Equivalent MIMO Channel Matrix Sparsification for Enhancement of Sensor Capabilities

Abstract

One of the development directions of new-generation mobile communications is using multiple-input multiple-output (MIMO) channels with a large number of antennas. This requires the development and utilization of new approaches to signal detection in MIMO channels, since the difference in the energy efficiency and the complexity between the optimal maximum likelihood algorithm and simpler linear algorithms become very large. The goal of the presented study is the development of a method for transforming a MIMO channel into a model based on a sparse matrix with a limited number of non-zero elements in a row. It was shown that the MIMO channel can be represented in the form of a Markov process. Hence, it becomes possible to use simple iterative MIMO demodulation algorithms such as message-passing algorithms (MPAs) and Turbo.

Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication

“…Statistical parameters of the channel, such as root mean square delay spread (RMS-DS), K factor, and shadowing are studied in [ 18 , 19 ]. The efficient method to approximate the MIMO channel model by sparse matrices with an equivalent Markov process is introduced and thoroughly analyzed in [ 20 ]. A shared aperture two-element MIMO antenna design for 5G standards is presented in [ 21 ], which uses the same radiating structure to cover both the sub-6 GHz and millimeter-wave bands.…”