On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Low-complexity detector for very large and massive MIMO transmission

International audienceMaximum-Likelihood (ML) joint detection has been proposed as an optimal strategy that detects simultaneously the transmitted signals. In very large multiple-input-multiple output (MIMO) systems, the ML detector becomes intractable due the computational cost that increases exponentially with the antenna dimensions. In this paper, we propose a relaxed ML detector based on an iterative decoding strategy that reduces the computational cost. We exploit the fact that the transmit constellation is discrete, and remodel the channel as a MIMO channel with sparse input belonging to the binary set {0, 1}. The sparsity property allows us to relax the ML problem as a quadratic minimization under linear and l1-norm constraint. We then prove the equivalence of the relaxed problem to a convex optimization problem solvable in polynomial time. Simulation results illustrate the efficiency of the low-complexity proposed detector compared to other existing ones in very large and massive MIMO context

Efficient Iterative Detection Based on Conjugate Gradient and Successive Over-Relaxation Methods for Uplink Massive MIMO Systems, Journal of Telecommunications and Information Technology, 2023, nr 2

Being a crucial aspect of fifth-generation (5G) mobile communications systems, massively multiple-input multipleoutput (mMIMO) architectures are expected to help achieve the highest key performance indicators. However, the huge numbers of antennas used in such systems make it difficult to determine the inversion of the signal channel matrix relied upon by several detection methods, hence posing a problem with accurate estimation of the symbols sent. In this paper, conjugate gradient (CG) and successive over-relaxation (SOR) methods are selected to construct a new iterative approach that avoids the matrix inversion computation issue. This suggested approach for uplink mMIMO detection is based on a joint cascade structure of both iterative methods. The CG method is first applied and adjusted for the initial solution, followed by the SOR method in the final iterations for terminal computations, resulting in an algorithm with robust performance and low computational complexity. Furthermore, the new hybrid scheme operates based on the relaxation parameter, whose value has a great impact on error performance and, whose optimal determination is necessary. Numerical simulations reveal that the proposed scheme is capable of significantly improving signal detection accuracy with minimum complexity. The simulation results indicated that the proposed detector outperforms CG and SOR detectors, achieves close to optimal performance, requires fewer iterations, and reduces complexit