Probabilistic Stone’s Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems

Abstract

By the increasing growth of the Internet of Things (IoT) which provides interconnection and communications between electronic devices and corresponding sensors, a large volume of data is exchanged by multi-input multi-output (MIMO) telecommunication systems. In the case of IoT, reducing the data volume by removing the data redundancy results in more green communication by less power consumption for data transmission and less required storage memory. An approach to avoid the pilot data thus having less redundant data is using blind techniques. This research work presents an improvement to Stone’s blind source separation (BSS) precision, robustness, and computation load and its application to blind MIMO IoT interference channel estimation, multi-nodes IoT data detection, separation and identification in a MIMO-OFDM IoT network. Stone’s BSS is based on complexity conjecture indicating the independent sources have higher predictability than the mixtures. The presented improvement to Stone’s BSS is by a probabilistic modification to the short-term predictability merit by acquiring the prediction coefficients proportional to probability weights which follow a super Gaussian distribution assumption for sources. The probabilistic modification to Stone’s BSS (P-Stone) makes it maximally compatible with a pre-specified probability distribution model, and thereof the signal recovery is not only due to predictability maximization, but it is also inherent to increasing non-gaussianity which results in more independent recoveries, and less dependent on serial dependency of sources. Despite the Stone’s BSS, the proposed merit function does not need any long-term predictor; thus, it achieves around fifty times lower complexity load using just short-term predictors. The superiority of P-Stone to Stone BSS, AMUSE, and SOBI as well-known second-order techniques has been statistically evaluated and clarified through the experiments over MIMO-IoT networks of different combinations. As well, the comparative analysis over multimedia mixtures of music, speech, and images demonstrates its efficiency dominance.