Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks

Abstract

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user (PU) which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS which is hard to compute and also hard to use in practice, the strategy for combining the individual sensing results of the SUs is learned with a CNN using training sensing samples. Accordingly, an environment-specific CSS which considers both spectral and spatial correlation of individual sensing outcomes, is found in an adaptive manner regardless of whether the individual sensing results are quantized or not. Through simulation, we show that the performance of CSS can be improved by the proposed DCS with low complexity even when the number of training samples is moderate. Index TermsCognitive radio network, cooperative spectrum sensing, deep learning, convolutional neural network, correlation.

20 Years of Evolution From Cognitive to Intelligent Communications

“…The convolution part automatically extracts the main features of raw radio environment data and the classification part approximates the complicated functions (i.e., mapping relationship) between the extracted features and the best actions. [49] has adopted the CNN model for cooperative spectrum detection. By using the raw primary signal strengths received at multiple detectors as the inputs, the CNN model can learn a better mapping relationship between the raw data and the detection results as well as achieve a better spectrum detection performance compared with the SVM model.…”

Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

“…By learning from spectrum data, machine learning has found rich applications in wireless communications [13], [14]. In particular, deep learning has been applied to learn complex spectrum environments, including spectrum sensing by a CNN [15], spectrum data augmentation by generative adversarial network (GAN) [16], [17], channel estimation by a feedforward neural network (FNN) [18], and jamming/anti-jamming with FNN in training and test times [19]- [21]. Modulation classification has been extensively studied with deep neural networks [1]- [6], where the goal is to classify a given isolated signal to a known modulation type.…”

Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

“…M ACHINE learning provides wireless communications with automated means to learn from and adapt to dynamic spectrum environment that includes a variety of topology, channel, traffic, and interference effects [1], [2], [3]. Examples of machine learning applications in wireless communications include spectrum sensing [4], channel estimation [5], spectrum access [6], power control [7], signal classification [8], and augmentation [9].…”