Overcoming challenges in large-core SI-POF-based system-level modeling and simulation

The application areas for plastic optical fibers such as in-building or aircraft networks usually have tight power budgets and require multiple passive components. In addition, advanced modulation formats are being considered for transmission over plastic optical fibers (POFs) to increase spectral efficiency. In this scenario, there is a clear need for a flexible and dynamic system-level simulation framework for POFs that includes models of light propagation in POFs and the components that are needed to evaluate the entire system performance. Until recently, commercial simulation software either was designed specifically for single-mode glass fibers or modeled individual guided modes in multimode fibers with considerable detail, which is not adequate for large-core POFs where there are millions of propagation modes, strong mode coupling and high variability. These are some of the many challenges involved in the modeling and simulation of POF-based systems. Here, we describe how we are addressing these challenges with models based on an intensity-vs-angle representation of the multimode signal rather than one that attempts to model all the modes in the fiber. Furthermore, we present model approaches for the individual components that comprise the POF-based system and how the models have been incorporated into system-level simulations, including the commercial software packages SimulinkTM and ModeSYSTM

Multiuser Random Beamforming in Millimetre-Waves Channels

This thesis aims to show that in mmWaves channels, schemes based on randomly-directional beamforming allow to harness both the spatial multiplexing and multi-user diversity characterizing the broadcast channel by using only limited feedback and a simple transmitter architecture. The number of necessary users with respect to the number of transmitting antennas for optimal performances is investigated as well as the fairness issue, for which the use of NOMA is shown to be advantageous w.r.t. OMA

Anwendung von maschinellem Lernen in der optischen Nachrichtenübertragungstechnik

Aufgrund des zunehmenden Datenverkehrs wird erwartet, dass die optischen Netze zukünftig mit höheren Systemkapazitäten betrieben werden. Dazu wird bspw. die kohärente Übertragung eingesetzt, bei der das Modulationsformat erhöht werden kann, erforder jedoch ein größeres SNR. Um dies zu erreichen, wird die optische Signalleistung erhöht, wodurch die Datenübertragung durch die nichtlinearen Beeinträchtigungen gestört wird. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Modellen des maschinellen Lernens, die auf diese nichtlineare Signalverschlechterung reagieren. Es wird die Support-Vector-Machine (SVM) implementiert und als klassifizierende Entscheidungsmaschine verwendet. Die Ergebnisse zeigen, dass die SVM eine verbesserte Kompensation sowohl der nichtlinearen Fasereffekte als auch der Verzerrungen der optischen Systemkomponenten ermöglicht. Das Prinzip von EONs bietet eine Technologie zur effizienten Nutzung der verfügbaren Ressourcen, die von der optischen Faser bereitgestellt werden. Ein Schlüsselelement der Technologie ist der bandbreitenvariable Transponder, der bspw. die Anpassung des Modulationsformats oder des Codierungsschemas an die aktuellen Verbindungsbedingungen ermöglicht. Um eine optimale Ressourcenauslastung zu gewährleisten wird der Einsatz von Algorithmen des Reinforcement Learnings untersucht. Die Ergebnisse zeigen, dass der RL-Algorithmus in der Lage ist, sich an unbekannte Link-Bedingungen anzupassen, während vergleichbare heuristische Ansätze wie der genetische Algorithmus für jedes Szenario neu trainiert werden müssen

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Terahertz wireless communication

The goal of this thesis is to explore Terahertz (THz) wireless communication technology. More specifically the objective is to develop and characterize several THz communication systems and study the effect of atmosphere propagation through fog droplets and dust particles on THz communications. For demonstration, a THz continuous wave (CW) photomixing system is designed. Terahertz signals are phase encoded with both analog ramp signals and pseudorandom binary data, transmitted over a short distance, and detected. The limitation of transmission bandwidth, low single to noise ratio, vibration effects are also analyzed. In order to study and compare propagation features of THz links with infrared (IR) links under different weather conditions, a THz and IR communications lab setup with a maximum data rate of 2.5 Gb/s at 625 GHz carrier frequency and 1.5 gm wavelength, have been developed respectively. A usual non return-to-zero (NRZ) format is applied to modulate the IR channel but a duobinary coding technique is used for driving the multiplier chain-based 625 GHz source, which enables signaling at high data rate and higher output power. The bit-error rate (BER), signal-to-noise ratio (SNR) and power on the receiver side have been measured, which describe the signal performance. Since weather conditions such as fog and dust exhibit a spectral dependence in the atmospheric attenuation, the corresponding impact on THz in comparison with IR communications is not equivalent. Simulation results of attenuation by fog and dust in the millimeter and sub-millimeter waveband (from 0.1 to 1 THz) and infrared waveband (1.5 µm) are presented and compared. Experimentally, after THz and IR beams propagated through the same weather conditions (fog), performance of both channels are analyzed and compared. The attenuation levels for the IR beam are typically several orders of magnitude higher than those for the THz beam. Mie scattering theory was used to study the attenuation of THz and IR radiation due to the dust particle. Different amounts of dust are loaded in the chamber to generate a variety of concentration for beam propagation. As the dust loading becomes heavier, the measured attenuation becomes more severe. Under identical dust concentrations, IR wavelengths are strongly attenuated while THz shows almost no impact

NOMA-based improper signaling for multicell MISO RIS-assisted broadcast channels

In this paper, we study the performance of reconfigurable intelligent surfaces (RISs) in a multicell broadcast channel (BC) that employs improper Gaussian signaling (IGS) jointly with non-orthogonal multiple access (NOMA) to optimize either the minimum-weighted rate or the energy efficiency (EE) of the network. We show that although the RIS can significantly improve the system performance, it cannot mitigate interference completely, so we have to employ other interference-management techniques to further improve performance. We show that the proposed NOMA-based IGS scheme can substantially outperform proper Gaussian signaling (PGS) and IGS schemes that treat interference as noise (TIN) in particular when the number of users per cell is larger than the number of base station (BS) antennas (referred to as overloaded networks). In other words, IGS and NOMA complement to each other as interference management techniques in multicell RIS-assisted BCs. Furthermore, we consider three different feasibility sets for the RIS components showing that even a RIS with a small number of elements provides considerable gains for all the feasibility sets.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Sangarapillai Lambotharan. The work of Ignacio Santamaria was supported by the Project ADELE funded by MCIN/ AEI /10.13039/501100011033, under Grant PID2019-104958RB-C43. The work of Eduard Jorswieck was supported by the Federal Ministry of Education and Research (BMBF, Germany) through the Program of Souverän. Digital. Vernetzt.” joint Project 6G-RIC, under Grants 16KISK020K and 16KISK031

Cooperative Transmission for Downlink Distributed Antenna in Time Division Duplex System

Multi-user distributed antenna system (MU-DAS) systems play the essential role in improving throughput performance in wireless communications. This improvement can be achieved by exploiting the spatial domain and without the need of additional power and bandwidth. In this thesis, three main issues which are of importance to the data rate transmission have been investigated. Firstly, user clustering in MU-DAS downlink systems has been considered, where this technique can be effciently used to reduce the complexity and cost caused by radio frequency chains, associated with antennas while keeping most of the diversity advantages of the system. The proposed user clustering algorithm which can select an optimal set of antennas for transmission. The capacity achieved by the proposed algorithm is almost same as the capacity of the optimum search method, with much lower complexity. Secondly, interference alignment in MU-DAS downlink systems has been studied. The inter-cluster interference is uncoordinated and limits the system performance. The inter-cluster interference should be eliminated or minimized carefully. The interference alignment is proposed to consolidate the strong inter-cluster interference into smaller dimensions of signal space at each user and use the remaining dimensions to transmit the desired signals without any interference. The performance of single cluster is better than the proposed algorithm due to the absence of intercluster interference in the single cluster. The numerical shows that the proposed algorithm is more suitable in multi-cell DAS environment due to the presence of inter-cell interference. Finally, the impact of different user mobility on TDD downlink MUDAS has been studied. The downlink data transmission in time division duplex (TDD) systems is optimized according to the channel state information (CSI) which is obtained at the uplink time slot. However, the actual channel at downlink time slot may be different from the estimated channel due to channel variation in mobility environment. Based on mobility state information (MSI), an autocorrelation based feedback interval adjustment technique is proposed. The proposed technique adjusts the CSI update interval and mitigates the performance degradation imposed by the user mobility and the transmission delay. Cooperative clusters are formed to maximize sum rate. In order to reduce the computational complexity, a channel gain based antenna selection and signal-to-interference plus noise ratio (SINR) based user clustering are developed. A downlink ergodic capacity is derived in single user clustering. The derived analytical expressions of the downlink ergodic capacity are verified by system simulations. Numerical results show that the proposed scheme can improved sum rate over the non cooperative system and no MSI knowledge. The proposed technique has good performance for a wide range of user speed and suitable for future wireless communications systems

Massive MIMO transmission techniques

Next generation of mobile communication systems must support astounding data traffic increases, higher data rates and lower latency, among other requirements. These requirements should be met while assuring energy efficiency for mobile devices and base stations. Several technologies are being proposed for 5G, but a consensus begins to emerge. Most likely, the future core 5G technologies will include massive MIMO (Multiple Input Multiple Output) and beamforming schemes operating in the millimeter wave spectrum. As soon as the millimeter wave propagation difficulties are overcome, the full potential of massive MIMO structures can be tapped. The present work proposes a new transmission system with bi-dimensional antenna arrays working at millimeter wave frequencies, where the multiple antenna configurations can be used to obtain very high gain and directive transmission in point to point communications. A combination of beamforming with a constellation shaping scheme is proposed, that enables good user isolation and protection against eavesdropping, while simultaneously assuring power efficient amplification of multi-level constellations

Interference as an Issue and a Resource in Wireless Networks

This dissertation will be focused on the phenomenon of interference in wireless net- works. On one hand, interference will be viewed as a negative factor that one should mitigate in order to improve the performance of a wireless network in terms of achiev- able rate, and on the other hand as an asset to increase the performance of a network in terms of security. The problems that will be investigated are, first, the character- isation of the performance of a communication network modelled as an interference channel (IC) when interference alignment (IA) is used to mitigate the interference with imperfect knowledge of the channel state, second, the characterisation of the secrecy in the Internet-of-Things (IoT) framework where some devices may use artificial noise to generate interference to potential eavesdroppers. Different scenarios will be studied in the case where interference is unwanted; the first one is when the channel error is bounded. A lower bound on the capacity achievable in this case is provided and a new performance metric namely the saturating SNR is derived. The derived lower bound is studied with respect to some parameters of the estimation strategy when using Least-Square estimation to estimate the channel ma- trices. The second scenario deals with unbounded Gaussian estimation errors, here the statistical distribution of the achievable rate is given along with a new performance metric called outage probability that simplifies the study of the IC with IA under im- perfect CSI. The results are used to optimise the network parameters and extend the analysis further to the case of cellular networks. In the wanted interference situation, the secrecy of the worst-case communication is studied and the conditions for secrecy are provided. Furthermore the average number of secure links achievable in the network is studied according to a theoretical model that is developed for the IoT case