Massive MIMO Channel Models: A Survey

The exponential traffic growth of wireless communication networks gives rise to both the insufficient network capacity and excessive carbon emissions. Massive multiple-input multiple-output (MIMO) can improve the spectrum efficiency (SE) together with the energy efficiency (EE) and has been regarded as a promising technique for the next generation wireless communication networks. Channel model reflects the propagation characteristics of signals in radio environments and is very essential for evaluating the performances of wireless communication systems. The purpose of this paper is to investigate the state of the art in channel models of massive MIMO. First, the antenna array configurations are presented and classified, which directly affect the channel models and system performance. Then, measurement results are given in order to reflect the main properties of massive MIMO channels. Based on these properties, the channel models of massive MIMO are studied with different antenna array configurations, which can be used for both theoretical analysis and practical evaluation

On the Sparsity and Aperiodicity of a Base Station Antenna Array in a Downlink MU-MIMO Scenario

An application study into irregular sparse arrays (ISAs) is proposed to function as base station antennas (BSAs) in a mm-wave multi-user multiple-input multiple-output (MU-MIMO) system. The results show that the sum rate capacity of ISAs can be increased relative to regularly-spaced BSA arrays with half a wavelength element separation, especially for a high number of users. This is due to the narrower beams formed by the larger antenna apertures of sparse arrays. Furthermore, the aperiodic distribution of antenna elements alleviates the problem of grating lobes in sparse arrays and is seen to improve the average power consumption of power amplifiers at the same time

Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays

Massive multiple-input multiple-output (MIMO) systems, which utilize a large number of antennas at the base station, are expected to enhance network throughput by enabling improved multiuser MIMO techniques. To deploy many antennas in reasonable form factors, base stations are expected to employ antenna arrays in both horizontal and vertical dimensions, which is known as full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform planar array (UPA), where antennas are placed in a grid pattern. To exploit the full benefit of massive MIMO in frequency division duplexing (FDD), the downlink channel state information (CSI) should be estimated, quantized, and fed back from the receiver to the transmitter. However, it is difficult to accurately quantize the channel in a computationally efficient manner due to the high dimensionality of the massive MIMO channel. In this paper, we develop both narrowband and wideband CSI quantizers for FD-MIMO taking the properties of realistic channels and the UPA into consideration. To improve quantization quality, we focus on not only quantizing dominant radio paths in the channel, but also combining the quantized beams. We also develop a hierarchical beam search approach, which scans both vertical and horizontal domains jointly with moderate computational complexity. Numerical simulations verify that the performance of the proposed quantizers is better than that of previous CSI quantization techniques.Comment: 15 pages, 6 figure

Atomic Norm decomposition for sparse model reconstruction applied to positioning and wireless communications

This thesis explores the recovery of sparse signals, arising in the wireless communication and radar system fields, via atomic norm decomposition. Particularly, we focus on compressed sensing gridless methodologies, which avoid the always existing error due to the discretization of a continuous space in on-grid methods. We define the sparse signal by means of a linear combination of so called atoms defined in a continuous parametrical atom set with infinite cardinality. Those atoms are fully characterized by a multi-dimensional parameter containing very relevant information about the application scenario itself. Also, the number of composite atoms is much lower than the dimension of the problem, which yields sparsity. We address a gridless optimization solution enforcing sparsity via atomic norm minimization to extract the parameters that characterize the atom from an observed measurement of the model, which enables model recovery. We also study a machine learning approach to estimate the number of composite atoms that construct the model, given that in certain scenarios this number is unknown. The applications studied in the thesis lay on the field of wireless communications, particularly on MIMO mmWave channels, which due to their natural properties can be modeled as sparse. We apply the proposed methods to positioning in automotive pulse radar working in the mmWave range, where we extract relevant information such as angle of arrival (AoA), distance and velocity from the received echoes of objects or targets. Next we study the design of a hybrid precoder for mmWave channels which allows the reduction of hardware cost in the system by minimizing as much as possible the number of required RF chains. Last, we explore full channel estimation by finding the angular parameters that model the channel. For all the applications we provide a numerical analysis where we compare our proposed method with state-of-the-art techniques, showing that our proposal outperforms the alternative methods.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Pablo Martínez Olmos.- Vocal: David Luengo Garcí

State-of-the-art assessment of 5G mmWave communications

Deliverable D2.1 del proyecto 5GWirelessMain objective of the European 5Gwireless project, which is part of the H2020 Marie Slodowska- Curie ITN (Innovative Training Networks) program resides in the training and involvement of young researchers in the elaboration of future mobile communication networks, focusing on innovative wireless technologies, heterogeneous network architectures, new topologies (including ultra-dense deployments), and appropriate tools. The present Document D2.1 is the first deliverable of Work- Package 2 (WP2) that is specifically devoted to the modeling of the millimeter-wave (mmWave) propagation channels, and development of appropriate mmWave beamforming and signal processing techniques. Deliver D2.1 gives a state-of-the-art on the mmWave channel measurement, characterization and modeling; existing antenna array technologies, channel estimation and precoding algorithms; proposed deployment and networking techniques; some performance studies; as well as a review on the evaluation and analysis toolsPostprint (published version

Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to co-design antennas with signal processing. The main open research challenges are identified at the end.Comment: Submitted to Proceedings of the IEEE, 36 pages, 23 figure

Antenna Systems

This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems

Optimum polarization configuration of planar circular patch MIMO antenna

Multiple Input Multiple Output (MIMO) is a key technology that plays an important role in achieving the 5G requirement due to its capability to increase channel capacity. However, the increase of channel capacity is influenced by several aspects such as mutual coupling. Many methods are done to decrease the mutual coupling effect such as polarization arrangement of the MIMO antennas. This study on the polarization arrangement of a circular patch MIMO antenna at 3.5 GHz was performed. Four elements of the MIMO antenna are arranged with several polarization configurations both in Co-Polarization and Cross-Polarization. Both simulation and measurement results showed that MIMO with Co-Polarization has a slightly wider bandwidth equal to 295.25 MHz compared to Cross-Polarization with a bandwidth of 274.63 MHz, due to better return loss performed by the former. However, from the mutual coupling perspective, it is observed that MIMO with Cross-Polarization can reduce the mutual coupling from -17.6676dB into -22.462 dB compared to Co-Polarization with the same element distance.Multiple Input Multiple Output (MIMO) merupakan teknologi kunci yang memiliki peran penting dalam pencapaian kebutuhan jaringan 5G karena kemampuannya untuk meningkatkan kapasitas kanal. Tetapi, peningkatan kapasitas kanal dipengaruhi oleh beberapa aspek yaitu salah satunya mutual coupling. Beberapa metode yang dilakukan untuk mengurangi efek mutual coupling antara lain susunan polarisasi dari antena MIMO. Penelitian kali ini berfokus kepada susunan polarisasi dari antenna MIMO berbentuk circular patch  dengan frekuensi 3.5 GHz. Empat elemen MIMO disusun dengan beberapa konfigurasi polarisasi yaitu Co-Polarization dan Cross-Polarization. Hasil simulasi dan pengukuran menunjukan bahwa MIMO dengan Co-Polarization memiliki pita yang lebih lebar sebesar 295.25 MHz dibandingkan dengan Cross-Polarization dengan lebar pita hanya 274.63 MHz, karena return loss yang lebih baik pada Co-Polarization. Akan tetapi, dari perspektif mutual couping, dapat diamati bahwa MIMO dengan Cross-Polarization dapat mengurangi mutual coupling dari -17.667 dB ke -22.462 dB dibandingkan dengan Co-Polarization dengan jarak elemen yang sam