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.
Highlights
This paper is an extended version of the conference paper [1]
A description of the Turbo detection algorithm in a multiple-input multiple-output (MIMO) system with a sparsed equivalent channel matrix based on a minimum mean square error (MMSE) detector is considered
One of the directions of the development of new generations of mobile communications is the use of MIMO channels with a large number of antennas
Summary
This paper is an extended version of the conference paper [1]. The study introduces and compares various multiple-input multiple-output (MIMO) channel matrix sparsification methods. Unlike MIMO systems, communication systems with NOMA have the ability to select various templates and user signals matching a specific type of processing at the receiver This makes it possible to provide good energy performance with acceptable implementation complexity. A necessary condition for their use is the ability to represent the channel model in the form of a sparse matrix, as a result of which an individual observation is not a superposition of all symbols at once but only a subset of the symbols In this case, the number of combinations for one observation will be significantly less than the number of combinations for the entire set of symbols. The goal of the paper is the development of a method for transforming a MIMO channel model into a channel with a sparse matrix containing a limited number of nonzero elements in a row or representing a MIMO channel signal in the form of a Markov process.
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