Templates and Other Innovative Research Methodsin Telecommunications

Pavel Loskot

Swansea University, United KingdomE-mail: p.loskot@swan.ac.uk

14th Int. Conf. on

ELECTRONICS, HARDWARE, WIRELESS and OPTICAL COMMUNICATIONS (EHAC ’16)

Mallorca, Spain, August 19-21, 2016

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Background

I am

• Engineer with 20 yrs experience, mostly insignal processing and telecommunications

• Senior Lecturer at Swansea University, inWales, United Kingdom

Some types of projects I was involved in

• telecommunication networks: from signals to protocols

• social networks: broadband network subscribers behavior and forecasting

• biological networks: whole-cell simulations

• air-transport networks: load optimization

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Introduction

Motivation

• R&D methods and publication procedures changed significantly in past 30 years

→ probably true not only in telecommunication sector

• but current R&D systems and procedures seem to be obsolete and inefficient

→ little use of Big Data and Machine Learning

→ duplication of efforts, reinventing the wheel

→ ideas abundant, knowledge and (some) skills became commodity

→ many R&D tasks are dull and laborious

→ growing importance of social inter-connections

Outline

1. Some views of the current system

2. (Critical) review of some research areas intelecommunications

3. Indications of forthcoming automation (“Research 4.0” ?)

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Some quotes

• “25 years ago, if you worked hard and played by the rules,

you would be able to have a solid middle-class life”

• “the broken promises of education, jobs, and incomes have become

more visible and painful”

• “price competition tends to work as a forward auction for those

at the top and as a reverse auction for those near the bottom of

occupational groups. Take the example of university professors ...”

• “inequalities in terms of winner-takes-all markets”

• “knowledge has become a commodity ... and it follows money”

students#College

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Research evolution

goingdown

goingup

science engineering commerce showbusiness?time

Going up

• #researchers, #papers, complexity of problems and systems, importance ofsocial connections, virtualization of research, tendency to maintain existingsocial structures, focus on short term goals and profits, use of ICT, etc. etc.

Going down

• research income per researcher, contributions per paper, usefulness andsignificance of research, importance of making actual contributions

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Dunning-Kruger effect

Questions

• how about interactions among researchers of same/different type A, B, C or D?

• what are the implications to graduate schools and PhD student-supervisorrelationships? and authors/reviewers/editors in journals?

• when is the best time to enter/leave a graduate school - A, B, C or D?

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Gartner cycle

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Wireless physical layer (PHY)

History lessons

• signal processing should be just a step ahead of the technology

• alternating focus: PHY in 90’s, now upper layers including multiple access→ PHY has become a commodity (many off-the shelf solutions available)

• great uncertainty about the propagation environment (channels, interference)→ robustness is far more important than possibly great performance

→ simple and robust solutions always preferred to optimum but complex

• unsolved fundamental problem→ reliable non-line-of-sight communications with no supporting infrastructure

Recent trends

• modular structure of transceiversand softwarization for flexibility

• consideration of distributedmodulation, coding and otherPHY tasks

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Wireless physical layer (PHY) (2)

Tacit assumptions

• well-behaved channels (stationary and ergodic)→ capacity approaching signaling (often realistic channels aren’t well behaved)

• SNR should be large enough, especially in multi-user and distributed systems→ so far, these systems cannot operate in low-mid SNR regime

Possible breakthrough?

• small SNR implies large BER (no matter what)

• however, new less-noisy hardware (beyond

semiconductors) can provide unprecedentedstimulus for development of new PHY solutions

• Exercise:

Consider any technical paper on PHY. Assumeimprovements in hardware technology, so thatthe target SNR can be increased by X dB. Seethe consequences.

log BER

SNR [dB]

regionsmall SNR

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Impact of Computer Science

Achievements

• concepts: virtualization, security, software development, programminglanguages, data structures, complexity, protocols, API, visualization, etc. etc.

• still very under-utilized in development of telecom equipment and networks

Traditionally

• Engineering builds components

• Computer Science builds systems from components→ impact more noticeable

E.g. software development

• established testing and validation strategies

• evolving software development strategies:agile/ scrum, open source, pipelines, reuse

E.g. programming languages

• semantic and syntactical description ofproblems for machines→ onthology

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Impact of Life Sciences

Achievements

• empirical strategies to study very complex and poorly defined systems

• drive the need for new technology (ICT, Big Data etc.)

• translational research (from the lab to the clinical practice)

• understanding the Nature will inspire complex technology

STRUCTURE FUNCTION

Reverse (data-driven) vs forward (application-driven) modeling

measurements model

application

available measurements constrain

possible applications

model

measurements

application

application determines required

measurements

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Modeling and models

• “all models are wrong, but some of them areuseful” [G. Box, 1976]

• “With four parameters I can fit an elephant,and with five I can make him wiggle his trunk.”[J. von Neumann]

• complex systems are often associated with(infinitely) many models

Big questions

• optimality→ is it even possible? in what sense? what constraints?

– if two models, how to choose the better one?

– can models of the same system be conflicting with each other?

• systematic approach→ enable automation of modeling

Reductionism [Wikipedia]

• Ontological: whole of reality consists of a minimal number of parts

• Methodological: explanations in terms of ever smaller entities

• Theoretical: new theory doesn’t replace the old, reduces it to more basic terms

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BER vs SNR performance

SNR [dB]

log BER

∆ SNR

∆ log BER

A

B

C

System A vs system B

• initially big SNR gain reduced to zero and even becomes negative at large SNR

System B vs system C

• seemingly large SNR gain corresponds to only a small reduction in BER(in other words, small sacrifice in BER gives the same operational SNR as C)

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Energy efficiency

Different players see different things

• negligible 3−5% overall energy consumption of ICT within the whole economy

• stand-by energy consumption is key (rarely considered in research papers)

→ 1. transmit as fast as possible → 2. turn-off things for as long as possible

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Security of networks

Computer networks

• their security well studied, but they areonly part of the overall cyber-social-technical-physical world we live in

• like in all research, the field nowmatured and tools are available foranybody to become computer hacker

Information and Communication Technologies

Applications and Services

Social Interactions

Social Activities

Cyber−Social Systems

Cyber−Physical Systems

I

II

soci

alse

curit

ycy

ber

secu

rity

III

E.g. security of social networks

• virtualization of society: departing from the reality→ lying, exaggerating, deceiving etc. very efficientif above certain threshold, otherwise absorbed ifbelow this threshold→ so called network effect

• dealing with increasing uncertainty and complexity

→ 1. bureaucracy

→ 2. decisions in order to primarily maintain theexisting social structures, even if these decisionsmay be maladaptive (e.g., university rankings, seealso cognitive biases)

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Network Science

Achievements

• tools to study concurrent relationships among large number of entities→ many complex systems have a network structure

• modeling of important processes: epidemic spreading, information cascades,social learning, searching networks, maximum flow, etc.

• modeling of important phenomena: emergence, self-organization, co-evolution,disruptive events and other dynamics, etc.

Next step?

NetworkScience Engineering

Network

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Signal processing

Challenges

• efficiency of mathematical vs computational models

• parameter explosion problem→ which are important? → sensitivity analysis

Need for

• more efficient mathematical descriptions→ existing mathematical notation easily obscure the underlying knowledge

→ combine the power of the human brain with that of the machines

• automation of problem solving

→ and eventually also automation of problem identification and formulation

mathematicalmodel model

computational analyzer

simplestructuredlanguage

languagegraphical

compilerartificial

intelligencebuildermachinelearning

recommendation answer

• algebra and signal operations beyond numbers/vectors/matrices/tensors etc.

→ more complex data structures: heterogeneous lists, graphs, databases

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Machine learning

Concepts

theoryestimation statistics

machinelearning

rigorousmethods

heuristic

largesmalldata volumes

Drivers

• availability of commodity computing platforms (GPUs)

• availability of Big Data for training/learning

• availability of Deep Learning architectures

→ efficient learning/approximation of complex system functions

→ back on track towards Artificial Intelligence

extraction/transformtrainable feature

classifier/predictor

trainableobjects/scenery reasoning

Opportunities in telecom systems

• evolutionary and online optimization through learning from Big Data

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Wireless power transfer and energy harvesting

Challenges

• the R&D value primarily driven by the principles of electrical engineering→ achievable distance vs power transferred, safety, efficiency→ many useful applications

• superimposing information transmission is certainly possible, but the addedvalue is negligible (cf. developments in power-line communications)

→ the bottom line, this is a topic for electrical not telecommunication engineers

• however, in the energy harvesting powered transceivers (e.g. sensors), whattelecommunication protocols to use?

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Nano-scale engineering

Nanotechnology

• many applications, some may benefit from simple communication functions→ these communications is more of a physics/chemistry/materials problem

Two worlds

• non-living (in-vitro): primarily physics, chemistry and materials eng. problems→ little opportunities (or need) for telecommunication engineers

• living (in-vivo): very complex bio-physics and bio-chemistry problems→ on the way towards in-vivo nano-scale telecom networks there are probablyseveral Nobel prizes in medicine, physics and chemistry

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Physical layer security

Mathematically beautiful concept in Information theory, but...

Challenges

• the goal of eavesdroppers (note the plural!) is to get information, not to followmathematical models and assumptions→ eavesdroppers can exploit machine learning algorithms, social engineeringand pivot attacks etc. → security is a complex matter

• any number of collaborating eavesdroppers can form any compounded channel→ in figure above, a smart eavesdropper just needs to be close to desired user

?reliable & secureHow to make systems

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Cyber-physical systems

Cyber

Physical Physical

Cyber

Main idea

• immerse, not combine cyber systems with physical systems

• cyber systems represent new interface to physical systems→ virtualization

Challenges

• communication infrastructure is again a commodity

• security, energy efficiency (e.g. battery powered sensors), limited bandwidth,coverage over multiple geographical scales, data management etc.

• crucially, how to exploit digital observations to improve the physical system?

→ this is a fundamental question for the specialists (transportation, healthcare,build environment, smart grid, etc.), not for telecommunication engineers

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Research issues

(additional)outputs

inputs

Point of diminishing returns

where are wein telecomunications?

Trends

• engineering disciplines mature and complexity growths→ diminishing returns

• fragmentation of systems→ loosing the big picture perspective

• individuals→ groups competition→ survival mentality (“anything goes”)

New ideas

“Everything new is a well/deliberately forgotten old.”

• ideas abundant and everywhere (Internet)→ zero production cost→ irrelevantwho originate them→ plagiarism is complex and no longer simple copy&paste

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Research methods 101

Heuristics [George Polya’s 1945]

1. If you are having difficulty understanding problem, draw a picture.

2. Assume some solution and see what you can derive from that (“go backward”).

3. If the problem is abstract, try examining a concrete example.

4. Try solving more general problem first → “inventor’s paradox” (more ambitious

plans may have more chances of success)

Some problems where heuristics have been very successful

• iterative (turbo) decoding

• Internet routing

• machine learning (Deep learning, naive Bayes, . . . )

• Computer Science (antivirus, searches, . . . )

Theory and practice are no longer clearly separable.

Research methods:

deterministic→ iterative→ evolutionary→ stochastic

simple

robust

optimum

complex

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Combinatorial innovations

Papers

• most papers are combinations of:known concepts, assumptions,existing models, and other alreadypublished papers

• not only visualization of theserelationships would be useful, but alsowould yield more efficient explorationof such combinatorial space

• many combinations are not sensible(and yet, they get happily published)

Concepts AssumptionsModels

2nd law of thermodynamics:

combinations (reuse) are a lot

easier than new concepts or models

Available tools

• rule-developing experimentation (RDE)→ systematic exploration of ideas, designs, and end-user needs for productdevelopment and service provisioning

• block combinatorial designs (BIBDs, PBDs)→ creating subsets from a given set that are useful for a particular application(e.g. laboratory experiment design, and exploring the degrees-of-freedom)

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Generalizations and translations

Main idea

• learn underlying concept in one or more successful products/papers/objects

→ analogy of supervised machine learning but with less and more complex data

while exploiting computationally much more powerful human brain

Case study: Internet

• large scale network of controlledinformation flows anytime and anywhere

Translations

• new data sources: sensors

• who communicates: also machines

• get computing power: create clouds

• new uses: introduce e-services

Generalizations

• new flows: energy (electricity), vehicles(cars), parcels→ Physical Internet

• flows→ interactions (social networks)

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Publishing flows

Paper generation process

idea literaturesearch model

mathderivations

math numericalverifications

write uprevisionspublicationof paper

• explicit load-sharing among the co-authors has enormous impact on productivity

• finite volume of ideas is shared by increasingly many researchers

• due to maturity of many fields and availability of user-friendly research tools,the entry barriers to research decreased significantly, at least for some tasks in

figure above, thus further adding to nonsensical competition in the research

• moreover, many tasks became labor work → sooner or later will be automated

Possible solution

• move to open-source research→ crowdsourced research

→ well established in open-source software development

→ align the efforts of brilliant minds and brains

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Publishing flows (2)

problem modeling methodology data analysis

problem modeling methodology data analysis

problem modeling methodology data analysis

nowJournals Future journals

New packaging

• collaborative decisions on important problems→ problem rankings

• discuss best methodology, modeling/analysis strategy

• vote on best solutions

→ much more efficient

use of research

resources

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Research automation

Already available (but not yet used extensively)

• information processing: data and text mining, knowledge discovery

• infrastructure: research labs in the cloud

• more flexible data structures: PDF→ HTML

Would be useful (and can be already implemented)

• automated literature search service→ by an expert system, not the authors to decideon previous relevant literature

• automated validation of results→ probabilistic evaluation of correctness, cross-testing against previous results

• learning trends, predicting research problems toinvestigate→ recommender systems for research

• visualizations of relationships→ who cites who

• detecting duplication and plagiarism

→ avoid re-selling same idea under different description, suppress info noise

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Putting it all together

Suggestions and recommendations (it’s all optional, of course)

• after the information throughput and energy efficiency, the next focus shouldbe on robustness, to provide performance guarantees under unpredictable andvarying conditions

• there are many ideas in Computer Science that were not yet utilized sufficientlyin the design of telecommunication networks, Machine Learning and Big Dataincluded

• telecommunication engineering underestimates the importance of consideringthe systems in their entirety (like Computer Science tend to do)

• we need more sophisticated tools to deal with the increasing complexity ofproblems, and also to maintain the efficiency of R&D systems under everincreasing number of researchers

Thank you!