Joint precoding and antenna selection in massive mimo systems

This thesis presents an overview of massive multiple-input multiple-output (MIMO) systems and proposes new algorithms to jointly precode and select the antennas. Massive MIMO is a new technology, which is candidate for comprising the fifth-generation (5G) of mobile cellular systems. This technology employs a huge amount of antennas at the base station and can reach high data rates under favorable, or asymptotically favorable, propagation conditions, while using simple linear processing. However, massive MIMO systems have some drawbacks, such as the high cost related to the base stations. A way to deal with this issue is to employ antenna selection algorithms at the base stations. These algorithms reduce the number of active antennas, decreasing the deployment and maintenance costs related to the base stations. Moreover, this thesis also describes a class of nonlinear precoders that are rarely addressed in the literature; these techniques are able to generate precoded sparse signals in order to achieve joint precoding and antenna selection. This thesis proposes two precoders belonging to this class, where the number of selected antennas is controlled by a design parameter. Simulation results show that the proposed precoders reach a lower bit-error rate than the classical antenna selection algorithms. Furthermore, simulation results show that the proposed precoders present a linear relation between the aforementioned design parameter that controls the signals’ sparsity and the number of selected antennas. Such relation is invariant to the number of base station’s antennas and the number of terminals served by this base station.Esta dissertação apresenta uma visão geral sobre MIMO (do termo em inglês, multiple-input multiple-output) massivo e propõe novos algoritmos que permitem a pré-codificacão de sinais e a seleção de antenas de forma simultânea. MIMO massivo é uma nova tecnologia candidata para compor a quinta geração (5G) dos sistemas celulares. Essa tecnologia utiliza uma quantidade muito grande de antenas na estação-base e, sob condições de propagação favorável ou assintoticamente favorável, pode alcançar taxas de transmissão elevadas, ainda que utilizando um simples processamento linear. Entretanto, os sistemas MIMO massivo apresentam algumas desvantagens, como por exemplo, o alto custo de implementação das estações-bases. Uma maneira de lidar com esse problema é utilizar algoritmos de seleção de antenas na estação-base. Com esses algoritmos é possível reduzir o número de antenas ativas e consequentemente reduzir o custo nas estações-bases. Essa dissertação também apresenta uma classe pouco estudada de pré-codificadores não-lineares que buscam sinais pré-codificados esparsos para realizar a seleção de antenas conjuntamente com a pré-codificação. Além disso, este trabalho propõem dois novos pré-codificadores pertencentes a essa classe, para os quais o número de antenas selecionadas é controlado por um parâmetro de projeto. Resultados de simulações mostram que os pré-codificadores propostos conseguem uma BER (do termo em inglês, bit-error rate) menor que os algoritmos clássicos usados para selecionar antenas. Além disso, resultados de simulações mostram que os pré-codificadores propostos apresentam uma relação linear com o parâmetro de projeto que controla a quantidade de antenas selecionadas; tal relação independe do número de antenas na estação-base e do número de terminais servidos por essa estação

Geringer RF-Komplexität Massive MIMO Systemen: Antennenselektion und Hybrid Analog-Digital Strahlformung

Wireless data traffic has been increased dramatically in the last decades, and will continue to increase in the future. As a consequence, the infrastructure of wireless communication systems needs to advance on the data capacity. Massive Multiple-Input Multiple-Output (MIMO) is a promising candidate technology to meet the demand. By scaling up the conventional MIMO by orders of magnitude number of \emph{active} antennas, a massive MIMO system can harvest considerable channel degrees of freedom to increase the spectral efficiency. However, increasing the number of \emph{active} antennas needs to increase both the numbers of Radio Frequency (RF) transceivers and antenna elements \emph{at the same rate}, which will increase the RF complexity and cost dramatically. It is known that the complexity and cost of antenna elements are usually much lower than that of RF transceivers, which motivates us to scale up MIMO by a lower increasing rate of the number of RF transceivers than that of antenna elements, resulting in so-called low RF-complexity massive MIMO systems. In this thesis, we study two types of low RF-complexity massive MIMO systems, i.e., massive MIMO antenna selection systems and massive MIMO hybrid analog-digital beamforming systems. Both systems use specific RF networks to bridge a massive number of antennas and a small number of RF transceivers, leading to signal dimension reduction from antennas to RF transceivers. The RF network used in antenna selection is referred to as RF switching network; while the RF network used in hybrid beamforming is referred to as Phase Shifting Network (PSN). Both RF networks have two types of architectures, i.e., full-array architecture and sub-array architecture. The latter has lower insertion loss, lower complexity and better scalability than the former, but at the price of performance degradation caused by connection constraint, which will be studied for both low RF-complexity systems in this thesis. In addition, a low RF-complexity PSN for the hybrid analog-digital beamforming system needs also to be studied to replace the conventional high-complexity-and-cost phase-shifter-based PSN. In the antenna selection system, the upper bounds on the channel capacity using asymptotic theory on order statistics are derived at the large-scale limit. The optimal antenna selection algorithms are also developed, which are based on Branch And Bound (BAB) search algorithm. Through the theoretical and algorithm studies, it is found that the sub-array antenna selection has close performance to the full-array antenna selection. In the hybrid beamforming system, we propose to use Rotman lens as PSN, which is of lower complexity and cost than the conventional phase-shifter-based PSN. Two beam selection algorithms, i.e., sub-optimal greedy search and optimal BAB search, are also proposed. In addition, the Rotman lenses are designed, fabricated and measured. The measurement results together with the beam selection algorithms are used to perform Monte Carlo simulation. Simulation results show that the proposed Rotman-lens-based system with the sub-array architecture suffers noticeable performance degradation compared to the system with the full-array architecture when ideal Rotman lenses are used. But when practical non-ideal Rotman lens are used, the former outperforms the latter when the number of antennas is large enough. Most interestingly, with non-ideal hardware, the sub-array Rotman-lens-based system has close performance to the sub-array phase-shifter-based system, and also exhibits a wideband capability. To prove the advantage of the low RF-complexity massive MIMO, two testbeds are built up for the antenna selection and hybrid beamforming systems, respectively. The measurement results show the low RF-complexity massive MIMO systems have superior performance over the small-scale MIMO systems under the condition of the same number of RF transceivers. The results in this thesis show that the low RF-complexity massive MIMO systems proposed in this thesis are feasible in technology and promising in performance, validating its potential usage for the future 5G wireless communication systems.Der drahtlose Datenverkehr ist in den letzten Jahrzehnten dramatisch gestiegen und wird auch in Zukunft weiter zunehmen. Infolgedessen muss die Datenkapazität der drahtlosen Infrastruktur erhöht werden. Mehrantennen Systeme mit einer sehr großen Anzahl an Antennen (engl. Massive Multiple-Input Multiple-Output (MIMO)) sind vielversprechende Technologiekandidaten, um diese Nachfrage zu erfüllen. Durch die Hochskalierung der Antennenanzahl eines konventionellen MIMO um mehrere Größenordnungen kann ein Massive MIMO-System erhebliche Kanalfreiheitsgrade erlangen, um die spektrale Effizienz zu verbessern. Allerdings muss mit der Anzahl der \emph{aktiven} Antennen sowohl die Anzahl der Hochfrequenz (engl. Radio Frequency (RF)) Transceiver als auch die der Antennenelemente \emph{im gleichen Maße} vergrössert werden, was die RF-Komplexität und Kosten dramatisch erhöht. Dabei ist bekannt, dass die Komplexität und die Kosten von Antennenelementen in der Regel viel niedriger sind als die von RF-Transceivern. Dies führt uns dazu dass wir das MIMO-System um eine im Verhältnis zur Antennenzahl geringere Anzahl von RF-Transceivern erweitern wollen, den so genannten Massive MIMO-Systemen mit geringer RF-Komplexität. In dieser Arbeit untersuchen wir zwei Arten von Massive MIMO-Systemen mit geringer RF-Komplexität, nämlich Massive MIMO-Antennenselektionssysteme und Massive MIMO-Hybrid-Analog-Digital-Strahlformungssysteme. Beide Systeme verwenden spezielle RF-Netzwerke, um eine größere Anzahl von Antennen von einer kleineren Anzahl von RF-Transceivern zu versorgen, was zu einer Signalraumreduktion von den Antennen zu den RF-Transceivern führt. Das bei der Antennenselektions verwendete RF-Netzwerk wird als RF-Koppelfeld bezeichnet, während das RF-Netzwerk, das bei der Hybrid-Strahlformung verwendet wird, als Phasenverschiebungsnetzwerk (engl. Phase Shifting Network, PSN) bezeichnet wird. Beide RF-Netzwerke können als Voll-Array-Architektur oder als Sub-Array-Architektur realisiert werden. Letztere hat eine geringere Einfügedämpfung, eine geringere Komplexität und eine bessere Skalierbarkeit als die erstere, aber zum Preis der Leistungsverschlechterung, die durch eine eingeschränkung Anzahl von Antennen-Transceiver-Verbindungen verursacht wird. Die vorliegende Arbeit untersucht dies für beide Systeme mit niedriger RF-Komplexität. Darüber hinaus wird auch ein PSN mit niedriger RF-Komplexität für das Hybride-Analog-Digital- Strahlformungssystem untersucht, das das herkömmliche hochkomplexe und kostenintensive PSN ersetzen soll. Im Antennenselektionssystem werden die Obergrenzen der Kanalkapazität unter Verwendung der Asymptoten Theorie der Ordnungsstatistik im Grenzverhalten abgeleitet. Die optimalen Antennenselektions-Algorithmen, die auf dem Branch and Bound (BAB) Suchalgorithmus basieren, werden ebenfalls entwickelt. Die theoretischen und algorithmischen Untersuchungen zeigen, dass die Leistung der Sub-Array-Antennenauswahl dicht bei der der Voll-Array-Antennenselektions liegt. Im Hybrid-Strahlformungssystem schlagen wir vor, eine Rotman-Linse als PSN zu verwenden, die von geringerer Komplexität und Kosten ist als das herkömmliche auf Phasenverschiebung basierende PSN. Es werden zwei Strahlauswahlalgorithmen vorgeschlagen, eine suboptimale Greedy-Suche und eine optimale BAB-Suche. Darüber hinaus wird die Rotman-Linse entworfen, gefertigt und vermessen. Die Messergebnisse werden zusammen mit den Strahlselektionsalgorithmen zur Durchführung einer Monte-Carlo-Simulation verwendet. Simulationsergebnisse zeigen, dass das vorgeschlagene Rotman-Linsen-basierte System mit der Sub-Array-Architektur eine spürbare Leistungsverschlechterung im Vergleich zum System mit der Full-Array-Architektur erleidet, wenn ideale Rotman-Linsen verwendet werden. Aber wenn reale nicht-ideale Rotman-Linsen verwendet werden, übertrifft erstere die zweite, wenn die Anzahl der Antennen groß genug ist. Noch interessanter, mit nicht-idealer Hardware, zeigt das Sub-Array Rotman-Linsen-basierte System in etwa die gleiche Leistung wie das Sub-Array Phasenschieber-basierte System und weist auch Breitbandfähigkeiten auf. Um den Vorteil der Massive MIMO-Systeme mit geringer RF-Komplexität zu beweisen, werden zwei Testumgebungen für die Antennenauswahl- und Hybrid-Strahlformungssysteme aufgebaut. Die Messergebnisse zeigen, dass, unter der Bedingung einer gleichen Anzahl von RF-Transceivern, die Massive MIMO-Systeme mit geringer RF-Komplexität in der Leistung den normalen MIMO-Systemen überlegen sind. Die Ergebnisse meiner Arbeit zeigen, dass die von mir vorgeschlagenen Massive MIMO-Systeme mit geringer RF-Komplexität technisch machbar und vielversprechend in der Leistung sind und bestätigen damit deren potentielle Nutzung für die zukünftigen 5G-Funkkommunikationssysteme

Low-complexity antenna selection techniques for massive MIMO systems

PhD ThesisMassive Multiple-Input Multiple-Output (M-MIMO) is a state of the art technology in wireless communications, where hundreds of antennas are exploited at the base station (BS) to serve a much smaller number of users. Employing large antenna arrays can improve the performance dramatically in terms of the achievable rates and radiated energy, however, it comes at the price of increased cost, complexity, and power consumption. To reduce the hardware complexity and cost, while maintaining the advantages of M-MIMO, antenna selection (AS) techniques can be applied where only a subset of the available antennas at the BS are selected. Optimal AS can be obtained through exhaustive search, which is suitable for conventional MIMO systems, but is prohibited for systems with hundreds of antennas due to its enormous computational complexity. Therefore, this thesis address the problem of designing low complexity AS algorithms for multi-user (MU) M-MIMO systems. In chapter 3, different evolutionary algorithms including bio-inspired, quantuminspired, and heuristic methods are applied for AS in uplink MU M-MIMO systems. It was demonstrated that quantum-inspired and heuristic methods outperform the bio-inspired techniques in terms of both complexity and performance. In chapter 4, a downlink MU M-MIMO scenario is considered with Matched Filter (MF) precoding. Two novel AS algorithms are proposed where the antennas are selected without any vector multiplications, which resulted in a dramatic complexity reduction. The proposed algorithms outperform the case where all antennas are activated, in terms of both energy and spectral efficiencies. In chapter 5, three AS algorithms are designed and utilized to enhance the performance of cell-edge users, alongside Max-Min power allocation control. The algorithms aim to either maximize the channel gain, or minimize the interference for the worst-case user only. The proposed methods in this thesis are compared with other low complexity AS schemes and showed a great performance-complexity trade-off