Machine Learning Based Iterative Detection and Multi-Interference Cancellation for Cognitive IoT

In this letter, a machine learning approach is proposed to cancel the interference from multiple sources in the concurrent spectrum access (CSA) model of the cognitive Internet of Things (C-IoT). We assume that the C-IoT system is non-cooperative with the primary user (PU) system, and has little knowledge on the interference caused by PU transmitters. In order to recover the C-IoT signal under power strong multi-interference, we employ an iterative receiver consisting of a linear estimation module, and a demodulation-and-decoding module, as well as a clustering module by following the idea of iterative detection and interference cancellation. Conventional clustering algorithms, such as the K-means algorithm and the Gaussian mixture model (GMM) based expectation maximization (EM) algorithm, can potentially be used to realize the clustering module. However, their performance is poor under the existence of multiple interferers since the performance of these algorithms heavily relies on the initialization of cluster centroids. To address this problem, we use the affinity propagation (AP) algorithm, which works well without the initialization of cluster centroids, to realize the clustering module. Numerical results demonstrate that the AP algorithm has much better performance than the K-means and GMM-EM algorithms in handling multi-interference.