This is about how to do research in the wireless communications area. We need to understand a lot of concepts and theories by heart to be a researcher in the field of wireless communications.
However, it is very unfortunate that these are not enough to be an independent researcher in the 21st century. The world of wireless communications is moving extremely fast. Researchers are forced to learn lots of new concepts and methodologies to conduct their work.
I have an ambition to post a series of articles introducing “buzz words” in wireless research community. It will touch a light side of various topics including mathematical tools, research methods, system concepts, and marketing terms. The items will be what I need to learn rather than what I already know.
Nice idea Ki Won! I am already waiting for your next post in this series :)
That is so true when you say “it is very unfortunate that these are not enough to be an independent researcher in the 21st century”. There are still so many new concepts to learn; when you think you are almost done with the list you make your self to learn (led by the pace of technological development in wireless communications) you find out that your knowledge is out-dated and need to take in what is new out there.
I am looking forward for the series of articles that you are thinking to publish… =)
@Pamela, you are already on the cutting edge, which means you will be familiar with most of my postings :)
Very good idea Ki Won :) I think we are a bit unlucky about our research area which is maybe the most dynamic area in academic world. So we also need very dynamic learning mechanism. Your upcoming posts will be very helpful for us Ki Won. I am also looking forward to reading them :)
Well, I guess this is like most areas of science that begins in a narrow corner – here it is probably wireless transmission over fading channels in the late 70’s – but expands over a wide area making it impossible for one person to master all aspects. What you need is “abstractions” or “black boxes” – simple models for complex things that you can use to cover up those parts of the complexity that you are not interested in doing research in right now. For instance, if you are studying congestion in wireless multiple access protocols, you are not likely interested in exactly what algorithm is used for error correction in you codec. This is an absolute necessity to maintain efficiency (and sanity ;-) ) in research. The tricky part is always that sometimes you stretch the assumptions about what goes on in the black boxes to an extent that the abstraction no longer works. You need to know at least a bit of the inner workings to know. Sometimes a simple explanation of a “buzzword” may not be enough for this (you need actually to understand what goes on) , sometime we can even care less about the “buzzword”.
Diversity (user, space, frequency, time) was an important keyword, over the decade.