5G Beyond and 6G Communication Technologies

While initial 5G communication standards have been released and 5G networks have been implemented in some cities around the world, some research groups at world-leading universities and tech-giant companies have already started conducting research on the sixth-generation (6G) communication networks. Although 6G standards are expected to be available in around 2030, there are some speculations what could be included and supported in 6G networks. The major expected technologies or techniques in 6G:  Of course, 6G will support all available technologies in 5G such as  machine-to-machine communication, massive internet of things (massive IoT), enhanced mobile broadband (eMBB),  Ultra-Reliable and Low Latency Communications (URLLC), Flexible network operations. Enhanced network operations which are supported by machine learning applications to improve data-rate, energy-efficiency and flexibility. It will also support vehicles and drones to  enable intelligent transportation systems. Virtual

DFT and FFT with Python and It is applications on various signals

Fast Fourier Transform (FFT) is one of the most important algorithms in computer science, electronics and signal processing engineering. It is a fast solver for Discrete Fourier Transform (DFT). Basically, DFT or FFT transforms signals from time-amplitude domain to frequency-amplitude domain. The reverse form of the FFT is known as Inverse Fast Fourier Transform which converts, naturally, signals from frequency domain to time domain.

FFT is heavily used in communication, radar or computer systems. For example OFDM (orthogonal frequency division multiplexing) is developed based on IFFT and FFT. Since Python is most common used scientific programming language beside Matlab, I would like to present some information about FFT and using it in Python.

Python or Koala 

This blog post (https://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/) includes the basics of the FFT and very clear comparison of  it to DFT. Another blog post (https://www.ritchievink.com/blog/2017/04/23/understanding-the-fourier-transform-by-example/) which includes a very good example of the FFT. This page (https://plot.ly/python/fft-filters/) has FFT filters using Python. An OFDM example which utilizes FFT and IFFT in Python is presented here (https://dspillustrations.com/pages/posts/misc/python-ofdm-example.html) .

An extra link: (http://www.music.helsinki.fi/tmt/opetus/uusmedia/esim/index-e.html) in which you can find some .wav sound examples to process using FFT. An application of short-term FFT on sounds: short term 
Sound Processing with Short Time Fourier Transform

Another working Python example of the short-term FFT which examine .wav files to find out power of the sound at specific frequency and time blocks.