Efficient Iterative Massive MIMO Detectors Based on Iterative Matrix Inversion Methods

Efficient Iterative Massive MIMO Detectors Based on Iterative Matrix Inversion Methods

Mahmoud Albreem (A'Sharqiyah University, Oman)
DOI: 10.4018/978-1-7998-4610-9.ch009
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Abstract

Massive multiple-input multiple-output (MIMO) is a key technology in fifth generation (5G) communication systems. Although the maximum likelihood (ML) obtains an optimal performance, it is prohibited in realization because of its high computational complexity. Linear detectors are an alternative solution, but they contain a matrix inversion which is not hardware friendly. Several methods have been proposed to approximate or to avoid the computation of exact matrix inversion. This chapter garners those methods and study their applicability in massive MIMO system so that a generalist in communication systems can differentiate between different algorithms from a wide range of solutions. This chapter presents the performance-complexity profile of a detector based on the Neuamnn-series (NS), Newton iteration (NI), successive over relaxation (SOR), Gauss-Seidel (GS), Jacobi (JA), Richardson (RI), optimized coordinate descent (OCD), and conjugate-gradient (CG) methods in 8×64, 16×64, and 32×64 MIMO sizes, and modulation scheme is 64QAM.
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Introduction

The number of mobile users is significantly increasing every year. It is expected to reach 12.3 billion mobile devices in 2022 while it was 7.3 billion devices in 2014. It is noteworthy that nearly 650 million devices and connections were added in 2017. In addition, the fifth generation (5G) wireless communication is anticipated to reach 1.5 billion subscriptions in 2024. It is also remarkable that the mobile data traffic is dramatically increasing from 3.7 Exabytes in 2015 to 21.7 Exabytes in 2019, and it is forecasted to reach twenty-fold in 2022.

It is foreseeable that efficient technologies are playing a crucial impact in 5G and beyond to meet the user demands in both the performance and the quality of services (QoS) (Araujo, Maksymyuk et al. 2016). The 5G deploys the device-to-device (D2D) communications, the ultra-dense networks (UDNs), the millimeter wave (mmWave), the internet of things (IoT), and the massive multiple-input multiple-output (MIMO) (Albreem 2015). The D2D communication networks facilitate data exchange among devices without the presence of the base station (BS) or access point where data can be transferred directly among connected devices without interaction with external devices. The UDNs involve a host of violently deployed small cells where UDNs are needed due to their distinguished advantages on escalating the spectrum reuse and lessening the path loss. In the mmWave, the spectrum from 30GHz to 300GHz will be utilized to provide a large bandwidth, and thus, data rate increases up to multi Gigabits per second (Albreem, El-Saleh et al. 2019). The IoT includes countless physical devices embedded with sensors, actuators and radio frequency identification (RFID) tags and enables their cooperation and data exchange through IoT protocols (Albreem, El-Saleh et al. 2017, Albreem, Juntti et al. 2019).

The conventional small-scale MIMO technology had been deployed since the third generation (3G) wireless networks to improve the performance of wireless transceivers. Massive MIMO is an extension of the small-scale MIMO and it is a promising candidate to achieve a high data rate, low latency, high energy and spectral efficiencies. In massive MIMO, a large number of antennas are deployed at the BS to serve a large number of mobile user terminals at the same frequency band where the number of mobile user terminals is noticeably smaller than the number of BS antennas (Björnson, Larsson et al. 2016). A precise and instantaneous channel state information (CSI) is required at the BS to perform detection in the uplink. In the scenario of a small number of active users and rich scattering channels, matched filter (MF) detector achieves a satisfactory performance. On the other hand, advanced detectors are needed in the scenario of spatially correlated channels. Although the maximum likelihood (ML) detector attains the optimum performance, it is prohibited in realization due to its high computational complexity (Larsson, Edfors et al. 2014).

In literature, a plethora of massive MIMO detection algorithms had been proposed to achieve a satisfactory balance between the performance and the complexity. Nonlinear detectors are not competitive in realization because they require a decomposition, i.e., QR or Cholesky decompositions (Ylinen, Burian et al. 2004), which increases the computational complexity. On the other hand, detectors based on linear methods are relatively simple and easy to implement but they suffer from a significant performance loss in highly loaded and ill-conditioned environments. They also contain a matrix inversion which is not hardware-friendly (Albreem, Juntti et al. 2019).

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