Reciprocity Calibration for Massive MIMO: Proposal, Modeling and Validation

This paper presents a mutual coupling based calibration method for time-division-duplex massive MIMO systems, which enables downlink precoding based on uplink channel estimates. The entire calibration procedure is carried out solely at the base station (BS) side by sounding all BS antenna pairs. An Expectation-Maximization (EM) algorithm is derived, which processes the measured channels in order to estimate calibration coefficients. The EM algorithm outperforms current state-of-the-art narrow-band calibration schemes in a mean squared error (MSE) and sum-rate capacity sense. Like its predecessors, the EM algorithm is general in the sense that it is not only suitable to calibrate a co-located massive MIMO BS, but also very suitable for calibrating multiple BSs in distributed MIMO systems. The proposed method is validated with experimental evidence obtained from a massive MIMO testbed. In addition, we address the estimated narrow-band calibration coefficients as a stochastic process across frequency, and study the subspace of this process based on measurement data. With the insights of this study, we propose an estimator which exploits the structure of the process in order to reduce the calibration error across frequency. A model for the calibration error is also proposed based on the asymptotic properties of the estimator, and is validated with measurement results.Comment: Submitted to IEEE Transactions on Wireless Communications, 21/Feb/201

System capacity enhancement for 5G network and beyond

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Doctor of PhilosophyThe demand for wireless digital data is dramatically increasing year over year. Wireless communication systems like Laptops, Smart phones, Tablets, Smart watch, Virtual Reality devices and so on are becoming an important part of people’s daily life. The number of mobile devices is increasing at a very fast speed as well as the requirements for mobile devices such as super high-resolution image/video, fast download speed, very short latency and high reliability, which raise challenges to the existing wireless communication networks. Unlike the previous four generation communication networks, the fifth-generation (5G) wireless communication network includes many technologies such as millimetre-wave communication, massive multiple-input multiple-output (MIMO), visual light communication (VLC), heterogeneous network (HetNet) and so forth. Although 5G has not been standardised yet, these above technologies have been studied in both academia and industry and the goal of the research is to enhance and improve the system capacity for 5G networks and beyond by studying some key problems and providing some effective solutions existing in the above technologies from system implementation and hardware impairments’ perspective. The key problems studied in this thesis include interference cancellation in HetNet, impairments calibration for massive MIMO, channel state estimation for VLC, and low latency parallel Turbo decoding technique. Firstly, inter-cell interference in HetNet is studied and a cell specific reference signal (CRS) interference cancellation method is proposed to mitigate the performance degrade in enhanced inter-cell interference coordination (eICIC). This method takes carrier frequency offset (CFO) and timing offset (TO) of the user’s received signal into account. By reconstructing the interfering signal and cancelling it afterwards, the capacity of HetNet is enhanced. Secondly, for massive MIMO systems, the radio frequency (RF) impairments of the hardware will degrade the beamforming performance. When operated in time duplex division (TDD) mode, a massive MIMO system relies on the reciprocity of the channel which can be broken by the transmitter and receiver RF impairments. Impairments calibration has been studied and a closed-loop reciprocity calibration method is proposed in this thesis. A test device (TD) is introduced in this calibration method that can estimate the transmitters’ impairments over-the-air and feed the results back to the base station via the Internet. The uplink pilots sent by the TD can assist the BS receivers’ impairment estimation. With both the uplink and downlink impairments estimates, the reciprocity calibration coefficients can be obtained. By computer simulation and lab experiment, the performance of the proposed method is evaluated. Channel coding is an essential part of a wireless communication system which helps fight with noise and get correct information delivery. Turbo codes is one of the most reliable codes that has been used in many standards such as WiMAX and LTE. However, the decoding process of turbo codes is time-consuming and the decoding latency should be improved to meet the requirement of the future network. A reverse interleave address generator is proposed that can reduce the decoding time and a low latency parallel turbo decoder has been implemented on a FPGA platform. The simulation and experiment results prove the effectiveness of the address generator and show that there is a trade-off between latency and throughput with a limited hardware resource. Apart from the above contributions, this thesis also investigated multi-user precoding for MIMO VLC systems. As a green and secure technology, VLC is achieving more and more attention and could become a part of 5G network especially for indoor communication. For indoor scenario, the MIMO VLC channel could be easily ill-conditioned. Hence, it is important to study the impact of the channel state to the precoding performance. A channel state estimation method is proposed based on the signal to interference noise ratio (SINR) of the users’ received signal. Simulation results show that it can enhance the capacity of the indoor MIMO VLC system

Max-Min SINR in Large-Scale Single-Cell MU-MIMO: Asymptotic Analysis and Low Complexity Transceivers

This work focuses on the downlink and uplink of large-scale single-cell MU-MIMO systems in which the base station (BS) endowed with $M$ antennas communicates with $K$ single-antenna user equipments (UEs). Particularly, we aim at reducing the complexity of the linear precoder and receiver that maximize the minimum signal-to-interference-plus-noise ratio subject to a given power constraint. To this end, we consider the asymptotic regime in which $M$ and $K$ grow large with a given ratio. Tools from random matrix theory (RMT) are then used to compute, in closed form, accurate approximations for the parameters of the optimal precoder and receiver, when imperfect channel state information (modeled by the generic Gauss-Markov formulation form) is available at the BS. The asymptotic analysis allows us to derive the asymptotically optimal linear precoder and receiver that are characterized by a lower complexity (due to the dependence on the large scale components of the channel) and, possibly, by a better resilience to imperfect channel state information. However, the implementation of both is still challenging as it requires fast inversions of large matrices in every coherence period. To overcome this issue, we apply the truncated polynomial expansion (TPE) technique to the precoding and receiving vector of each UE and make use of RMT to determine the optimal weighting coefficients on a per-UE basis that asymptotically solve the max-min SINR problem. Numerical results are used to validate the asymptotic analysis in the finite system regime and to show that the proposed TPE transceivers efficiently mimic the optimal ones, while requiring much lower computational complexity.Comment: 13 pages, 4 figures, submitted to IEEE Transactions on Signal Processin

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

Clock Error Impact on NB-IoT Radio Link Performance

3GPP has recently addressed the improvements in Random Access Network (RAN) and specified some new technologies such as enhanced Machine Type Communication (eMTC) and Narrow Band – Internet of Things (NB-IoT) in its release 13 which is also known as LTE-Advanced Pro. These new technologies are addressed mainly to focus on development and deployment of cellular IoT services. NB-IoT is less complex and easily deployable through software upgradation and is compatible to legacy cellular networks such as GSM and 4G which makes it a suitable candidate for IoT. NB-IoT will greatly support LPWAN, thus, it can be deployed for Smart cities and other fields such as smart electricity, smart agriculture, smart health services and smart homes. The NB-IoT targets for low cost device, low power consumption, relaxed delay sensitivity and easy deployment which will greatly support above mentioned fields. This thesis work studies the clock error impact on the radio link performance for up-link transmission on the NB-IoT testbed based on Cloud-RAN using Software Defined Radios (SDR) on a LTE protocol stack. The external clock error is introduced to the network and performance issues are analyzed in the radio link. The analysis indicates packet drops up to 51% in the radio link through the study of received power, packet loss, retransmissions, BLER and SINR for different MCS index. The major performance issues depicted by the analysis are packet loss up to 51% and retransmission of packets up to 128 times for lower SINR and high clock errors. Also, clock errors produce CFO up to 1.25 ppm which results in bad synchronization between UE and eNodeB

Massive MIMO for Dependable Communication

Cellular communication is constantly evolving; currently 5G systems are being deployed and research towards 6G is ongoing. Three use cases have been discussed as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). To fulfill the requirements of these use cases, new technologies are needed and one enabler is massive multiple-input multiple-output (MIMO). By increasing the number of antennas at the base station side, data rates can be increased, more users can be served simultaneously, and there is a potential to improve reliability. In addition, it is possible to achieve better coverage, improved energy efficiency, and low-complex user devices. The performance of any wireless system is limited by the underlying channels. Massive MIMO channels have shown several beneficial properties: the array gain stemming from the combining of the signals from the many antennas, improved user separation due to favourable propagation -- where the user channels become pair-wise orthogonal -- and the channel hardening effect, where the variations of channel gain decreases as the number of antennas increases. Previous theoretical works have commonly assumed independent and identically distributed (i.i.d.) complex Gaussian channels. However, in the first studies on massive MIMO channels, it was shown that common outdoor and indoor environments are not that rich in scattering, but that the channels are rather spatially correlated. To enable the above use cases, investigations are needed for the targeted environments. This thesis focuses on the benefits of deploying massive MIMO systems to achieve dependable communication in a number of scenarios related to the use cases. The first main area is the study of an industrial environment and aims at characterizing and modeling massive MIMO channels to assess the possibility of achieving the requirements of URLLC in a factory context. For example, a unique fully distributed array is deployed with the aim to further exploit spatial diversity. The other main area concerns massive MIMO at sub-GHz, a previously unexplored area. The channel characteristics when deploying a physically very large array for IoT networks are explored. To conclude, massive MIMO can indeed bring great advantages when trying to achieve dependable communication. Although channels in regular indoor environments are not i.i.d. complex Gaussian, the model can be justified in rich scattering industrial environments. Due to massive MIMO, the small-scale fading effects are reduced and when deploying a distributed array also the large-scale fading effects are reduced. In the Internet-of-Things (IoT) scenario, the channel is not as rich scattering. In this use case one can benefit from the array gain to extend coverage and improved energy efficiency, and diversity is gained due to the physically large array

Systems with Massive Number of Antennas: Distributed Approaches

As 5G is entering maturity, the research interest has shifted towards 6G, and specially the new use cases that the future telecommunication infrastructure needs to support. These new use cases encompass much higher requirements, specifically: higher communication data-rates, larger number of users, higher accuracy in localization, possibility to wirelessly charge devices, among others.The radio access network (RAN) has already gone through an evolution on the path towards 5G. One of the main changes was a large increment of the number of antennas in the base-station. Some of them may even reach 100 elements, in what is commonly referred as Massive MIMO. New proposals for 6G RAN point in the direction of continuing this path of increasing the number of antennas, and locate them throughout a certain area of service. Different technologies have been proposed in this direction, such as: cell-free Massive MIMO, distributed MIMO, and large intelligent surface (LIS). In this thesis we focus on LIS, whose conducted theoretical studies promise the fulfillment of the aforementioned requirements.While the theoretical capabilities of LIS have been conveniently analyzed, little has been done in terms of implementing this type of systems. When the number of antennas grow to hundreds or thousands, there are numerous challenges that need to be solved for a successful implementation. The most critical challenges are the interconnection data-rate and the computational complexity.In the present thesis we introduce the implementation challenges, and show that centralized processing architectures are no longer adequate for this type of systems. We also present different distributed processing architectures and show the benefits of this type of schemes. This work aims at giving a system-design guideline that helps the system designer to make the right decisions when designing these type of systems. For that, we provide algorithms, performance analysis and comparisons, including first order evaluation of the interconnection data-rate, processing latency, memory and energy consumption. These numbers are based on models and available data in the literature. Exact values depend on the selected technology, and will be accurately determined after building and testing these type of systems.The thesis concentrates mostly on the topic of communication, with additional exploration of other areas, such as localization. In case of localization, we benefit from the high spatial resolution of a very-large array that provides very rich channel state information (CSI). A CSI-based fingerprinting via neural network technique is selected for this case with promising results. As the communication and localization services are based on the acquisition of CSI, we foresee a common system architecture capable of supporting both cases. Further work in this direction is recommended, with the possibility of including other applications such as sensing.The obtained results indicate that the implementation of these very-large array systems is feasible, but the challenges are numerous. The proposed solutions provide encouraging results that need to be verified with hardware implementations and real measurements