Nonlinear MIMO for Industrial Internet of Things in Cyber-Physical Systems

Massive multiple-input multiple-output (MIMO) wireless communication technology with the characteristics of hyperconnectivity is an ideal channel to connect the industrial Internet of Things (IIoT) and the cyber-physical system. It provides stable and reliable connectivity from the data center to distributed user terminals and the IIoT. However, traditional massive MIMO suffers from high power consumption and fabrication cost. The design of energy-efficient massive MIMO technology is essential for larger scale industrial deployments. In this article, we design three types of nonlinear RF chain structures, which not only reduce the power consumption of massive MIMO systems but also save fabrication costs. Information theoretic analysis demonstrates the power efficiency performance of our nonlinear system design. Our nonlinear MIMO system designs can increase the power efficiency by up to 2.3 times compared with the traditional MIMO system. We have demonstrated that our systems can achieve the same uplink rate as traditional MIMO by increasing the number of receiving antennas but with less overall power consumption. We also proposed an algorithm to overcome the problem of low computational efficiency due to high-dimensional integration when calculating the uplink achievable rate of nonlinear MIMO. Moreover, we reveal that when the skew-normal distribution is used as signaling, the nonlinear MIMO systems can achieve better performance than the Gaussian distribution.

Automated summary

Massive MIMO technology provides stable and reliable connectivity for the industrial Internet of Things, but traditional designs suffer from high power consumption and fabrication costs. This study's nonlinear MIMO system designs improve power efficiency and achieve the same uplink rate as traditional MIMO by increasing the number of receiving antennas, reducing overall power consumption. Using skew-normal distribution as signaling, nonlinear MIMO systems outperform Gaussian distribution in performance.