Prediction of Spatial Spectrum in Cognitive Radio using Cellular Simultaneous Recurrent Networks

In cognitive radio networks, it is desirable to determine radio spectrum usage in frequency, time, and spatial domains. Spectrum data improves cognitive radio network planning, sensing, routing, and security. Due to cost concerns, spectrum monitors are deployed sparsely in space, spectrum usage at nearby locations can be modeled for use in these applications. Previous work using neural networks for spatial spectrum prediction involved prior knowledge of transmitter locations as input to the models. In practical scenarios, the prior knowledge is not available. Hence, this work considers prediction of the spatial spectrum without knowing the transmitter location information. The prediction task is achieved by using a specialized recurrent neural network known as Cellular Simultaneous Recurrent Network (CSRN). Our investigation shows the proposed recurrent neural network operates in real-time and is generalized to offer spectrum estimations without further changes to the network, even when a transmitter location is changed. The experiments are conducted in a challenging indoor environment to assess the performance in a practical scenario. Our results suggest the CSRN can learn efficiently to predict signal across an indoor space while transmitters move to different locations. We perform a performance comparison of our proposed technique with an MLP based estimation method. Our analysis further suggests that the CSRN achieves comparable prediction accuracy to that of the MLP based method. The major advantage of the proposed CSRN based method is the ability to perform prediction from new radio configurations without retraining the network, and, hence is more suitable for a real-time practical environment.