scholarly journals Multi-Objective Optimization of Massive MIMO 5G Wireless Networks towards Power Consumption, Uplink and Downlink Exposure

2019 ◽  
Vol 9 (22) ◽  
pp. 4974 ◽  
Author(s):  
Michel Matalatala ◽  
Margot Deruyck ◽  
Sergei Shikhantsov ◽  
Emmeric Tanghe ◽  
David Plets ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Electric Field ◽  
Power Consumption ◽  
Massive Mimo ◽  
Rapid Development ◽  
Multiple Input Multiple Output ◽  
Base Stations ◽  
Reference Scenario ◽  
User Coverage ◽  
Input Multiple Output

The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, the positions and power levels of massive MIMO-LTE (Multiple Input Multiple Output-Long Term Evolution) base stations are optimized towards low power consumption, low downlink and uplink electromagnetic exposure and maximal user coverage. A suburban area in Ghent, Belgium has been considered. The results show that the higher the number of BS antenna elements, the fewer number of BSs the massive MIMO network requires. This leads to a decrease of the downlink exposure (−12% for the electric field and −32% for the downlink dose) and an increase of the uplink exposure (+70% for the uplink dose), whereas both downlink and uplink exposure increase with the number of simultaneous served users (+174% for the electric field and +22% for the uplink SAR). The optimal massive MIMO network presenting the better trade-off between the power consumption, the total dose and the user coverage has been obtained with 37 64-antenna BSs. Moreover, the level of the downlink electromagnetic exposure (electric field) of the massive MIMO network is 5 times lower than the 4G reference scenario.

scholarly journals Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks

2020 ◽  
Vol 10 (20) ◽  
pp. 7261
Author(s):  
Wei Zhao ◽  
Wen-Hsing Kuo
Keyword(s):  
Heterogeneous Networks ◽  
Massive Mimo ◽  
Routing Algorithm ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Multihop Wireless ◽  
Wireless Backhaul ◽  
Input Multiple Output

With the development of 5G communication, massive multiple input multiple output (MIMO) technology is getting more and more attention. Massive MIMO uses a large amount of simultaneous transmitting and receiving antennas to reduce power consumption and raise the level of transmission quality. Meanwhile, the diversification of user equipment (UE) in the 5G environment also makes heterogeneous networks (HetNets) more prevalent. HetNets allow UE of different network standards to access small cells, while the base stations of small cells access a macro base station (BS) to form a multihop wireless heterogeneous backhaul network. However, how to effectively combine these two technologies by efficiently allocating the antennas of each BS during the route construction process of heterogeneous wireless backhaul networks is still an important issue that is yet to be solved. In this paper, we propose an algorithm called preallocated sequential routing (PSR). Based on the links’ channel conditions and the available antennas and location of BSs, it builds a wireless heterogeneous network backhaul topology and adjusts each link’s transmitting and receiving antennas to maximize total utility. Simulation results showed that the proposed algorithm significantly improved the overall utility and the utility of the outer area of heterogeneous networks.

scholarly journals Massive MIMO wireless networks: an overview

2021 ◽  
Author(s):  
Noha Hassan ◽  
Xavier N. Fernando
Keyword(s):  
Wireless Networks ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Heterogeneous Systems ◽  
Mimo Networks ◽  
Wireless Broadband ◽  
Input Multiple Output ◽  
Processing Techniques ◽  
Linear Algorithms

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. Keywords: 5G wireless networks; massive MIMO; linear precoding; encoding; channel estimation; pilot contamination; beamforming; HetNets

scholarly journals Massive MIMO wireless networks: an overview

2021 ◽  
Author(s):  
Noha Hassan ◽  
Xavier N. Fernando
Keyword(s):  
Wireless Networks ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Heterogeneous Systems ◽  
Mimo Networks ◽  
Wireless Broadband ◽  
Input Multiple Output ◽  
Processing Techniques ◽  
Linear Algorithms

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. Keywords: 5G wireless networks; massive MIMO; linear precoding; encoding; channel estimation; pilot contamination; beamforming; HetNets

scholarly journals On the Energy Efficiency of Millimeter Wave Massive MIMO Based on Hybrid Architecture

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2227 ◽  
Author(s):  
Peerapong Uthansakul ◽  
Arfat Ahmad Khan
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Cellular Systems ◽  
Hybrid Architecture ◽  
Radiated Energy ◽  
Multiple Input ◽  
Input Multiple Output

Millimeter Wave (mmWave) Massive Multiple Input Multiple Output (MIMO) has been a promising candidate for the current and next generation of cellular networks. The hybrid analogue/digital precoding will be a crucial ingredient in the mmWave cellular systems to reduce the number of Radio Frequency (RF) chains along with the corresponding energy and power consumption of the systems. In this paper, we aim to improve the energy efficiency of mmWave Massive MIMO by using a combination of high dimension analogue precoder and low dimension digital precoder. The spectral efficiency and the corresponding transmitted and consumed power of the mmWave Massive MIMO is formulated by taking all the consumed power from the transmitting side to receiving end into account. We propose the Power Controlled Energy Maximization (PCEM) algorithm in this paper, and the proposed algorithm works by controlling the transmission power to balance the improved radiated energy efficiency and the increased power consumption for a given number of transceiver chains. The simulation and analytical results show that the proposed algorithm performs better than the reference algorithms by maximizing the overall energy efficiency of the system without much complexity.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

PROBLEM STATEMENT OF JOINT ANTENNA SELECTION AND PRECODING IN MASSIVE MIMO COMMUNICATION SYSTEMS

2021 ◽  
Author(s):  
В.Б. КРЕЙНДЕЛИН ◽  
М.В. ГОЛУБЕВ
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Legal Restrictions ◽  
Multiple Transmission

Совместный с прекодингом автовыбор антенн на приемной и передающей стороне - одно из перспективных направлений исследований для реализации технологий Multiple Transmission and Reception Points (Multi-TRP, множество точек передачи и приема) в системах со многими передающими и приемными антеннами Massive MIMO (Multiple-Input-Multiple-Output), которые активно развиваются в стандарте 5G. Проанализированы законодательные ограничения, влияющие на применимость технологий Massive MIMO, и специфика реализации разрабатываемого алгоритма в миллиметровомдиапа -зоне длин волн. Рассмотрены алгоритмы формирования матриц автовыбора антенн как на передающей, так и на приемной стороне. Сформулирована строгая математическая постановка задачи для двух критериев работы алгоритма: максимизация взаимной информации и минимизация среднеквадратичной ошибки. Joint precoding and antenna selection both on transmitter and receiver sides is one of the promising research areas for evolving toward the Multiple Transmission and Reception Points (Multi-TRP) concept in Massive MIMO systems. This technology is under active development in the coming 5G 3GPP releases. We analyze legal restrictions for the implementation of 5G Massive MIMO technologies in Russia and the specifics of the implementation of the developed algorithm in the millimeter wavelength range. Algorithms of antenna auto-selection matrices formation on both transmitting and receiving sides are considered. Two criteria are used for joint antenna selection and precoding: maximizing mutual information and minimizing mean square error.

scholarly journals The Downlink Performance for Cell-Free Massive MIMO with Instantaneous CSI in Slowly Time-Varying Channels

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1552
Author(s):  
Tongzhou Han ◽  
Danfeng Zhao
Keyword(s):  
Power Control ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Time Varying ◽  
Input Multiple Output ◽  
Efficiency Performance ◽  
A Cell ◽  
Time Varying Channel

In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.

scholarly journals Application of cell-free massive MIMO in 5G and beyond 5G wireless networks: a survey

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
Hope Ikoghene Obakhena ◽  
Agbotiname Lucky Imoize ◽  
Francis Ifeanyi Anyasi ◽  
K. V. N. Kavitha
Keyword(s):  
Wireless Networks ◽  
Massive Mimo ◽  
System Modeling ◽  
High Capacity ◽  
High Energy ◽  
Lessons Learned ◽  
Base Stations ◽  
Innovative Technology ◽  
Processing Unit ◽  
Central Processing

AbstractIn recent times, the rapid growth in mobile subscriptions and the associated demand for high data rates fuels the need for a robust wireless network design to meet the required capacity and coverage. Deploying massive numbers of cellular base stations (BSs) over a geographic area to fulfill high-capacity demands and broad network coverage is quite challenging due to inter-cell interference and significant rate variations. Cell-free massive MIMO (CF-mMIMO), a key enabler for 5G and 6G wireless networks, has been identified as an innovative technology to address this problem. In CF-mMIMO, many irregularly scattered single access points (APs) are linked to a central processing unit (CPU) via a backhaul network that coherently serves a limited number of mobile stations (MSs) to achieve high energy efficiency (EE) and spectral gains. This paper presents key areas of applications of CF-mMIMO in the ubiquitous 5G, and the envisioned 6G wireless networks. First, a foundational background on massive MIMO solutions-cellular massive MIMO, network MIMO, and CF-mMIMO is presented, focusing on the application areas and associated challenges. Additionally, CF-mMIMO architectures, design considerations, and system modeling are discussed extensively. Furthermore, the key areas of application of CF-mMIMO such as simultaneous wireless information and power transfer (SWIPT), channel hardening, hardware efficiency, power control, non-orthogonal multiple access (NOMA), spectral efficiency (SE), and EE are discussed exhaustively. Finally, the research directions, open issues, and lessons learned to stimulate cutting-edge research in this emerging domain of wireless communications are highlighted.

Sign in / Sign up

Export Citation Format

Share Document