Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin

Space-time-frequency methods for interference-limited communication systems

textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Tensor-based signal processing with applications to MIMO-ODFM systems and intelligent reflecting surfaces

Der Einsatz von Tensor-Algebra-Techniken in der Signalverarbeitung hat in den letzten zwei Jahrzehnten zugenommen. Anwendungen wie Bildverarbeitung, biomedizinische Signalverarbeitung, radar, maschinelles Lernen, deep Learning und Kommunikation im Allgemeinen verwenden weitgehend tensorbasierte Verarbeitungstechniken zur Wiederherstellung, Schätzung und Klassifizierung von Signalen. Einer der Hauptgründe für den Einsatz der Tensorsignalverarbeitung ist die Ausnutzung der mehrdimensionalen Struktur von Signalen, wobei die Einzigartigkeitseigenschaften der Tensor-Zerlegung profitieren. Bei der drahtlosen Kommunikation beispielsweise können die Signale mehrere "Dimensionen" haben, wie Raum, Zeit, Frequenz, Polarisation, usw. Diese Arbeit ist in zwei Teile gegliedert. Im ersten Teil betrachten wir die Anwendung von Tensor-basierten Algorithmen für multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) Systeme unter Berücksichtigung von Vorhandensein von Phasenrauschenstörungen. In diesem Teil schlagen wir einen zweistufigen tensorbasierten Empfänger für eine gemeinsame Kanal-, Phasenrausch- und Datenschätzung in MIMO-OFDM-Systemen vor. In der ersten Stufe zeigen wir, dass das empfangene Signal auf den Pilotunterträgern als PARAFAC-Tensor dritter Ordnung modelliert werden kann. Auf der Grundlage dieses Modells werden zwei Algorithmen für die Schätzung der Phasen- und Kanalrauschen in den Pilotton vorgeschlagen. In der zweiten Stufe werden die übertragenen Daten geschätzt. Zu diesem Zweck schlagen wir einen Zero Forcing (ZF)-Empfänger vor, der sich die Tensorstruktur des empfangenen Signals auf den Datenträgern zunutze macht, indem er den vorgeschlagenen selektiven Kronecker-Produkt-Operators (SKP) kapitalisiert. Die Simulationsergebnisse zeigen, dass der vorgeschlagene Empfänger sowohl bei der Symbolfehlerrate als auch beim normalisierten mittleren quadratischen Fehler des geschätzten Kanal- und Phasenrauschmatrizen eine bessere Leistung im Vergleich zum Stand der Technik erzielt. Der zweite Teil dieser Arbeit befasst sich mit der Anwendung der Tensormodellierung zur Reduzierung des Kontrollsignalisierungsoverhead in zukünftigen drahtlosen Systemen, die durch intelligent reconfigurable surfaces (IRSs) unterstützt werden. Zu diesem Zweck schlagen wir eine Annäherung an die nahezu optimalen IRS-Phasenverschiebungen vor, die sonst einen prohibitiv hohen Kommunikationsoverhead auf den BS-IRS-Kontrollverbindungen verursachen würde. Die Hauptidee besteht darin, den optimalen Phasenvektor des IRSs, der Hunderte oder Tausende von Elementen haben kann, durch ein Tensormodell mit niedrigem Rang darzustellen. Dies wird erreicht durch Faktorisierung einer tensorisierten Version des IRS-Phasenverschiebungsvektors, wobei jede Komponente als Kronecker-Produkt einer vordefinierten Anzahl von Faktoren mit kleinerer Größe modelliert wird, die durch Tensor Zerlegungsalgorithmen erhaltet werden können. Wir zeigen, dass die vorgeschlagenen Low-Rank-Modelle die Rückkopplungsanforderungen für die BS-IRS-Kontrollverbindungen drastisch reduzieren. Die Simulationsergebnisse zeigen, dass die vorgeschlagene Methode besonders in Szenarien mit einer starken Sichtverbindung attraktiv sind. In diesem Fall wird fast die gleiche spektrale Effizienz erreicht wie in den Fällen mit nahezu optimalen Phasenverschiebungen, jedoch mit einem drastisch reduzierten Kommunikations-Overhead.The use of tensor algebra techniques in signal processing has been growing over the last two decades. Applications like image processing, biomedical signal processing, radar, machine/deep learning, and communications in general, largely employ tensor-based techniques for recovery, estimating, and classifying signals. One of the main reasons for using tensor signal processing is the exploitation of the multidimensional structure of signals, while benefiting from the uniqueness properties of tensor decomposition. For example, in wireless communications, the signals can have several “dimensions", e.g., space, time, frequency, polarization, beamspace, etc. This thesis is divided into two parts, first, in the application of a tensor-based algorithm in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems with the presence of phase-noise impairments. In this first part, we propose a two-stage tensor-based receiver for a joint channel, phase-noise, and data estimation in MIMO-OFDM systems. In the first stage, we show that the received signal at the pilot subcarriers can be modeled as a third-order PARAFAC tensor. Based on this model, we propose two algorithms for channel and phase-noise estimation at the pilot subcarriers. The second stage consists of data estimation, for which we propose a ZF receiver that capitalizes on the tensor structure of the received signal at the data subcarriers using the proposed SKP operator. Numerical simulations show that the proposed receivers achieves an improved performance compared to the state-of-art receivers in terms of symbol error rate (SER) and normalized mean square error (NMSE) of the estimated channel and phase-noise matrices. The second part of this thesis focuses on the application of tensor modeling to reduce the control signaling overhead in future wireless systems aided by intelligent reconfigurable surfaces (IRS). To this end, we propose a low-rank approximation of the near-optimal IRS phase-shifts, which would incur prohibitively high communication overhead on the BS-IRS controller links. The key idea is to represent the potentially large IRS phase-shift vector using a low-rank tensor model. This is achieved by factorizing a tensorized version of the IRS phase-shift vector, where each component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, which can be obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase-shifts, but with a drastically reduced communication overhead

Algorithms for Blind Equalization Based on Relative Gradient and Toeplitz Constraints

Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity. In this dissertation, we explore the application of relative gradient for equalizer adaptation with a structure constraint on the equalizer matrix, for fast convergence without excessive computational complexity. We model blind equalization with symbol-rate sampling as a blind source separation (BSS) problem and study two single-carrier transmission schemes, specifically block transmission with guard intervals and continuous transmission. Under either scheme, blind equalization can be achieved using independent component analysis (ICA) algorithms with a Toeplitz or circulant constraint on the structure of the separating matrix. We also develop relative gradient versions of the widely used Bussgang-type algorithms. Processing the equalizer outputs in sliding blocks, we are able to use the relative gradient for adaptation of the Toeplitz constrained equalizer matrix. The use of relative gradient makes the Bussgang condition appear explicitly in the matrix adaptation and speeds up convergence. For the ICA-based and Bussgang-type algorithms with relative gradient and matrix structure constraints, we simplify the matrix adaptations to obtain equivalent equalizer vector adaptations for reduced computational cost. Efficient implementations with fast Fourier transform, and approximation schemes for the cross-correlation terms used in the adaptation, are shown to further reduce computational cost. We also consider the use of a relative gradient algorithm for channel shortening in orthogonal frequency division multiplexing (OFDM) systems. The redundancy of the cyclic prefix symbols is used to shorten a channel with a long impulse response. We show interesting preliminary results for a shortening algorithm based on relative gradient

Wireless Channel Equalization in Digital Communication Systems

Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential. The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation. The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4. For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research. Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases