Harmonic distortion in digital sinusoidal generators
In Problem 4.36 of DSP-Proakis [1], the task is to provide insights into harmonic distortion which may be present in practical sinusoidal generators. Consider the signal , where . My take: The discrete time signal of fundamental period can consist of frequency components separated by radians or cycles (Refer Section4.2 in [1]). The Fourier series…
GATE-2012 ECE Q39 (communication)
Question 39 on communication from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q39. The signal as shown is applied both to a phase modulator (with as the phase constant) and a frequency modulator (with as the frequency constant) having the same carrier frequency. The ratio for the same maximum phase deviation is,…
OT: Happy Schools Blog
Mr. Raghuram contacted me and informed about Happy Schools Blog. He writes about Graduate School Admission in U.S., Job opportunities for students, University Selection based on his personal experience. He recently published few articles which might of interest to some of our readers. Here are the URL for few articles:
Softbit for 16QAM
In the post on Soft Input Viterbi decoder, we had discussed BPSK modulation with convolutional coding and soft input Viterbi decoding in AWGN channel. Let us know discuss the derivation of soft bits for 16QAM modulation scheme with Gray coded bit mapping. The channel is assumed to be AWGN alone.
Symbol Error Rate for 16PSK
In this post, let us try to derive the symbol error rate for 16-PSK (16-Phase Shift Keying) modulation. Consider a general M-PSK modulation, where the alphabets, are used. (Refer example 5-38 in [DIG-COMM-BARRY-LEE-MESSERSCHMITT]) Figure: 16-PSK constellation plot
Bit error rate for 16PSK modulation using Gray mapping
In this post, let us derive the theoretical bit error probability for 16PSK modulation using Gray coded mapping. For deriving the equation, we will refer material from the following posts: (a) Symbol Error Rate for 16PSK (b) Gray code to Binary code conversion for PSK (c) Binary to Gray code conversion for PSK As discussed…
Hamming (7,4) code with hard decision decoding
In previous posts, we have discussed convolutional codes with Viterbi decoding (hard decision, soft decision and with finite traceback). Let us know discuss a block coding scheme where a group of information bits is mapped into coded bits. Such codes are referred to as codes. We will restrict the discussion to Hamming codes, where 4…
GATE-2012 ECE Q25 (math)
Question 25 on math from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q25. If , then the value of is, (a) (b) (c) (d) 1
Closed form solution for linear regression
In the previous post on Batch Gradient Descent and Stochastic Gradient Descent, we looked at two iterative methods for finding the parameter vector which minimizes the square of the error between the predicted value and the actual output for all values in the training set. A closed form solution for finding the parameter vector is possible, and in this post…
BER for BPSK in Rayleigh channel
Long back in time we discussed the BER (bit error rate) for BPSK modulation in a simple AWGN channel (time stamps states August 2007). Almost an year back! It high time we discuss the BER for BPSK in a Rayleigh multipath channel. In a brief discussion on Rayleigh channel, wherein we stated that a circularly…
Migrated to Amazon EC2 instance (from shared hosting)
Being not too happy with the speed of the shared hosting, decided to move the blog to an Amazon Elastic Compute Cloud (Amazon EC2) instance. Given this is a baby step, picked up a micro instance running an Ubuntu server and installed Apache web server, MySQL, PHP . After doing a bit of tweaking with this new…
GATE-2012 ECE Q36 (math)
Question 36 on math from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q36. A fair coin is tossed till a head appears for the first time. The probability that the number of required tosses is odd, is (A) 1/3 (B) 1/2 (C) 2/3 (D) 3/4 Solution Let us start by…
Gradients for linear regression
Understanding gradients is essential in machine learning, as they indicate the direction and rate of change in the loss function with respect to model parameters. This post covers the gradients for the vanilla Linear Regression case taking two loss functions Mean Square Error (MSE) and Mean Absolute Error (MAE) as examples. The gradients computed analytically…