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The need for multiple radio systems in overlapping regions of a factory floor introduces a coexistence problem. The current research challenge is to design and realize radio systems that should be able to achieve a desired quality-of-service (QoS) in harsh, time-varying, coexisting industrial environments. Conventional coexistence solutions attempt to accommodate coexisting systems in a single dimension, mostly in the frequency dimension. The concept of multidimensional electromagnetic (EM) space utilization provides optimal opportunities to achieve coexistence optimized solutions. It can revolutionarily augment the shareable capacity of the resource space and provide optimal coexisting capabilities of radio systems.A software defined radio (SDR)-based cognitive radio (CR) is realized which can exploit the frequency, time, and power dimensions of the EM space to improve coexistence in the 2.4 GHz industrial, scientific, and medical (ISM) band. Furthermore, a conventional hardware defined radio (HDR) and additional simulations are used to test and prove the feasibility of the triple EM space utilization. Joint results of these experiments are presented in this contribution. Additionally, we present a novel computational efficient algorithm to detect cyclic properties of industrial wireless systems.

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Cognitive radios (CR) can sense and detect temporarily available spectral holes for an opportunistic operation to improve the spectral efficiency and coexistence of industrial radio systems. It will be of particular interest for a CR system to apply predictive modeling in order to forecast the behavior of the coexisting environment. A secondary cognitive user shall use preemptive tuning of its operating parameters following the predictive model. However, a considerable challenge is to generate an accurate model and predict efficiently in order to meet strict time related requirements of industrial applications. Such predictive modeling has already gained some attention but real-time results are never reported. In this contribution we investigate real-time aspects of predictive modeling for its application in industrial CR systems.