Survey of Large-Scale MIMO Systems

Zheng, Kan; Zhao, Long; Mei, Jie; Shao, Bin; Wang, Xiang; Hanzo, Lajos

doi:10.1109/comst.2015.2425294

Cited by 282 publications

(181 citation statements)

References 104 publications

(175 reference statements)

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“…There are many benefits of using massive mmWave multi-antenna systems, and these include the following [8]:…”

Section: Beam Forming and Multiplexing With Millimeter Wavesmentioning

confidence: 99%

“…This contributes to interference cancellation, and then data is passed to the scheduled downlink users cooperatively from the BSs (sometimes using beamforming) [15]. In contrary, massive MIMO systems have substantial (M) number of antennas per BS, simultaneously serving a much smaller (K) number of users [8]. Table 2 compares cooperating network MIMO and conventional massive MIMO approaches.…”

Section: Introductionmentioning

confidence: 99%

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Massive MIMO Wireless Networks: An Overview

Hassan

Fernando

2017

Electronics

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Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems.

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“…There are many benefits of using massive mmWave multi-antenna systems, and these include the following [8]:…”

Section: Beam Forming and Multiplexing With Millimeter Wavesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Massive MIMO Wireless Networks: An Overview

Hassan

Fernando

2017

Electronics

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“…Additionally, the work [16] maximizes the EE by joint power and subcarrier allocation for a single-cell OFDMA network with multiple relays. An energy-efficient carrier aggregation scheme is considered in [17], while large-scale MIMO based systems are discussed in [18]. Similarly, the designs of [19]- [21] are either for singlecell or fixed networks.…”

Section: A Related Workmentioning

confidence: 99%

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

Zhao

Chen

Zhao

et al. 2017

IEEE Trans. Wireless Commun.

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Abstract-In this paper, the energy-spectral efficiency (ESE) benefiting from the joint optimization of coordinated multipoint (CoMP) transmission and base station (BS) deployment are evaluated in the context of dense large-scale cellular network. We first derive a closed-form network ESE expression for a largescale CoMP-enhanced network, which allows us to quantify the influence of key network parameters on the achievable network ESE, including the BS density and the cooperation activation probability characterized by a CoMP activation factor as well as the users' behaviors, such as their geographical mobile-traffic intensity and average user rate. With the aid of this tractable ESE expression and for a given BS density, we next formulate a cellular-scenario-aware CoMP activation optimization problem whilst considering the users' outage probability as constraints to maximize the network's ESE. We then further jointly optimize the CoMP activation factor and the BS density to maximize the network ESE, again under the constraint of the users' outage probability. Our simulation results confirm the accuracy of our analysis and verify the impact of several key parameters on the network ESE. Finally, the ESE improvement of our proposed strategies is evaluated under diverse scenarios, which provides valuable insight into joint CoMP and BS deployment optimization in dense large-scale cellular networks.

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“…Multiple-Input Multiple-Output (MIMO) techniques are characterised by an increased capacity as well as improved performance and hence the research in the MIMO design has seen a great interest for the last few years [1][2][3]. Recently, large-scale MIMOs have been proposed in order to scale up the MIMO gains [4,5], where, for example a large number of antennas can be implemented at the base station for supporting a large number of users.…”

Section: Introductionmentioning

confidence: 99%

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Halak

El‐Hajjar

Hassanein

2018

JSAN

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Multiple-Input Multiple-Output (MIMO) systems are characterised by increased capacity and improved performance compared to the single-input single-output (SISO) systems. One of the main challenge in the design of MIMO systems is the detection of the transmitted signals due to the interference caused by the multiple simultaneously transmitted symbols from the multiple transmit antennas. Several detection techniques have been proposed in the literature in order to reduce the detection complexity, while maintaining the required quality of service. Among these low-complexity techniques is the Lattice Reduction (LR), which can provide good performance and significantly lower complexity compared to Maximum Likelihood (ML) detector. In this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better Bit Error Ratio (BER) performance and lower complexity than the conventional Lenstra, Lanstra, and Lovasz (LLL) LR-aided detection. Additionally, we propose a hardware implementation for the ELR-aided K-Best detector for a MIMO system equipped with four transmit and four receive antennas. The ELR-aided K-Best detector requires an extra 18% increase in power consumption and an extra 20% in area overhead compared to a regular K-Best detector dispensing with ELR, where this increase in the hardware requirements is needed in order to achieve a 2 dB performance improvement at a bit error rate of 10 −5 .

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Survey of Large-Scale MIMO Systems

Cited by 282 publications

References 104 publications

Massive MIMO Wireless Networks: An Overview

Massive MIMO Wireless Networks: An Overview

Joint Energy-Spectral-Efficiency Optimization of CoMP and BS Deployment in Dense Large-Scale Cellular Networks

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

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