/pii/S0167642304000772


Traffic Forecast

• J. Capka, R. Boutaba, Mobility Prediction in Wireless Networks Using Neural Networks, Proc. MMNS 2004, Springer LNCS Vol. 3271, available at https://link.springer.com/chapter/10.1007/978-3-540-30189-9_26

• G. Zhang et al., Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting Vol. 14 No. 1, March 1998, pp. 35–62, available at https://www.sciencedirect.com/science/article/pii/S0169207097000447

• J.J. Prevost et al., Prediction of cloud data center networks loads using stochastic and neural models, Proc. 6th intl. conf. System and Systems Engineering, Albuquerque, NM, USA, June 2011, available at http://ace.wacong.org/Assets/doc/05966610-1.pdf

• T. Anagnostopoulos et al., Predicting the Location of Mobile Users: A Machine Learning Approach, Proc. ICPS’09, London, UK, July 2009, available at https://www.researchgate.net/profile/Theodoros_Anagnostopoulos/publication/234817029_Predicting_the_location_of_mobile_users_A_machine_learning_approach/links/55279c3f0cf2e486ae41178a.pdf

https://link.springer.com/chapter/10.1007/978-3-540-30189-9_26

https://www.sciencedirect.com/science/article/pii/S0169207097000447

http://ace.wacong.org/Assets/doc/05966610-1.pdf

https://www.researchgate.net/profile/Theodoros_Anagnostopoulos/publication/234817029_Predicting_the_location_of_mobile_users_A_machine_learning_approach/links/55279c3f0cf2e486ae41178a.pdf


Fraud Detection

• M. Taniguchi et al., Fraud detection in communication networks using neural and probabilistic methods, Proc. IEEE ICASSP’98, Seattle, WA, USA, May 1998, available at www.academia.edu/download/46160606/article2.pdf

• J. Hollmén, V. Tresp, Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model, Advances in Neural Information Processing, 1999, available at http://www.dbs.ifi.lmu.de/~tresp/papers/nips_book.pdf

• Y. Moreau et al., Detection of mobile phone fraud using supervised neural networks: A first prototype, Proc. ICANN’97, Springer LNCS 1327, available at https://pdfs.semanticscholar.org/a736/90807d653e6a28e8bc8bb8583f933dcc6229.pdf

• P. Burge et al., Fraud detection and management in mobile telecomunications networks, Proc. European Conf. on Security and Detection, Apr. 1997

• P. Burge, J. Shawe-Taylor, An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection, Journal of Parallel and Distributed Computing, Vol. 61, 2001, pp. 915–925

• lots of more articles: search scholar.google.com for mobile network fraud detection neural network

http://www.academia.edu/download/46160606/article2.pdf

http://www.dbs.ifi.lmu.de/~tresp/papers/nips_book.pdf

https://pdfs.semanticscholar.org/a736/90807d653e6a28e8bc8bb8583f933dcc6229.pdf


Churn Prediction

• M.C. Mozer et al., Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry, IEEE Trans. Neural Networks Vol. 11 No. 3, May 2000, available at https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/MozerWolniewiczetal2000.pdf

• S.-Y. Hung et al., Applying data mining to telecom churn management, Expert Systems with Applications Vol. 31 No. 3, Oct. 2006, pp. 515–524, available at http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/telecomchurnanalysis.pdf

• more articles: search scholar.google.com for churn prediction neural network

https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/MozerWolniewiczetal2000.pdf

http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/telecomchurnanalysis.pdf


Network Optimization

• M. Menth, R. Martin, J. Charzinski, Capacity Overprovisioning for Networks with ResilienceRequirements. ACM SIGCOMM Computer Communication Review (CCR), Vol. 36 No. 4, October 2006, pp. 87-98

• C. Reichert, T. Magedanz, A Fast Heuristic for Genetic Algorithms in Link WeightOptimization, Springer Lecture Notes on Computer Science LNCS Vol. 3266, pp. 144–153

• A. Riedl, D.A. Schupke, Routing Optimization in IP Networks Utilizing Additive and ConcaveLink Metrics, IEEE/ACM Trans. Networking, Vol. 15 No. 5, Oct. 2007, pp. 1136–1148, available at http://edari.iust.ac.ir/files/dept_ce/azhari_0a944/adv_computer_network/raahemi/p1136-riedl.pdf

• J. Charzinski, Admission Control versus Dimensioningfor Modern Network Services, MMBnet workshop, Hamburg, Sep. 2007

http://edari.iust.ac.ir/files/dept_ce/azhari_0a944/adv_computer_network/raahemi/p1136-riedl.pdf


Handover

• R. Käallman, Method for Making Handover Decisions in a Radio Communications Network, U.S. Patent No. 5,434,950, 1995

• N.D. Tripathi, Generic Adaptive Handoff Algorithms Using Fuzzy Logic and Neural Networks, PhD Dissertation, Virginia Tech, Aug. 1997, available at https://vtechworks.lib.vt.edu/handle/10919/29267

• G. Pollini, Trends in Handover Design, IEEE Communications Magazine, March 1996, pp. 82–90

• K. Pahlavan et al, Handoff in Hybrid Mobile Data Networks, IEEE Personal Communications Vol. 7, pp. 34–47, Apr. 2000

• S. Michaelis, C. Wietfeld, Comparison of User Mobility Pattern Prediction Algorithms to increase Handover Trigger Accuracy, Proc. VTC-2006 Spring, Melbourne, AUS, May 2006, available at http://docshare02.docshare.tips/files/12752/127523180.pdf

• D. Muñoz-Rodriguez et al., Neural supported hand off methodology in micro cellular systems, Proc. Vehicular Technology Society 42nd VTS Conference, Denver, CO, USA, May 1992, available via IEEE Xplore

https://vtechworks.lib.vt.edu/handle/10919/29267

http://docshare02.docshare.tips/files/12752/127523180.pdf


Cognitive Radio

• N. Abbas, Y. Nasser, K. El Ahmad, Recent advances on artificial intelligence and learning techniques in cognitive radio networks, EURASIP Journal on Wireless Communications and Networking, 2015, available at https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-015-0381-7

• C. Clancy, J. Hecker, E. Stuntebeck, T. O’Shea, Applications of Machine Learning to Cognitive Radio Networks, IEEE Wireless Communications Vol. 14 No. 4, Sep. 2007, available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.145.2762&rep=rep1&type=pdf

• J. Lundén, S. R. Kulkarni, V. Koivunen, H. V. Poor, Multiagent Reinforcement Learning Based Spectrum Sensing Policies for Cognitive Radio Networks, IEEE J. Sel. Topics in Signal Processing, Vol. 7 No. 5, Oct. 2013, pp. 858–868

https://jwcn-eurasipjournals.springeropen.com/articles/10.1186/s13638-015-0381-7

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.145.2762&rep=rep1&type=pdf


LTE standards

see www.3gpp.orgLayer 1 (PHY)• TS 36201: PHY Overview• TS 36211: PHY Modulation• TS 36212: Multiplexing and Channel Coding• TS 36213: PHY related procedures• TS 36214: MeasurementsLayer 2 (MAC, RRC)• TS 36300: MAC and RRC Overview• TS 36321: Media Access Control• TS 36331: Radio Resource Control


http://www.3gpp.org/


Further applications

• H.I. Fahmy et al, Application of Neural Networks and Machine Learning in Network Design, IEEE JSAC Vol. 15, pp. 226–237, Feb. 1997

• K. Das, S.D. Morgera, Adaptive Interference Cancellation for DS-CDMA Systems Using Neural Network Techniques, IEEE JSAC Vol. 16, pp. 1774–1784, Dec. 1998

• X. Wang, S.B. Wicker, An Artificial Neural Net Viterbi Decoder, IEEE Trans. Communications, Vol. COM-44, pp. 165–171, Feb. 1996
