This demo shows how to use the You Only Look Once (YOLO) version 8 framework to identify radio-frequency (RF) signals for spectrum sensing in Software-Defined-Radio (SDR) and Cognitive-Radio (CR) systems. To this purpose, a trained YOLOv8 nano framework was embedded in an IoT device based on a Raspberry Pi 5 and connected to an ADALM-PLUTO SDR board to detect and classify the activity of RF frames around the industrial, scientific, and medical (ISM) frequency band. This framework detects wireless standards such as Bluetooth and WiFi. This hardware demonstrator operates in real-time and can detect RF frames showing its potential application in SDR/CR terminals1.

This paper presents a Radio-Frequency (RF) spectrum prediction system based on a Convolutional Neural Network (CNN) architecture for image-based forecasting intended for Cognitive Radio (CR) terminals. Compared to previous approaches based on the use of time series, we propose a more efficient way for occupancy spectrum prediction based on channel availability tables (derived from spectrograms) to train a deep learning model. As a result, the neural engine is able to predict the best band available for Secondary User (SU) transmission in edge devices. As a proof of concept, a simple CR demonstrator combining a computational model for the neural engine – previously modeled and trained in Python using the Keras API – in MATLAB/SIMULINK with two Software-Defined Radio (SDR) boards has been developed. Three different table sizes were used as input for the predictor and a comparison is presented. The system performance is assessed using real over-the-air signals captured in the 2.412 GHz central frequency with 60 MHz bandwidth to validate the presented approach1.

In this paper, we analyze the use of ChatGPT in teaching random signals and processes and image processing, and we identify pros and cons compared to a classical approach. We focus on these two important engineering topics, which can be challenging for beginners. Regarding random signals and processes, we selected two examples from our learning platform dedicated to better understanding random signals and processes. The first example demonstrates why a linear transformation of normal and uniform random variables does not change the type of variables. The second example explains why the sum of two normal random variables is normal while the sum of two uniform random variables is not. Next, we consider a demo example for teaching image processing from the Image Enhancement Module: Notch Filter for the Moiré Pattern. We conclude the paper by summarizing the findings from the classical, and ChatGPT approaches in the learning process.

User mobility models are used in simulations of mobile communications systems to study characteristics of network performance. One of the models which is in common use is the Random Waypoint Model (RWP). The RWP is a simple mobility model based on random destinations, speeds and pause times. The RWP is often criticised as not representing how humans actually move. Paradoxically, validation against real mobility data is seen as being difficult due to the impracticalities of obtaining real mobility data.We give details of a real world user movement trace from which we obtained data about one individual's destinations, travel routes, average speed and rest times whilst moving throughout a city-wide area. We present results from this real life data and use it to validate some of the key characteristics of the RWP. In this paper we consider the RWP as a model of user mobility in networks that cater for a large geographical area - such as a city.