Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing

Abstract

This article introduces a novel energy efficient spectrum sensing method for multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems in dynamic spectrum sharing (DSS) environments. To be specific, a spiking reservoir computing (RC) based approach is introduced for spectrum sensing of MIMO-OFDM systems to take advantage of the spatial and temporal correlations of the environment. In particular, we develop a spiking delayed feedback reservoir (DFR). DFRs are a new class of recurrent neural networks (RNNs) which tackle the vanishing gradient problem of traditional RNNs while spiking neurons are energy efficient and biologically plausible neuron models. We extend our introduced model in time and space domains to capture the spatial and temporal correlations in DSS environments. Conditional generative adversarial networks (cGANs) are introduced to tackle the data scarcity issue arose from applying machine learning techniques to MIMO-OFDM systems where training data is limited. Simulation results suggest that the probability of detection of the introduced RC based spectrum sensing at the low signal-to-noise (SNR) regime is significantly higher than the state-of-the-art techniques and the computational complexity is also reduced compared with traditional RNNs.