Abstract:
In modern wireless communication, adaptive devices are needed to overcome
different challenges. Adding artificial intelligence to these adaptive devices can further
increase their capabilities. One of its aspects is that the receiver should know which
modulation schemes to handle. So, to achieve this objective, different deep learning
algorithms are used at the receiver side. In this work, we will be using deep learning
techniques for modulation classification. For this purpose, high accuracy coupled with
fast and efficient processing is required. Moreover, most of the traditional methods are
insufficient to classify while providing high accuracy at high end processing. To fulfil
the classification task, a convolutional neural network (CNN) is used. The modulated
signals are converted into a constellation diagram image, following that, the data is put
into a convolutional neural network for training. On the basis of training, thesis objective
is achieved with 95.69% accuracy. To achieve an above-stated objective, there is a need
to design a hardware architecture for a deep learning algorithm CNN for modulation
classification. This thesis is aimed to design hardware architecture for modulation
classifiers based on the convolutional neural network along with field programmable gate
array (FPGA) prototyping solutions.
