Following a brief discussion with my friend Mr. Rethnakaran Pulikkoonattu on phase noise profiles, he pointed me to his write up on Oscillator Phase Noise and Sampling Clock Jitter . In this post, we will discuss the math behind integrating the phase noise power spectral density (in dBc/Hz) to find the root mean square jitter value.
The post on thermal noise described the noise produced by resistor ohms over bandwidth at temperature Kelvin. In this post, let us define the noise voltage at the input and output of a resistor network and further use it to define the Noise Figure of such a network.
Question 13 on analog electronics from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q13. The diodes and the capacitors in the circuit shown are ideal. The voltage across the diode is (A) (B) (C) (D) Solution The first half of the circuit is a negative clamper circuit and the second half…
Using the services of a new author ‘RV’, we are starting a new series of articles in the blog. Typically in India, many of the competitive examinations pertaining to Engineering (GATE, IES) and rectuitment by private and public sector companies (ISRO, BSNL, BEL, BHEL) uses examination with objective questions for the first level screening. We…
In two previous posts, we have discussed Convolutional Coding and the associated hard decision Viterbi decoding. In this post lets extent Viterbi decoding algorithm to soft input decision scheme. The modulation used is BPSK and the channel is assumed to be AWGN alone.
In a previous post, we discussed about a probable first order digital PLL for tracking constant phase offset. The assumption was that as the phase offset is small and the bits gets decoded correctly, the phase difference between the ideal and actual constellation gives the initial value of phase. However, in typical scenarios it may…
In the past, I have wondered about discussing personal thoughts in this blog. The answer in my mind was NO and I ve been focusing only on technical topics till date. However, there is a change of mind, thanks to my friend Manoj. Thanks to him, I happened to see Prof. Randy Pausch’s brief 10…
The earlier posts on phase noise discussed about phase noise in oscillators, conversion of phase noise profile to jitter and the impact of phase noise on the error vector magnitude (evm). This post discuss the impact of phase noise on the spectrum of the transmit waveform. A simple random QPSK modulated symbols, oversampled and passed…
Thanks to Prof. K V S Hari, I was informed that the centenary year for Indian Institute of Science (IISc), Bangalore starts on May 27th 2008. As part of the event, a conference with the theme Managing Complexity in a Distributed World (MCDES) is to be held at IISc from May27th to 31st 2008.
Following the request by Siti Naimah, this post discuss the bit error probability for coherent demodulation of binary Frequency Shift Keying (BFSK) along with a small Matlab code snippet. Using the definition provided in Sec 4.4.4 of [DIG-COMM-SKLAR]), in binary Frequency shift keying (BFSK), the bits 0’s and 1’s are represented by signals and having…
This post attempts to build further on the MIMO equalization schemes which we have discussed – (a) Minimum Mean Square Error (MMSE) equalization, (b) Zero Forcing equalization with Successive Interference Cancellation (ZF-SIC) and (c) ZF-SIC with optimal ordering. We have learned that successive interference cancellation with optimal ordering improves the performance with Zero Forcing equalization….
The definition of Toeplitz matrix from [1] is: A matrix is said to be Toeplitz if the elements are determined completely by the difference .
In the past, we had discussed BER for BPSK in flat fading Rayleigh channel and BER for BPSK in a frequency selective channel using Zero Forcing Equalization. In this post, lets discuss a frequency selective channel with the use of Minimum Mean Square Error (MMSE) equalization to compensate for the inter symbol interference (ISI). For…
I happened to stumble on Prof. Andrew Ng’s Machine Learning classes which are available online as part of Stanford Center for Professional Development. The first lecture in the series discuss the topic of fitting parameters for a given data set using linear regression. For understanding this concept, I chose to take data from the top…