Precoded Chebyshev-NLMS based pre-distorter for nonlinear LED compensation in NOMA-VLC

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users' signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation (SIC). Some recent results on NOMA exist which greatly enhance the achievable capacity as compared to orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition based Chebyshev precoding to improve performance of nonlinear multiple-input multiple output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this work, which is valid even in scenarios when users experience similar channels. Additionally, in this work, analytical upper bounds for the bit error rate of the proposed detector are derived for square $M$-quadrature amplitude modulation.Comment: R. Mitra and V. Bhatia are with Indian Institute of Technology Indore, Indore-453552, India, Email:[email protected], [email protected]. This work was submitted to IEEE Transactions on Communications on October 26, 2016, decisioned on March 3, 2017, and revised on April 25, 2017, and is currently under review in IEEE Transactions on Communication

Resource allocation for 5G technologies under statistical queueing constraints

As the launch of fifth generation (5G) wireless networks is approaching, recent years have witnessed comprehensive discussions about a possible 5G standard. Many transmission scenarios and technologies have been proposed and initial over-the-air experimental trials have been conducted. Most of the existing literature studies on 5G technologies have mainly focused on the physical layer parameters and quality of service (QoS) requirements, e.g., achievable data rates. However, the demand for delay-sensitive data traffic over wireless networks has increased exponentially in the recent years, and is expected to further increase by the time of 5G. Therefore, other constraints at the data-link layer concerning the buffer overflow and delay violation probabilities should also be regarded. It follows that evaluating the performance of the 5G technologies when such constraints are considered is a timely task. Motivated by this fact, in this thesis we explore the performance of three promising 5G technologies when operating under certain QoS at the data-link layer. We follow a cross-layer approach to examine the interplay between the physical and data-link layers when statistical QoS constraints are inflicted in the form of limits on the delay violation and buffer overflow probabilities. Noting that wireless systems, generally, have limited physical resources, in this thesis we mainly target designing adaptive resource allocation schemes to maximize the system performance under such QoS constraints. We initially investigate the throughput and energy efficiency of a general class of multiple-input multiple-output (MIMO) systems with arbitrary inputs. As a cross-layer evaluation tool, we employ the effective capacity as the main performance metric, which is the maximum constant data arrival rate at a buffer that can be sustained by the channel service process under specified QoS constraints. We obtain the optimal input covariance matrix that maximizes the effective capacity under a short-term average power budget. Then, we perform an asymptotic analysis of the effective capacity in the low signal-to-noise ratio and large-scale antenna (massive MIMO) regimes. Such analysis has a practical importance for 5G scenarios that necessitate low latency, low power consumption, and/or ability to simultaneously support massive number of users. Non-orthogonal multiple access (NOMA) has attracted significant attention in the recent years as a promising multiple access technology for 5G. In this thesis, we consider a two-user power-domain NOMA scheme in which both transmitters employ superposition coding and the receiver applies successive interference cancellation (SIC) with a certain order. For practical concerns, we consider limited transmission power budgets at the transmitters, and assume that both transmitters have arbitrarily distributed input signals. We again exploit the effective capacity as the main cross-layer performance measure. We provide a resource management scheme that can jointly obtain the optimal power allocation policies at the transmitters and the optimal decoding order at the receiver, with the goal of maximizing the effective capacity region that provides the maximum allowable sustainable arrival rate region at the transmitters' buffers under QoS guarantees. In the recent years, visible light communication (VLC) has emerged as a potential transmission technology that can utilize the visible light spectrum for data transmission along with illumination. Different from the existing literature studies on VLC, in this thesis we consider a VLC system in which the access point (AP) is unaware of the channel conditions, thus the AP sends the data at a fixed rate. Under this assumption, and considering an ON-OFF data source, we provide a cross-layer study when the system is subject to statistical buffering constraints. To this end, we employ the maximum average data arrival rate at the AP buffer and the non-asymptotic bounds on buffering delay as the main performance measures. To facilitate our analysis, we adopt a two-state Markov process to model the fixed-rate transmission strategy, and we then formulate the steady-state probabilities of the channel being in the ON and OFF states. The coexistence of radio frequency (RF) and VLC systems in typical indoor environments can be leveraged to support vast user QoS needs. In this thesis, we examine the benefits of employing both technologies when operating under statistical buffering limitations. Particularly, we consider a multi-mechanism scenario that utilizes RF and VLC links for data transmission in an indoor environment. As the transmission technology is the main physical resource to be concerned in this part, we propose a link selection process through which the transmitter sends data over the link that sustains the desired QoS guarantees the most. Considering an ON-OFF data source, we employ the maximum average data arrival rate at the transmitter buffer and the non-asymptotic bounds on data buffering delay as the main performance measures. We formulate the performance measures under the assumption that both links are subject to average and peak power constraints

Fifty Years of Noise Modeling and Mitigation in Power-Line Communications.

Building on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noise modeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and non-parametric techniques employed at the receiver. Furthermore, since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC

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

Extending TDL based non-WSSUS vehicle-to-everything channel model

In den vergangenen Jahrzehnten haben drahtlose Kommunikationssysteme eine rasante Entwicklung durchgemacht und es wurden viele Untersuchungen durchgeführt, seit Maxwell die Existenz von elektromagnetischer Wellen vorausgesagt hat. In den letzten Jahren hat die Forschung im Bereich der vehicle to X (V2X)-Kommunikation stetig zugenommen. V2X beschreibt die Fähigkeit, Daten zwischen einem Fahrzeug oder vehicle (V) und “allem” zu übertragen. In Zukunft könnten Fahrzeuge mit ihrer Umgebung kommunizieren, um Verkehrsunfälle zu vermeiden und Staus zu verringern. Dazu werden sie ihr Geschwindigkeits- und Positionsdaten über Ad-hoc-Fahrzeugnetze senden und empfangen können. Um die Verkehrssicherheit zu erhöhen, ist eine zuverlässige Kommunikationsverbindung notwendig. Die größte Herausforderung bei der Fahrzeugkommunikation besteht darin, dass sich die Eigenschaften des Physical Layers aufgrund der inhärenten Mobilität innerhalb des Kanals, der hohen Fahrzeuggeschwindigkeiten, der unterschiedlichen Antennenpositionen und der vielen Handover aufgrund kleinerer Zellen schnell ändern. Dies bringt eine Reihe von Herausforderungen in Bezug auf die Kanalcharakterisierung mit sich. Es handelt sich um einen Kanal mit starker Zeitvarianz und es treten viele Übergänge auf. Somit handelt es sich um einen nicht-stationärer (non-stationary) Kanal. Das Hauptziel dieser Untersuchung ist es, eine Methode zu finden, mit der der Kanal einer komplexen Umgebung in einer einfachen Form mit weniger strengen Beziehungen zur Geometrie dargestellt werden kann. Dabei werden die statistischen Eigenschaften ähnlich der Messdaten beibehalten. In dieser Arbeit werden nichtstationäre tapped delay line (TDL)-Modelle verwendet, um vehicle to infrastructure (V2I)-Kanäle zu beschreiben. Es wird eine neue Strategie zur Extraktion von TDL-Kanalmodellparametern aus Messdaten vorgeschlagen. Dieser Ansatz basiert auf einer bestehenden Methode zur Ableitung von Parametern für ein TDLModell. Es wird gezeigt, dass mit einer anderen Methode zur Auswahl der Taps die Anzahl der Abgriffe, die zur Rekonstruktion der root mean square delay spread (RMS-DS) eines Kanals erforderlich sind, erheblich reduziert werden kann. Ein neuer Ansatz zur überprüfen der Korrektheit der Ableitung der Kanalmodellparameter wird aufgezeigt. Die Durchführbarkeit der Methode wird anhand von Channel Sounding Messungen bestätigt. In dieser Dissertation wird ein Generator zur Erzeugung von Kanalimpulsantworten entwickelt und das nichtstationäre Verhalten der Kanäle durch die Verwendung eines ON/OFF-Prozesses beschrieben. Es werden Markov-Ketten unterschiedlicher Ordnung modelliert, um das nicht-stationäre Verhalten besser zu erfassen. Die Untersuchung zeigt, dass Markov-Ketten erster Ordnung mit zwei Zuständen vorzuziehen sind, um das häufige ON/OFF-Verhalten von Mehrwegpfaden darzustellen, und dass die Markov-Modelle zweiter und dritter Ordnung keine großen Auswirkungen haben. Eine Methode zur Erweiterung eines single input single output (SISO)-TDL-Modells auf multiple input multiple output (MIMO) unter der non-wide sense stationary uncorrelated scattering (non-WSSUS)-Annahme wird eingeführt, um TDL-Kanalmodelle für V2I MIMO-Systeme zu entwickeln. Die Analyse bewertet die SISO- mit der MIMO-Konfiguration in Bezug auf die Kanalkapazität. Es werden verschiedene MIMO-Konfigurationen untersucht, und es wird gezeigt, dass die Position der Antennen eine wichtige Rolle spielt. Die Verwendung von nur vier Antennen am transmitter (Tx) und receiver (Rx), die in unterschiedliche Richtungen abstrahlen, führt zu einem qualitativen Sprung in der Leistungsfähigkeit des Systems.In the past decades, wireless communication systems have undergone rapid development, and many investigations have been done since Maxwell predicted the existence of electromagnetic waves. In recent years, vehicle to X (V2X) communication research has been growing steadily. V2X describes the ability to transmit data between a vehicle (V) and “everything”. In the future, vehicles might be able to communicate with their environment to prevent traffic accidents and reduce congestion by allowing vehicles to transmit and receive data through a vehicular ad hoc network at their speed and position. In order to achieve the ultimate goal of enhancing transportation safety, it is crucial to establish reliable communication links. The main challenge of vehicular communications introduces new properties because the physical layer properties are rapidly changing due to inherent mobility within the channel, high vehicle speeds, varying antenna positions, and many handovers due to smaller cells. This brings up a number of challenges in terms of channel characterization because it is a strong time-variant channel and many transitions occur; therefore, it is a non-stationary channel. In this thesis, non-stationary tapped delay line (TDL) models are used to describe the vehicle to infrastructure (V2I) channels. This thesis proposes a new strategy to extract TDL channel model parameters from measurement data. The proposed approach is based on an existing method to derive parameters for a TDL model. It will be shown that with a different method of choosing taps, the number of taps necessary to regenerate the root mean square delay spread (RMS-DS) of a channel can be significantly reduced. An approach is proposed to verify the correctness of the channel model parameters derivation. The feasibility of the method will be confirmed using channel-sounding measurements. This dissertation devises a generator to produce channel impulse responses (CIRs) and describes the non-stationary behavior of the channels via employing an ON/OFF process. Different order Markov chains are modeled with the aim of better capturing the non-stationary behavior. The investigation shows that first-order two-state Markov chains are preferable to represent multipath’s frequent ON/OFF behavior, and the second- and third-order Markov models do not make enormous effects. A method for extending a single input single output (SISO)-TDL model to multiple input multiple output (MIMO) under non-wide sense stationary uncorrelated scattering (non-WSSUS) assumption is introduced to develop TDL channel models for the V2I MIMO systems. The analysis evaluates SISO- with MIMO configuration in terms of channel capacity. Different MIMO configurations are explored, and it will be illustrated that the position of antennas plays an important role. Using only four antennas at the transmitter (Tx) and receiver (Rx) that radiate towards different directions will make a qualitative leap in the performance of the system

Detection and Localisation Using Light

Visible light communication (VLC) systems have become promising candidates to complement conventional radio frequency (RF) systems due to the increasingly saturated RF spectrum and the potentially high data rates that can be achieved by VLC systems. Furthermore, people detection and counting in an indoor environment has become an emerging and attractive area in the past decade. Many techniques and systems have been developed for counting in public places such as subways, bus stations and supermarkets. The outcome of these techniques can be used for public security, resource allocation and marketing decisions. This thesis presents the first indoor light-based detection and localisation system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our light detection and localisation (LiDAL) system. Our system enables active detection, counting and localisation of people, in addition to being fully compatible with existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by an optical receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate ambiguity in target detection and localisation. We develop models for the human body and its reflections and consider the impact of the colour and texture of the cloth used as well as the impact of target mobility. A number of detection and localisation methods are developed iii for our LiDAL system including cross correlation, a background subtraction method and a background estimation method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment