## Resolving Phase Ambiguity through Unique Word and Differential Encoding and Decoding

In the context of carrier synchronization, we have discussed the Costas loop and other techniques before. Today, we discuss the significance of differential encoding and decoding for phase ambiguity resolution. Keep in mind that this topic is different than differential detection. In the former case, the data bits are encoded before modulation and decoded after demodulation in a differential manner. Nevertheless, the demodulation is still coherent (i.e., it requires carrier synchronization). In the latter case, the data symbols are detected during demodulation through differential operations, thus canceling the effect of channel phase and eliminating the need for carrier synchronization. Let

## The Coin Toss Puzzle and the Simplest Possible Solution

Recently, I wrote an article on why the Monty Hall problem has perplexed so many brilliant minds where I showed that it was a corner case between 1 open and 1 closed door, while the intuitive but wrong answer is close to the probability curve of 1 open door. Now a coin toss puzzle has appeared on Twitter that has gone viral as it goes against our common intuition of probability and random sequences (such as a series of coin tosses). The puzzle goes as follows. The Problem Flip a fair coin 100 times—it gives a sequence of heads (H)

## What is Supervised Learning?

In a previous article, we had a little introduction to the big picture of machine learning. More than what is it is, we focused on what it is not. Today we explore the category of supervised learning that opens the door to our understanding of advanced machine learning techniques. To see how supervised learning is proliferating in all walks of life, consider a few examples below. Insurance companies can predict the costs of storms damages for the future years due to climate change and adjust their premiums accordingly. During an interview, companies can classify the candidates with high and low

Timing synchronization is one of the most fascinating topics in the field of digital communications. The impact of symbol timing offset has been discussed in the context of single-carrier systems before. The intuition behind how an OFDM system works is also presented in a previous article. However, the problem of timing synchronization is quite different in OFDM systems as compared to single-carrier systems due to the nature of the waveform. Let us explore how a timing error impacts the demodulated waveform in such a scenario. To avoid using many indices, we skip the OFDM symbol index $m$ in the following
Most of the books and resources explain the z-Transform as a mathematical concept rather than a signal processing idea. Today I will provide a simple explanation of how the z-Transform helps in determining whether a system is causal and stable. I hope that this visual approach will help my readers learn this concept in a better manner. The z-Transform For a discrete-time signal $h[n]$ (that is the impulse response of a system), the z-Transform is defined as $$\label{equation-z-transform} H(z) = \sum _{n=-\infty}^{\infty} h[n]z^{-n}$$ Then, $z$ is a complex number and hence can be written as \[ z = re^{j\omega}