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4 pages, 174 KiB

Open AccessEditorial

Editorial: Special Issue “Massive MIMO and mm-Wave Communications”

by Gianmarco Romano

Sensors 2022, 22(2), 519; https://doi.org/10.3390/s22020519 - 11 Jan 2022

Cited by 2 | Viewed by 1737

Abstract

Massive multiple-input multiple-output (mMIMO) communication systems and the use of millimeter-wave (mm-Wave) bands represent key technologies that are expected to meet the growing demand of data traffic and the explosion of the number of devices that need to communicate over 5G/6G wireless networks [...] Read more.

Massive multiple-input multiple-output (mMIMO) communication systems and the use of millimeter-wave (mm-Wave) bands represent key technologies that are expected to meet the growing demand of data traffic and the explosion of the number of devices that need to communicate over 5G/6G wireless networks [...] Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

Research

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25 pages, 11453 KiB

Open AccessArticle

Towards Environmental RF-EMF Assessment of mmWave High-Node Density Complex Heterogeneous Environments

by Mikel Celaya-Echarri, Leyre Azpilicueta, Fidel Alejandro Rodríguez-Corbo, Peio Lopez-Iturri, Victoria Ramos, Mohammad Alibakhshikenari, Raed M. Shubair and Francisco Falcone

Sensors 2021, 21(24), 8419; https://doi.org/10.3390/s21248419 - 16 Dec 2021

Cited by 3 | Viewed by 3043

Abstract

The densification of multiple wireless communication systems that coexist nowadays, as well as the 5G new generation cellular systems advent towards the millimeter wave (mmWave) frequency range, give rise to complex context-aware scenarios with high-node density heterogeneous networks. In this work, a radiofrequency [...] Read more.

The densification of multiple wireless communication systems that coexist nowadays, as well as the 5G new generation cellular systems advent towards the millimeter wave (mmWave) frequency range, give rise to complex context-aware scenarios with high-node density heterogeneous networks. In this work, a radiofrequency electromagnetic field (RF-EMF) exposure assessment from an empirical and modeling approach for a large, complex indoor setting with high node density and traffic is presented. For that purpose, an intensive and comprehensive in-depth RF-EMF E-field characterization study is provided in a public library study case, considering dense personal mobile communications (5G FR2 @28 GHz) and wireless 802.11ay (@60 GHz) data access services on the mmWave frequency range. By means of an enhanced in-house deterministic 3D ray launching (3D-RL) simulation tool for RF-EMF exposure assessment, different complex heterogenous scenarios of high complexity are assessed in realistic operation conditions, considering different user distributions and densities. The use of directive antennas and MIMO beamforming techniques, as well as all the corresponding features in terms of radio wave propagation, such as the body shielding effect, dispersive material properties of obstacles, the impact of the distribution of scatterers and the associated electromagnetic propagation phenomena, are considered for simulation. Discussion regarding the contribution and impact of the coexistence of multiple heterogeneous networks and services is presented, verifying compliance with the current established international regulation limits with exposure levels far below the aforementioned limits. Finally, the proposed simulation technique is validated with a complete empirical campaign of measurements, showing good agreement. In consequence, the obtained datasets and simulation estimations, along with the proposed RF-EMF simulation tool, could be a reference approach for the design, deployment and exposure assessment of the current and future wireless communication technologies on the mmWave spectrum, where massive high-node density heterogeneous networks are expected. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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31 pages, 5694 KiB

Open AccessArticle

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

by Jonathan Aguiar Soares, Kayol Soares Mayer, Fernando César Comparsi de Castro and Dalton Soares Arantes

Sensors 2021, 21(24), 8200; https://doi.org/10.3390/s21248200 - 8 Dec 2021

Cited by 9 | Viewed by 3210

Abstract

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its [...] Read more.

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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25 pages, 1550 KiB

Open AccessArticle

Multi-Objective Optimization of Energy Saving and Throughput in Heterogeneous Networks Using Deep Reinforcement Learning

by Kyungho Ryu and Wooseong Kim

Sensors 2021, 21(23), 7925; https://doi.org/10.3390/s21237925 - 27 Nov 2021

Cited by 16 | Viewed by 3413

Abstract

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO₂ emission is mandatory to confront global climate change, we [...] Read more.

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO₂ emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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26 pages, 13114 KiB

Open AccessArticle

5G Network Coverage Planning and Analysis of the Deployment Challenges

by Md Maruf Ahamed and Saleh Faruque

Sensors 2021, 21(19), 6608; https://doi.org/10.3390/s21196608 - 3 Oct 2021

Cited by 44 | Viewed by 12299

Abstract

The 5G cellular network is no longer hype. Mobile network operators (MNO) around the world (e.g., Verizon and AT&T in the USA) started deploying 5G networks in mid-frequency bands (i.e., 3–6 GHz) with existing 4G cellular networks. The mid-frequency band can significantly boost [...] Read more.

The 5G cellular network is no longer hype. Mobile network operators (MNO) around the world (e.g., Verizon and AT&T in the USA) started deploying 5G networks in mid-frequency bands (i.e., 3–6 GHz) with existing 4G cellular networks. The mid-frequency band can significantly boost the existing network performance additional spectrum (i.e., 50 MHz–100 MHz). However, the high-frequency bands (i.e., 24 GHz–100 GHz) can offer a wider spectrum (i.e., 400~800 MHz), which is needed to meet the ever-growing capacity demands, highest bitrates (~20 Gb/s), and lowest latencies. As we move to the higher frequency bands, the free space propagation loss increases significantly, which will limit the individual cell site radius to 100 m for the high-frequency band compared to several kilometers in 4G. Therefore, the MNOs will need to deploy hundreds of new small cells (e.g., 100 m cell radius) compared to one large cell site (e.g., Macrocell with several km in radius) to ensure 100% network coverage for the same area. It will be a big challenge for the MNOs to accurately plan and acquire these massive numbers of new cell site locations to provide uniform 5G coverage. This paper first describes the 5G coverage planning with a traditional three-sector cell. It then proposes an updated cell architecture with six sectors and an advanced antenna system that provides better 5G coverage. Finally, it describes the potential challenges of 5G network deployment with future research directions. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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17 pages, 1234 KiB

Open AccessArticle

Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems

by Won-Seok Lee and Hyoung-Kyu Song

Sensors 2021, 21(16), 5298; https://doi.org/10.3390/s21165298 - 5 Aug 2021

Cited by 3 | Viewed by 2627

Abstract

This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should [...] Read more.

This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should communicate with multiple devices at the same time. To communicate with multiple devices in the same time and frequency slot, high-dimensional channel information should be used to control interferences between the receivers. Therefore, the feedback overhead for the channels of the devices is impractically high. To reduce the overhead, this paper uses common sparsity of channel and nonlinear quantization. To find a common sparse part of a wide frequency band, the proposed system uses minimum mean squared error orthogonal matching pursuit (MMSE-OMP). After the search of the common sparse basis, sparse vectors of subcarriers are searched by using the basis. The sparse vectors are quantized by a nonlinear codebook that is generated by conditional random vector quantization (RVQ). For the conditional RVQ, the Linde–Buzo–Gray (LBG) algorithm is used in conditional vector space. Typically, elements of sparse vectors are sorted according to magnitude by the OMP algorithm. The proposed quantization scheme considers the property for the conditional RVQ. For feedback, indices of the common sparse basis and the quantized sparse vectors are delivered and the channel is recovered at a transmitter for precoding of MU-MIMO. The simulation results show that the proposed scheme achieves lower MMSE for the recovered channel than that of the linear quantization scheme. Furthermore, the transmitter can adopt analog and digital precoding matrix freely by the recovered channel and achieve higher sum rate than that of conventional codebook-based MU-MIMO precoding schemes. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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20 pages, 1579 KiB

Open AccessArticle

On Asymptotic Efficiency of the M₂M₄ Signal-to-Noise Estimator for Deterministic Complex Sinusoids

by Gianmarco Romano

Sensors 2021, 21(15), 4950; https://doi.org/10.3390/s21154950 - 21 Jul 2021

Cited by 1 | Viewed by 1819

Abstract

The moment-based

M_{2} M_{4}

signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that [...] Read more.

The moment-based

M_{2} M_{4}

signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that the proposed estimator is asymptotically efficient and exhibits finite sample super-efficiency for some combinations of signal and noise power. In this paper, we derive the analytical asymptotic performances of the proposed

M_{2} M_{4}

SNR estimator, and we show that, contrary to what it has been concluded by Sekhar and Sreenivas, the proposed estimator is neither (asymptotically) efficient nor super-efficient. We also show that when dealing with deterministic signals, the covariance matrix needed to derive asymptotic performances must be explicitly derived as its known general form for random signals cannot be extended to deterministic signals. Numerical examples are provided whose results confirm the analytical findings. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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11 pages, 856 KiB

Open AccessCommunication

Uplink Sparse Channel Estimation for Hybrid Millimeter Wave Massive MIMO Systems by UTAMP-SBL

by Shuai Hou, Yafeng Wang and Chao Li

Sensors 2021, 21(14), 4760; https://doi.org/10.3390/s21144760 - 12 Jul 2021

Cited by 3 | Viewed by 2407

Abstract

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation [...] Read more.

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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20 pages, 10622 KiB

Open AccessArticle

Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

by Milembolo Miantezila Junior, Bin Guo, Chenjie Zhang and Xuemei Bai

Sensors 2021, 21(11), 3584; https://doi.org/10.3390/s21113584 - 21 May 2021

Cited by 4 | Viewed by 2404

Abstract

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the [...] Read more.

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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19 pages, 564 KiB

Open AccessArticle

Self-Interference Channel Training for Full-Duplex Massive MIMO Systems

by Taehyoung Kim, Kyungsik Min and Sangjoon Park

Sensors 2021, 21(9), 3250; https://doi.org/10.3390/s21093250 - 7 May 2021

Cited by 8 | Viewed by 3509

Abstract

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and [...] Read more.

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and the SI cancellation performance depends significantly on the estimation accuracy of the SI channel. In this study, we proposed a novel partial SI channel training method for minimizing the residual SI power for FD massive multiple-input multiple-output (MIMO) systems. Based on an SI channel training framework under a limited training overhead, using the proposed scheme, the BS estimates only a part of the SI channel vectors, while skipping the channel training for the other remaining SI channel vectors by using their last estimates. With this partial training framework, the proposed scheme finds the optimal partial SI channel training strategy for pilot allocation to minimize the expected residual SI power, considering the time-varying Rician fading channel model for the SI channel. Therefore, the proposed scheme can improve the sum-rate performance compared with other simple partial training schemes for FD massive MIMO systems under a limited training overhead. Numerical results confirm the effectiveness of the proposed scheme for FD massive MIMO systems compared with the full training scheme, as well as other partial training schemes. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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19 pages, 3309 KiB

Open AccessArticle

Machine Learning-Inspired Hybrid Precoding for mmWave MU-MIMO Systems with Domestic Switch Network

by Xiang Li, Yang Huang, Wei Heng and Jing Wu

Sensors 2021, 21(9), 3019; https://doi.org/10.3390/s21093019 - 25 Apr 2021

Cited by 10 | Viewed by 2938

Abstract

Hybrid precoding is an attractive technique in MU-MIMO systems with significantly reduced hardware costs. However, it still requires a complex analog network to connect the RF chains and antennas. In this paper, we develop a novel hybrid precoding structure for the downlink transmission [...] Read more.

Hybrid precoding is an attractive technique in MU-MIMO systems with significantly reduced hardware costs. However, it still requires a complex analog network to connect the RF chains and antennas. In this paper, we develop a novel hybrid precoding structure for the downlink transmission with a compact RF structure. Specifically, the proposed structure relies on domestic connections instead of global connections to link RF chains and antennas. Fixed-degree phase shifters provide candidate signals, and simple on-off switches are used to route the signal to antennas, thus RF adders are no longer required. Baseband zero forcing and block diagonalization are used to cancel interference for single-antenna and multiple-antenna users, respectively. We formulate how to design the RF precoder by optimizing the probability distribution through cross-entropy minimization which originated in machine learning. To optimize the energy efficiency, we use the fractional programming technique and exploit the Dinkelbach method-based framework to optimize the number of active antennas. Simulation results show that proposed algorithms can yield significant advantages under different configurations. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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23 pages, 756 KiB

Open AccessArticle

Switchable Coupled Relays Aid Massive Non-Orthogonal Multiple Access Networks with Transmit Antenna Selection and Energy Harvesting

by Thanh-Nam Tran and Miroslav Voznak

Sensors 2021, 21(4), 1101; https://doi.org/10.3390/s21041101 - 5 Feb 2021

Cited by 5 | Viewed by 2547

Abstract

The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and [...] Read more.

The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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17 pages, 2691 KiB

Open AccessArticle

A Geometry-Based Beamforming Channel Model for UAV mmWave Communications

by Kai Mao, Qiuming Zhu, Maozhong Song, Boyu Hua, Weizhi Zhong and Xijuan Ye

Sensors 2020, 20(23), 6957; https://doi.org/10.3390/s20236957 - 5 Dec 2020

Cited by 21 | Viewed by 3642

Abstract

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing [...] Read more.

Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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17 pages, 6266 KiB

Open AccessLetter

Multi Beam Dielectric Lens Antenna for 5G Base Station

by Farizah Ansarudin, Tharek Abd Rahman, Yoshihide Yamada, Nurul Huda Abd Rahman and Kamilia Kamardin

Sensors 2020, 20(20), 5849; https://doi.org/10.3390/s20205849 - 16 Oct 2020

Cited by 26 | Viewed by 5205

Abstract

In the 5G mobile system, new features such as millimetre wave operation, small cell size and multi beam are requested at base stations. At millimetre wave, the base station antennas become very small in size, which is about 30 cm; thus, dielectric lens [...] Read more.

In the 5G mobile system, new features such as millimetre wave operation, small cell size and multi beam are requested at base stations. At millimetre wave, the base station antennas become very small in size, which is about 30 cm; thus, dielectric lens antennas that have excellent multi beam radiation pattern performance are suitable candidates. For base station application, the lens antennas with small thickness and small curvature are requested for light weight and ease of installation. In this paper, a new lens shaping method for thin and small lens curvature is proposed. In order to develop the thin lens antenna, comparisons of antenna structures with conventional aperture distribution lens and Abbe’s sine lens are made. Moreover, multi beam radiation pattern of three types of lenses are compared. As a result, the thin and small curvature of the proposed lens and an excellent multi beam radiation pattern are ensured. Full article

(This article belongs to the Special Issue Massive MIMO and mm-Wave Communications)

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