A Primer on MIMO Detection Algorithms for 5G Communication Network

In the recent past, demand for large use of mobile data has increased tremendously due to the proliferation of hand held devices which allows millions of people access to video streaming, VOIP and other internet related usage including machine to machine (M2M) communication. One of the anticipated attribute of the fifth generation (5G) network is its ability to meet this humongous data rate requirement in the order of 10s Gbps. A particular promising technology that can provide this desired performance if used in the 5G network is the massive multiple-input, multiple-output otherwise called the Massive MIMO. The use of massive MIMO in 5G cellular network where data rate of the order of 100x that of the current state of the art LTE-A is expected and high spectral efficiency with very low latency and low energy consumption, present a challenge in symbol/signal detection and parameter estimation as a result of the high dimension of the antenna elements required. One of the major bottlenecks in achieving the benefits of such massive MIMO systems is the problem of achieving detectors with realistic low complexity for such huge systems. We therefore review various MIMO detection algorithms aiming for low computational complexity with high performance and that scales well with increase in transmit antennas suitable for massive MIMO systems. We evaluate detection algorithms for small and medium dimension MIMO as well as a combination of some of them in order to achieve our above objectives. The review shows no single one detector can be said to be ideal for massive MIMO and that the low complexity with optimal performance detector suitable for 5G massive MIMO system is still an open research issue. A comprehensive review of such detection algorithms for massive MIMO was not presented in the literature which was achieved in this work

Improved QR Decomposition-Based SIC Detection Algorithm for MIMO System

Abstract: Multiple-Input Multiple-Output (MIMO) systems can increase wireless communication system capacity enormously. Maximum Likelihood (ML) detection algorithm is the optimum detection algorithm which computational complexity growing exponentially with the number of transmit-antennas, which makes it difficult to use it in practice system. Ordered Successive Interference Cancellation (SIC) algorithm with lower computing complexity will suffer from error propagation when an incorrect symbol is selected in the early layers. An MIMO signal detection algorithm based on Improved Sorted-QR decomposition (ISQR) is presented in this study. According to the rule of SNR, ISQR can obtain the optimum detection order with less calculation. Based on ISQR an improved detection algorithm is proposed which providing 2 adjustable parameters. Trade-off between performance and complexity can be selected properly by setting the 2 parameters at different values. Simulation experiments are given under the multiple scattering wireless communication environments and the simulation experiment results show the validity of proposed algorithm

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