This document discusses the evolution and challenges in telecommunications research, highlighting the inefficiencies of current R&D systems and the potential for automation and integration of modern methodologies. It examines the impact of various fields, including computer science and life sciences, on telecommunications and emphasizes the need for innovative research methods to improve effectiveness and collaboration in the industry. The author proposes several recommendations for enhancing research practices, leveraging big data and machine learning, and adapting to the complexities of contemporary problems.

1. Templates and Other Innovative Research Methods
in Telecommunications
Pavel Loskot
Swansea University, United Kingdom
E-mail: p.loskot@swan.ac.uk
14th Int. Conf. on
ELECTRONICS, HARDWARE, WIRELESS and OPTICAL COMMUNICATIONS (EHAC ’16)
Mallorca, Spain, August 19-21, 2016

2. 1/29
Background
I am
• Engineer with 20 yrs experience, mostly in
signal processing and telecommunications
• Senior Lecturer at Swansea University, in
Wales, 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

3. 2/29
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 in
telecommunications
3. Indications of forthcoming automation (“Research 4.0” ?)

4. 3/29
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

5. 4/29
Research evolution
going
down
going
up
science engineering commerce showbusiness?
time
Going up
• #researchers, #papers, complexity of problems and systems, importance of
social connections, virtualization of research, tendency to maintain existing
social structures, focus on short term goals and profits, use of ICT, etc. etc.
Going down
• research income per researcher, contributions per paper, usefulness and
significance of research, importance of making actual contributions

6. 5/29
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-supervisor
relationships? and authors/reviewers/editors in journals?
• when is the best time to enter/leave a graduate school - A, B, C or D?

7. 6/29
Gartner cycle

8. 7/29
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 transceivers
and softwarization for flexibility
• consideration of distributed
modulation, coding and other
PHY tasks

9. 8/29
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 unprecedented
stimulus for development of new PHY solutions
• Exercise:
Consider any technical paper on PHY. Assume
improvements in hardware technology, so that
the target SNR can be increased by X dB. See
the consequences.
log BER
SNR [dB]
region
small SNR

10. 9/29
Impact of Computer Science
Achievements
• concepts: virtualization, security, software development, programming
languages, 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 of
problems for machines → onthology

11. 10/29
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

12. 11/29
Modeling and models
• “all models are wrong, but some of them are
useful” [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

13. 12/29
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)

14. 13/29
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

15. 14/29
Security of networks
Computer networks
• their security well studied, but they are
only part of the overall cyber-social-
technical-physical world we live in
• like in all research, the field now
matured and tools are available for
anybody to become computer hacker
Information and Communication Technologies
Applications and Services
Social Interactions
Social Activities
Cyber−Social Systems
Cyber−Physical Systems
I
II
social
securitycybersecurity
III
E.g. security of social networks
• virtualization of society: departing from the reality
→ lying, exaggerating, deceiving etc. very efficient
if above certain threshold, otherwise absorbed if
below this threshold → so called network effect
• dealing with increasing uncertainty and complexity
→ 1. bureaucracy
→ 2. decisions in order to primarily maintain the
existing social structures, even if these decisions
may be maladaptive (e.g., university rankings, see
also cognitive biases)

16. 15/29
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?
Network
Science Engineering
Network

17. 16/29
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
mathematical
model model
computational analyzer
simple
structured
language
language
graphical
compiler
artificial
intelligencebuilder
machine
learning
recommendation answer
• algebra and signal operations beyond numbers/vectors/matrices/tensors etc.
→ more complex data structures: heterogeneous lists, graphs, databases

18. 17/29
Machine learning
Concepts
theory
estimation statistics
machine
learning
rigorous
methods
heuristic
largesmall
data 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/transform
trainable feature
classifier/predictor
trainableobjects/
scenery reasoning
Opportunities in telecom systems
• evolutionary and online optimization through learning from Big Data

19. 18/29
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 added
value 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), what
telecommunication protocols to use?

20. 19/29
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 probably
several Nobel prizes in medicine, physics and chemistry

21. 20/29
Physical layer security
Mathematically beautiful concept in Information theory, but...
Challenges
• the goal of eavesdroppers (note the plural!) is to get information, not to follow
mathematical models and assumptions
→ eavesdroppers can exploit machine learning algorithms, social engineering
and 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

22. 21/29
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

23. 22/29
Research issues
(additional)
outputs
inputs
Point of diminishing returns
where are we
in 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 → irrelevant
who originate them → plagiarism is complex and no longer simple copy&paste

24. 23/29
Research methods 101
Heuristics [George P´olya’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

25. 24/29
Combinatorial innovations
Papers
• most papers are combinations of:
known concepts, assumptions,
existing models, and other already
published papers
• not only visualization of these
relationships would be useful, but also
would yield more efficient exploration
of such combinatorial space
• many combinations are not sensible
(and yet, they get happily published)
Concepts Assumptions
Models
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 product
development 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)

26. 25/29
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 controlled
information 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)

27. 26/29
Publishing flows
Paper generation process
idea literature
search model
math
derivations
math numerical
verifications
write uprevisionspublication
of 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

28. 27/29
Publishing flows (2)
problem modeling methodology data analysis
problem modeling methodology data analysis
problem modeling methodology data analysis
now
Journals 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

29. 28/29
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 decide
on previous relevant literature
• automated validation of results
→ probabilistic evaluation of correctness, cross-
testing against previous results
• learning trends, predicting research problems to
investigate → 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

30. 29/29
Putting it all together
Suggestions and recommendations (it’s all optional, of course)
• after the information throughput and energy efficiency, the next focus should
be on robustness, to provide performance guarantees under unpredictable and
varying conditions
• there are many ideas in Computer Science that were not yet utilized sufficiently
in the design of telecommunication networks, Machine Learning and Big Data
included
• telecommunication engineering underestimates the importance of considering
the systems in their entirety (like Computer Science tend to do)
• we need more sophisticated tools to deal with the increasing complexity of
problems, and also to maintain the efficiency of R&D systems under ever
increasing number of researchers

31. Thank you!