Discover digital signal processing fundamentals, delve into signal design and analysis, learn about transforms, and develop practical OFDM systems focusing on modulation and demodulation. Gain insights into time and frequency domains.

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We live in a world filled with signals which can be represented by sequences of numbers in a digital machine. These sequences of numbers can be manipulated by algorithms and have numerous applications in audio, images, video, wireless transmission and countless other domains. 

In this course, you’ll understand the field of digital signal processing with a focus on the design and analysis of signals, and algorithms. You’ll cover the DSP fundamentals of signals, systems and transforms. You’lll learn about the basic signal types and perform different operations. You’ll develop an understanding about the frequency domain and how to represent signals in time, and the transformation from one domain to the other using the Fourier transform. You’ll also learn about digital systems. Finally, you’ll develop a practical OFDM system focusing on modulation and demodulation.

By the end of this course, you’ll be able to model, analyze and design signals and systems in greater depth, in the time and frequency domains.

Fundamentals of Digital Signal Processing

The convolution output $y[n]$ between two signals $x[n]$ and $h[n]$ is expressed as:
$$
y[n]=\sum _m x[m]h[n-m]
$$

## Real and imaginary parts

When the two signals are complex, we can write
$$
\begin{align*}
x[n] &= x_I[n]+jx_Q[n]\\
h[n] &= h_I[n]+jh_Q[n]
\end{align*}
$$
in which the subscripts $I$ and $Q$ refer to inphase (real) and quadrature (imaginary) parts of the signal.

Now the multiplication in convolution can be split as:
$$
\begin{align*}
y_I[n]&=\sum _m x_I[m]h_I[n-m] - \sum _m x_Q[m]h_Q[n-m]\\
y_Q[n]&= \sum _m x_Q[m]h_I[n-m] + \sum _m x_I[m]h_Q[n-m]
\end{align*}
$$

This can also be written as:
$$
\begin{align*}
y_I[n] &= x_I[n]*h_I[n] - x_Q[n]*h_Q[n]\\
y_Q[n] &= x_Q[n]*h_I[n] + x_I[n]*h_Q[n]
\end{align*}
$$

This is drawn in the figure below:

The convolution output $y[n]$ between two signals $x[n]$ and $h[n]$ is expressed as:
$$
y[n]=\sum _m x[m]h[n-m]
$$

# Real and imaginary parts

When the two signals are complex, we can write
$$
\begin{align*}
x[n] &= x_I[n]+jx_Q[n]\\
h[n] &= h_I[n]+jh_Q[n]
\end{align*}
$$
in which the subscripts $I$ and $Q$ refer to inphase (real) and quadrature (imaginary) parts of the signal.

Now the multiplication in convolution can be split as:
$$
\begin{align*}
y_I[n]&=\sum _m x_I[m]h_I[n-m] - \sum _m x_Q[m]h_Q[n-m]\\
y_Q[n]&= \sum _m x_Q[m]h_I[n-m] + \sum _m x_I[m]h_Q[n-m]
\end{align*}
$$

This can also be written as:
$$
\begin{align*}
y_I[n] &= x_I[n]*h_I[n] - x_Q[n]*h_Q[n]\\
y_Q[n] &= x_Q[n]*h_I[n] + x_I[n]*h_Q[n]
\end{align*}
$$

This is drawn in the figure below:

Explore why convolution between two complex signals is more computationally expensive compared to real signals.

Introduction to Signals

Basic Signals and Operations

Complex Signals and the Frequency Domain

Sampling a Signal

The Discrete Fourier Transform

Fourier Transform Properties

Digital Systems

Putting It All Together: An OFDM Modem

Demodulating an OFDM Signal Stream

The Last Words

Bibliography

Convolution of Complex Signals

Real and imaginary parts