Exploitation of Robust AoA Estimation and Low Overhead Beamforming in mmWave MIMO System

The limited spectral resource for wireless communications and dramatic proliferation of new applications and services directly necessitate the exploitation of millimeter wave (mmWave) communications. One critical enabling technology for mmWave communications is multi-input multi-output (MIMO), which enables other important physical layer techniques, specifically beamforming and antenna array based angle of arrival (AoA) estimation. Deployment of beamforming and AoA estimation has many challenges. Significant training and feedback overhead is required for beamforming, while conventional AoA estimation methods are not fast or robust. Thus, in this thesis, new algorithms are designed for low overhead beamforming, and robust AoA estimation with significantly reduced signal samples (snapshots). The basic principle behind the proposed low overhead beamforming algorithm in time-division duplex (TDD) systems is to increase the beam serving period for the reduction of the feedback frequency. With the knowledge of location and speed of each candidate user equipment (UE), the codeword can be selected from the designed multi-pattern codebook, and the corresponding serving period can be estimated. The UEs with long serving period and low interference are selected and served simultaneously. This algorithm is proved to be effective in keeping the high data rate of conventional codebook-based beamforming, while the feedback required for codeword selection can be cut down. A fast and robust AoA estimation algorithm is proposed as the basis of the low overhead beamforming for frequency-division duplex (FDD) systems. This algorithm utilizes uplink transmission signals to estimate the real-time AoA for angle-based beamforming in environments with different signal to noise ratios (SNR). Two-step neural network models are designed for AoA estimation. Within the angular group classified by the first model, the second model further estimates AoA with high accuracy. It is proved that these AoA estimation models work well with few signal snapshots, and are robust to applications in low SNR environments. The proposed AoA estimation algorithm based beamforming generates beams without using reference signals. Therefore, the low overhead beamforming can be achieved in FDD systems. With the support of proposed algorithms, the mmWave resource can be leveraged to meet challenging requirements of new applications and services in wireless communication systems

Massive Multi-Antenna Communications with Low-Resolution Data Converters

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in future cellular communication systems. In massive MU-MIMO systems, the number of antennas at the base station (BS) is scaled up by several orders of magnitude compared to traditional multi-antenna systems with the goals of enabling large gains in capacity and energy efficiency. However, scaling up the number of active antenna elements at the BS will lead to significant increases in power consumption and system costs unless power-efficient and low-cost hardware components are used. In this thesis, we investigate the performance of massive MU-MIMO systems for the case when the BS is equipped with low-resolution data converters.First, we consider the massive MU-MIMO uplink for the case when the BS uses low-resolution analog-to-digital converters (ADCs) to convert the received signal into the digital domain. Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information (CSI), which implies that the channel realizations have to be learned through pilot transmission followed by BS-side channel estimation, based on coarsely quantized observations. We derive a low-complexity channel estimator and present lower bounds and closed-form approximations for the information-theoretic rates achievable with the proposed channel estimator together with conventional linear detection algorithms. Second, we consider the massive MU-MIMO downlink for the case when the BS uses low-resolution digital-to-analog converters (DACs) to generate the transmit signal. We derive lower bounds and closed-form approximations for the achievable rates with linear precoding under the assumption that the BS has access to perfect CSI. We also propose novel nonlinear precoding algorithms that are shown to significantly outperform linear precoding for the extreme case of 1-bit DACs. Specifically, for the case of symbol-rate 1-bit DACs and frequency-flat channels, we develop a multitude of nonlinear precoders that trade between performance and complexity. We then extend the most promising nonlinear precoders to the case of oversampling 1-bit DACs and orthogonal frequency-division multiplexing for operation over frequency-selective channels.Third, we extend our analysis to take into account other hardware imperfections such as nonlinear amplifiers and local oscillators with phase noise.The results in this thesis suggest that the resolution of the ADCs and DACs in massive MU-MIMO systems can be reduced significantly compared to what is used in today\u27s state-of-the-art communication systems, without significantly reducing the overall system performance

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we examine exact and approximate near-field channel models for XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further motivate and discuss low-complexity signal processing schemes to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems.Comment: 38 pages, 10 figure

Wireless interference networks with limited feedback

Wir betrachten das Problem der Akquirierung von Kanalzustandsinformationen an den Sendern von drahtlosen Netzwerken und entwickeln Feedbackverfahren und Sendestrategien für verschiedene Netzwerk Architekturen. Die entwickelten Verfahren werden analysiert und die Skalierung der Performance des Gesamtsystems anhand bestimmter Systemparameter bestimmt. Zuerst betrachten wir eine einzelne Zelle eines zellularen Systems und nehmen an, dass die Beamformingvektoren durch ein festes Codebuch vorgegeben sind. Wir entwickeln und analysieren ein neues Feedbackverfahren, dass Flexibilität und Robustheit vereint und dadurch effiziente und zuverlässige Kommunikation mit den Empfängern ermöglicht. Eine Analyse des Verfahrens zeigt, dass die Skalierung des Ratenverlustes durch quantisierte Kanalzustandsinformation besser ist als bei vergleichbaren Verfahren. Für das Feedbackverfahren wird ein spezieller Algorithmus entwickelt der es ermöglicht Codebücher für verschiedene Kanalmodelle zu generieren und zu optimieren. Die analytischen Ergebnisse werden durch Simulationen validiert und bestätigen einen Gewinn gegenüber vergleichbaren Verfahren. Anschließend betrachten wir zellulare Systeme mit mehreren Zellen. Wir charakterisieren die Freiheitsgrade (degrees of freedom) unter verschiedenen Annahmen über das Kanalmodell. Des weiteren entwickeln wir verschiedene Algorithmen, die die optimalen Freiheitsgrade erreichen können. Anschließend wird ein Feedbackverfahren entwickelt, dass den Feedbackaufwand für die entwickelten Algorithmen signifikant reduziert. Wir analysieren eine breite Klasse von zellularen Systemen die beliebige koordinierte Sendestrategien verwenden. Für diese Klasse von Systemen leiten wir die Skalierung des Ratenverlustes relativ zum Feedbackaufwand her. Abschließend zeigen wir, wie die analytischen Ergebnisse auf das entwickelte Feedbackverfahren angewendet werden können. Im letzten Kapitel entwickeln wir ein Framework, dass das Potenzial von Compressed Sensing nutzt um den Messaufwand und Feedbackaufwand in zellularen Systemen mit vielen Teilnehmern signifikant zu reduzieren. Das Framework ermöglicht es die Datenraten der Nutzer innerhalb gegebener Fehlerschranken zu schätzen. Grundlage ist neben Compressed Sensing ein neues Messverfahren, dass die Überlagerung von Signalen im Kanal nutzt, um zufällige nicht adaptive Messungen der Kanalkoeffizienten am Empfänger zu ermöglichen. Diese Messungen werden zu einer zentralen Steuereinheit übertragen und dort dekodiert. Wir analysieren die Genauigkeit der Rekonstruktion für einen linearen und einen nicht-linearen Dekodierer und leiten die Skalierung mit der Anzahl der Messungen her. Abschließend zeigen wir, wie der entwickelte Ansatz in zellularen Systemen angewendet werden kann.We consider the problem of acquiring accurate channel state information at the transmitters of a wireless network. We develop different feedback and transmit strategies for different network architectures and analyze their performance. First, we consider a single cell of cellular system and assume that the beamforming vectors are given by a fixed transmit codebook. We develop and analyze a new feedback and transmit strategy which combines flexibility and robustness needed for efficient and reliable communication. We prove that it has better scaling properties compared to classical results on the limited feedback problem in the broadcast channel and that this benefit improves with an increasing number of transmit antennas. We show how feedback codebooks can be designed for different propagation environments. Link level and system level simulations sustain the analytic results showing performance gains of up to 50 % or 70 % compared to zeroforcing when using multiple antennas at the base station and multiple antennas or a single antenna at the terminals, respectively. We characterize the degrees of freedom (i.e. the multiplexing gain) of multi-cellular systems under different assumptions on the channel model and for different system setups. We propose different algorithms that possibly achieve the optimal degrees of freedom. The first algorithm aims on aligning the interference at each receiver in a subspace of the available receive space. Our second algorithm aims on directly maximizing the signal-to-interference-plus-noise ratio (SINR) of all receivers. By allowing symbol extensions over time or frequency and including a user selection we are able to achieve the alignment of interference for many system setups and exploit multi-user diversity. For coordinated transmit strategies we find the scaling of the performance loss with the feedback load. A distributed interference alignment algorithm is introduced. The algorithm makes efficient use of quantized channel state information and significantly reduces the feedback overhead. We develop a framework that we call compressive rate estimation. To this end, we assume that the composite channel gain matrix (i.e. the matrix of all channel gains between all network nodes) is compressible which means it can be approximated by a sparse or low rank representation. We develop a sensing protocol that exploits the superposition principle of the wireless channel and enables the receiving nodes to obtain non-adaptive random measurements of columns of the composite channel matrix. The random measurements are fed back to a central controller who decodes the composite channel gain matrix (or parts of it) and estimates individual user rates. We analyze the rate loss for a linear and a non-linear decoder and find the scaling laws according to the number of non-adaptive measurements