139 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
Power Allocation in Uplink NOMA-Aided Massive MIMO Systems
In the development of the fifth-generation (5G) as well as the vision for the future generations of wireless communications networks, massive multiple-input multiple-output (MIMO) technology has played an increasingly important role as a key enabler to meet the growing demand for very high data throughput. By equipping base stations (BSs) with hundreds to thousands antennas, the massive MIMO technology is capable of simultaneously serving multiple users in the same time-frequency resources with simple linear signal processing in both the downlink (DL) and uplink (UL) transmissions. Thanks to the asymptotically orthogonal property of users' wireless channels, the simple linear signal processing can effectively mitigate inter-user interference and noise while boosting the desired signal's gain, and hence achieves high data throughput. In order to realize this orthogonal property in a practical system, one critical requirement in the massive MIMO technology is to have the instantaneous channel state information (CSI), which is acquired via channel estimation with pilot signaling. Unfortunately, the connection capability of a conventional massive MIMO system is strictly limited by the time resource spent for channel estimation. Attempting to serve more users beyond the limit may result in a phenomenon known as pilot contamination, which causes correlated interference, lowers signal gain and hence, severely degrades the system's performance. A natural question is ``Is it at all possible to serve more users beyond the limit of a conventional massive MIMO system?''. The main contribution of this thesis is to provide a promising solution by integrating the concept of nonorthogonal multiple access (NOMA) into a massive MIMO system.
The key concept of NOMA is based on assigning each unit of orthogonal radio resources, such as frequency carriers, time slots or spreading codes, to more than one user and utilize a non-linear signal processing technique like successive interference cancellation (SIC) or dirty paper coding (DPC) to mitigate inter-user interference. In a massive MIMO system, pilot sequences are also orthogonal resources, which can be allocated with the NOMA approach. By sharing a pilot sequence to more than one user and utilizing the SIC technique, a massive MIMO system can serve more users with a fixed amount of time spent for channel estimation. However, as a consequence of pilot reuse, correlated interference becomes the main challenge that limits the spectral efficiency (SE) of a massive MIMO-NOMA system. To address this issue, this thesis focuses on how to mitigate correlated interference when combining NOMA into a massive MIMO system in order to accommodate a higher number of wireless users.
In the first part, we consider the problem of SIC in a single-cell massive MIMO system in order to serve twice the number of users with the aid of time-offset pilots. With the proposed time-offset pilots, users are divided into two groups and the uplink pilots from one group are transmitted simultaneously with the uplink data of the other group, which allows the system to accommodate more users for a given number of pilots. Successive interference cancellation is developed to ease the effect of pilot contamination and enhance data detection.
In the second part, the work is extended to a cell-free network, where there is no cell boundary and a user can be served by multiple base stations. The chapter focuses on the NOMA approach for sharing pilot sequences among users. Unlike the conventional cell-free massive MIMO-NOMA systems in which the UL signals from different access points are equally combined over the backhaul network, we first develop an optimal backhaul combining (OBC) method to maximize the UL signal-to-interference-plus-noise ratio (SINR). It is shown that, by using OBC, the correlated interference can be effectively mitigated if the number of users assigned to each pilot sequence is less than or equal to the number of base stations. As a result, the cell-free massive MIMO-NOMA system with OBC can enjoy unlimited performance when the number of antennas at each BS tends to infinity.
Finally, we investigate the impact of imperfect SIC to a NOMA cell-free massive MIMO system. Unlike the majority of existing research works on performance evaluation of NOMA, which assume perfect channel state information and perfect data detection for SIC, we take into account the effect of practical (hence imperfect) SIC. We show that the received signal at the backhaul network of a cell-free massive MIMO-NOMA system can be effectively treated as a signal received over an additive white Gaussian noised (AWGN) channel. As a result, a discrete joint distribution between the interfering signal and its detected version can be analytically found, from which an adaptive SIC scheme is proposed to improve performance of interference cancellation
Advances in Multi-User Scheduling and Turbo Equalization for Wireless MIMO Systems
Nach einer Einleitung behandelt Teil 2 Mehrbenutzer-Scheduling für die
Abwärtsstrecke von drahtlosen MIMO Systemen mit einer Sendestation und
kanaladaptivem precoding: In jeder Zeit- oder Frequenzressource kann eine
andere Nutzergruppe gleichzeitig bedient werden, räumlich getrennt durch
unterschiedliche Antennengewichte. Nutzer mit korrelierten Kanälen sollten
nicht gleichzeitig bedient werden, da dies die räumliche Trennbarkeit
erschwert. Die Summenrate einer Nutzermenge hängt von den Antennengewichten
ab, die wiederum von der Nutzerauswahl abhängen. Zur Entkopplung des
Problems schlägt diese Arbeit Metriken vor basierend auf einer geschätzten
Rate mit ZF precoding. Diese lässt sich mit Hilfe von wiederholten
orthogonalen Projektionen abschätzen, wodurch die Berechnung von
Antennengewichten beim Scheduling entfällt. Die Ratenschätzung kann
basierend auf momentanen Kanalmessungen oder auf gemittelter Kanalkenntnis
berechnet werden und es können Datenraten- und Fairness-Kriterien
berücksichtig werden. Effiziente Suchalgorithmen werden vorgestellt, die
die gesamte Systembandbreite auf einmal bearbeiten können und zur
Komplexitätsreduktion die Lösung in Zeit- und Frequenz nachführen können.
Teil 3 zeigt wie mehrere Sendestationen koordiniertes Scheduling und
kooperative Signalverarbeitung einsetzen können. Mittels orthogonalen
Projektionen ist es möglich, Inter-Site Interferenz zu schätzen, ohne
Antennengewichte berechnen zu müssen. Durch ein Konzept virtueller Nutzer
kann der obige Scheduling-Ansatz auf mehrere Sendestationen und sogar
Relays mit SDMA erweitert werden. Auf den benötigten Signalisierungsaufwand
wird kurz eingegangen und eine Methode zur Schätzung der Summenrate eines
Systems ohne Koordination besprochen. Teil4 entwickelt Optimierungen für
Turbo Entzerrer. Diese Nutzen Signalkorrelation als Quelle von Redundanz.
Trotzdem kann eine Kombination mit MIMO precoding sinnvoll sein, da bei
Annahme realistischer Fehler in der Kanalkenntnis am Sender keine optimale
Interferenzunterdrückung möglich ist. Mit Hilfe von EXIT Charts wird eine
neuartige Methode zur adaptiven Nutzung von a-priori-Information zwischen
Iterationen entwickelt, die die Konvergenz verbessert. Dabei wird gezeigt,
wie man semi-blinde Kanalschätzung im EXIT chart berücksichtigen kann.
In Computersimulationen werden alle Verfahren basierend auf
4G-Systemparametern überprüft.After an introduction, part 2 of this thesis deals with downlink multi-user
scheduling for wireless MIMO systems with one transmitting station
performing channel adaptive precoding:Different user subsets can be served
in each time or frequency resource by separating them in space with
different antenna weight vectors. Users with correlated channel matrices
should not be served jointly since correlation impairs the spatial
separability.The resulting sum rate for each user subset depends on the
precoding weights, which in turn depend on the user subset. This thesis
manages to decouple this problem by proposing a scheduling metric based on
the rate with ZF precoding such as BD, written with the help of orthogonal
projection matrices. It allows estimating rates without computing any
antenna weights by using a repeated projection approximation.This rate
estimate allows considering user rate requirements and fairness criteria
and can work with either instantaneous or long term averaged channel
knowledge.Search algorithms are presented to efficiently solve user
grouping or selection problems jointly for the entire system bandwidth
while being able to track the solution in time and frequency for complexity
reduction.
Part 3 shows how multiple transmitting stations can benefit from
cooperative scheduling or joint signal processing. An orthogonal projection
based estimate of the inter-site interference power, again without
computing any antenna weights, and a virtual user concept extends the
scheduling approach to cooperative base stations and finally included SDMA
half-duplex relays in the scheduling.Signalling overhead is discussed and a
method to estimate the sum rate without coordination.
Part 4 presents optimizations for Turbo Equalizers. There, correlation
between user signals can be exploited as a source of redundancy.
Nevertheless a combination with transmit precoding which aims at reducing
correlation can be beneficial when the channel knowledge at the transmitter
contains a realistic error, leading to increased correlation. A novel
method for adaptive re-use of a-priori information between is developed to
increase convergence by tracking the iterations online with EXIT charts.A
method is proposed to model semi-blind channel estimation updates in an
EXIT chart.
Computer simulations with 4G system parameters illustrate the methods using realistic channel models.Im Buchhandel erhältlich:
Advances in Multi-User Scheduling and Turbo Equalization for Wireless MIMO Systems / Fuchs-Lautensack,Martin
Ilmenau: ISLE, 2009,116 S.
ISBN 978-3-938843-43-
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