Enabling Technology and Algorithm Design for Location-Aware Communications

Location-awareness is emerging as a promising technique for future-generation wire­ less network to adaptively enhance and optimize its overall performance through location-enabled technologies such as location-assisted transceiver reconfiguration and routing. The availability of accurate location information of mobile users becomes the essential prerequisite for the design of such location-aware networks. Motivated by the low locationing accuracy of the Global Positioning System (GPS) in dense multipath environments, which is commonly used for acquiring location information in most of the existing wireless networks, wireless communication system-based po­sitioning systems have been investigated as alternatives to fill the gap of the GPS in coverage. Distance-based location techniques using time-of-arrival (TOA) mea­surements are commonly preferred by broadband wireless communications where the arrival time of the signal component of the First Arriving Path (FAP) can be con­verted to the distance between the receiver and the transmitter with known location. With at least three transmitters, the location of the receiver can be determined via trilatération method. However, identification of the FAP’s signal component in dense multipath scenarios is quite challenging due to the significantly weaker power of the FAP as compared with the Later Arriving Paths (LAPs) from scattering, reflection and refraction, and the superposition of these random arrival LAPs’ signal compo­ nents will become large interference to detect the FAP. In this thesis, a robust FAP detection scheme based on multipath interference cancellation is proposed to im­ prove the accuracy of location estimation in dense multipath environments. In the proposed algorithm, the signal components of LAPs is reconstructed based on the estimated channel and data with the assist of the communication receiver, and sub­ sequently removed from the received signal. Accurate FAP detection results are then achieved with the cross-correlation between the interference-suppressed signal and an augmented preamble which is the combination of the original preamble for com­ munications and the demodulated data sequences. Therefore, more precise distance estimation (hence location estimation) can be obtained with the proposed algorithm for further reliable network optimization strategy design. On the other hand, multiceli cooperative communication is another emerging technique to substantially improve the coverage and throughput of traditional cellular networks. Location-awareness also plays an important role in the design and imple­mentation of multiceli cooperation technique. With accurate location information of mobile users, the complexity of multiceli cooperation algorithm design can be dra­matically reduced by location-assisted applications, e.g., automatic cooperative base station (BS) determination and signal synchronization. Therefore, potential latency aroused by cooperative processing will be minimized. Furthermore, the cooperative BSs require the sharing of certain information, e.g., channel state information (CSI), user data and transmission parameters to perform coordination in their signaling strategies. The BSs need to have the capabilities to exchange available information with each other to follow up with the time-varying communication environment. As most of broadband wireless communication systems are already orthogonal frequency division multiplexing (OFDM)-based, a Multi-Layered OFDM System, which is spe­cially tailored for multiceli cooperation is investigated to provide parallel robust, efficient and flexible signaling links for BS coordination purposes. These layers are overlaid with data-carrying OFDM signals in both time and frequency domains and therefore, no dedicated radio resources are required for multiceli cooperative networks. In the final aspect of this thesis, an enhanced channel estimation through itera­ tive decision-directed method is investigated for OFDM system, which aims to provide more accurate estimation results with the aid of the demodulated OFDM data. The performance of traditional training sequence-based channel estimation is often lim­ ited by the length of the training. To achieve acceptable estimation performance, a long sequence has to be used which dramatically reduces the transmission efficiency of data communication. In this proposed method, the restriction of the training se­quence length can be removed and high channel estimation accuracy can be achieved with high transmission efficiency, and therefore it particular fits in multiceli coopera­tive networks. On the other hand, as the performance of the proposed FAP detection scheme also relies on the accuracy of channel estimation and data detection results, the proposed method can be combined with the FAP detection scheme to further optimize the accuracy of multipath interference cancellation and FAP detection

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

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

MIMO signal processing in offset-QAM based filter bank multicarrier systems

Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft