We know data rate is bits per second. It can be also considered as baud rate(symbols per second) times the number of bits in symbol. So, if to increase data rate, we can increase baud rate or we can increase number of bits in a symbol. Why can't we keep on increasing these two? Can someone explain what happens with these 2 occasions separately?
This is essentially a physics question. We can play all sorts of games with how to physically represent a signal (hence, getting more bits per baud), but at the end of the day you can only physically convey so much information for any given rate of change of a signal. If you want to communicate faster, you have to up the frequency, which means having signals that change faster in time -- and nature ultimately limits how fast you can change the signal.
See:
http://en.wikipedia.org/wiki/Nyquist_rate
This gets even worse when you add noise:
http://en.wikipedia.org/wiki/Shannon%E2%80%93Hartley_theorem
Related
I am a little confused about the BER. I found that the BER of 16QAM is better than that of 32QAM. is this right, if so, why we go to higher QAM (i.e. 32, 64, and etc).
thank you in advance
If one would target the best BER, you wouldn't even go up to 16QAM and stick at 4QAM / QPSK. You'll have a secure transmission, with the downside of a low spectral efficiency.
16QAM can achieve a spectral efficiency of 4 Bits/s/Hz, where 64QAM has already 6 Bits/s/Hz. This means, you can increase the bitrate by 50% compared to the previous setting. This is especially important if you have limited resources like channels or bandwidth. In Wireless transmission you'll have a bandwidth of a few MHz and there's no parallel channel for other users, so spectral efficiency is the key to increase data throughput. (In fact there's something like an parallel channel, called MIMO, but you get the key)
See the table here for an overview of wireless transmission systems and their spectral efficiency. Spectral Efficiency
Even for more robust transmission systems (in case of BER) you can pick relatively high modulation grades and use the increased number of bits for redundant information. In case of a bit error the receiver is able to repair the original content. This is called Forward Error Correction
I would like to know number of nodes a typical network(CAN/LIN/MOST) Contain and On which basis we will decide?
There's no fixed number but it depends on multiple factors:
Baud rate: Lower the baud rate more the number of nodes. It takes more time for signal to propogate and higher baud rate won't allow that delay.
Wiring: Every node will add capacitance to bus so your wiring scheme will also impact node count.
Signal strength weakens as bus length/node count increases. Hence repeaters may be requred.
I am working on top500 supercomputer database.(http://www.top500.org/)
Rmax is maximum performance
RPeak is theorotical maximum performance.
Does Ratio of Rmax to RPeak results to something? Like say efficiency? or anything which could say something about a supercomputer.
Could it be something like Lie factor?
Rmax is determine by HPL benchmark. Details aren't always published, unfortunately, but in most cases, the problem dimension requires a decent fraction of total memory.
Rpeak is determined by multiplying the number of floating point units (usually vector) per processor times processor count times the number of floating point instructions that can be issued per second. This is a bit hard today because of frequency variation.
The ratio can be viewed as an efficiency factor, although it may not be productive to use the result for assigning value to systems. 75% of 1000 is the same as 100% of 750, and if they have the same dollar and power costs, what difference does it make?
I tend to view the combination of Top500, Graph500 and HPCG results as a more robust way to compare systems, but one cannot ignore power and dollar costs if one pays for systems (most users do not, at least directly).
I want to know how to calculate interference weight in the combination of APs running on different channel frequencies.
Lets say, i have 10 APs, with different modes running, like 11a, 11na and 11ac.
If 11a is running a 20MHz channel say (36), and 11na devices running with 40MHz (36 and 40), and 11ac devices running with 80MHz(36, 40,44,48).
Now how does these frequencies interfere with each other and how to calculate the interference weight among these frequencies.
First of all you should read the 802.11-2012 standard and 802.11ac amendment to understand the difference in PHY between the 3 modes. But more generally I think a more precise definition of "interference weight" or at least how you would use this measure, is needed to assist.
In practice, interference depends on many variables, is highly dynamic, and has many elements of randomness. The standard allows you to define a quantity measure called RSSI of the signal you are measuring but the actual method is proprietary and no vendor will be the same. Moreover different hardware/firmwave/drivers will measure signal and SNR differently at the exact same location and time.
IMO, all measures of signal quality are by definition averages of some kind. Interference can be more precisely defined and measured on a per-symbol basis but with millions of symbols per second this is of limited use
I have been looking into how to convert my digital data into analog.
So, I have a two column ASCII data file (x: time, y=voltage amplitude) which I would like to convert into an analog signal (varying Voltage with time). There are Digital to Analog converters, but the good ones are quite expensive. There should be a more trivial way to achieve this.
Ultimately what I'd like to do is to reconstruct the original time variant voltage which was sampled every nano-second and recorded as an ASCII data file.
I thought I may feed the data into my laptop's sound card and re-generate the time variant voltage which I can then feed into the analyzer via the audio jack. Does this sound feasible?
I am not looking into recovering the "shape" but the signal (voltage) itself.
Puzzled on several accounts.
You want to convert into an analog signal (varying Voltage with time) But the what you already have, the discrete signal, is indeed a "varying voltage with time", only that both the values (voltages) and times are discrete. That's the way computers (digital equipment, in general) work.
Only when the signal goes to some non-discrete medium (eg. a classical audio cable+plug) we have an analog signal. Precisely, the sound card of your computer is at its core a "Digital to Analog converter".
So, it appears you are not trying to do some digital processing of your signal (interpolation, or whatever), you are not dealing with computer programming, but with a hardware thing: getting the signal to a cable. If so, SO is not the proper place. YOu might try https://electronics.stackexchange.com/ ...
But, on another thing, you say that your data was "sampled every nano-second". That means 1 billion samples per second, or a sample freq of 1Ghz. That's a ridiculously high frequency, at least in the audio world. You cant output that to a sound card, which would be limited to the audio range (about 48Khz = 48000 samples per second).
You want to just fit a curve to the data. Assuming the sampling rate is sufficient, a third-order polynomial would be plenty. At each point N, you fit a cubic polynomial to points N-1, N, N+1, and N+2, and then you have an analytic expression for the data values between those points. Shift over one, and repeat. You can average the values for multiple successive curves, if you want.