This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.

In this thesis, anomalies are defined as data points whose value differs significantly from the normal pattern of the data set. Anomalousobservations on time series measured on satellites has a growing need of being detected directly on board the space-orbit systems to for example prevent malfunction and have efficient data management. Unibap's service Spacecloud Framework (SCFW) is developed to allow the deployment of machine learning applications directly on the satellite systems. Neural Networks (NNs) is therefore a candidate for the possibility to predict anomalies on satellite time series. The work described in this reportaims to implement and create a benchmark for Convolutional Autoencoder NN (CNN) and a Long Short-term Memory Autoencoder NN (LSTM). These implementations are used to determine which NN can be applied in Unibap's SCFW and detect anomalies with accuracy.  The NNs are trained and tested using a public data-sets which containreal and artificial time-series with labelled anomalies. The anomaliesare detected by reconstructing the time series and creating a threshold between the output and the input. The algorithms classify a data pointas an anomaly if it lies above the threshold. The networks are evaluated based on accuracy, execution time and size, to assess whether they are suited for implementation in SCFW. The results from the NNs indicatethat CNN is best suited for further application. On this basis, anattempt to implement CNN in SCFW is performed, but failed due to time and documentation limitations. Therefore, further research is needed to identify whether CNN can be implemented in SCFW and successfully detect anomalies.