A baseband wireless spectrum hypervisor for multiplexing concurrent OFDM signals

The next generation of wireless and mobile networks will have to handle a significant increase in traffic load compared to the current ones. This situation calls for novel ways to increase the spectral efficiency. Therefore, in this paper, we propose a wireless spectrum hypervisor architecture that abstracts a radio frequency (RF) front-end into a configurable number of virtual RF front ends. The proposed architecture has the ability to enable flexible spectrum access in existing wireless and mobile networks, which is a challenging task due to the limited spectrum programmability, i.e., the capability a system has to change the spectral properties of a given signal to fit an arbitrary frequency allocation. The proposed architecture is a non-intrusive and highly optimized wireless hypervisor that multiplexes the signals of several different and concurrent multi-carrier-based radio access technologies with numerologies that are multiple integers of one another, which are also referred in our work as radio access technologies with correlated numerology. For example, the proposed architecture can multiplex the signals of several Wi-Fi access points, several LTE base stations, several WiMAX base stations, etc. As it able to multiplex the signals of radio access technologies with correlated numerology, it can, for instance, multiplex the signals of LTE, 5G-NR and NB-IoT base stations. It abstracts a radio frequency front-end into a configurable number of virtual RF front ends, making it possible for such different technologies to share the same RF front-end and consequently reduce the costs and increasing the spectral efficiency by employing densification, once several networks share the same infrastructure or by dynamically accessing free chunks of spectrum. Therefore, the main goal of the proposed approach is to improve spectral efficiency by efficiently using vacant gaps in congested spectrum bandwidths or adopting network densification through infrastructure sharing. We demonstrate mathematically how our proposed approach works and present several simulation results proving its functionality and efficiency. Additionally, we designed and implemented an open-source and free proof of concept prototype of the proposed architecture, which can be used by researchers and developers to run experiments or extend the concept to other applications. We present several experimental results used to validate the proposed prototype. We demonstrate that the prototype can easily handle up to 12 concurrent physical layers

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Noise and distortion analysis of dual frequency comb photonic RF channelizers

Dual frequency combs are emerging as highly effective channelizers for radio frequency (RF) signal processing, showing versatile capabilities in various applications including Fourier signal mapping, analog-to-digital conversion and sub-sampling of sparse wideband signals. Although previous research has considered the impact of comb power and harmonic distortions in individual systems, a rigorous and comprehensive performance analysis is lacking, particularly regarding the impact of phase noise. This is especially important considering that phase noise power increases quadratically with comb line number. In this paper, we develop a theoretical model of a dual frequency comb channelizer and evaluate the signal to noise ratio limits and design challenges when deploying such systems in a high bandwidth signal processing context. We show that the performance of these dual comb based signal processors is limited by the relative phase noise between the two optical frequency combs, which to our knowledge has not been considered in previous literature. Our simulations verify the theoretical model and examine the stochastic noise contributions and harmonic distortion, followed by a broader discussion of the performance limits of dual frequency comb channelizers, which demonstrate the importance of minimizing the relative phase noise between the two frequency combs to achieve high signal-to-noise ratio signal processing

Development of Space-Flight Compatible Room-Temperature Electronics for the Lynx X-Ray Microcalorimeter

We are studying the development of space-flight compatible room-temperature electronics for the Lynx x-ray microcalorimeter (LXM) of the Lynx mission. The baseline readout technique for the LXM is microwave SQUID multiplexing. The key modules at room temperature are the RF electronics module and the digital electronics and event processor (DEEP). The RF module functions as frequency converters and mainly consists of local oscillators and I/Q mixers. The DEEP performs demultiplexing and event processing, and mainly consists of field-programmable gate arrays, ADCs, and DACs. We designed the RF electronics and DEEP to be flight ready, and estimated the power, size, and mass of those modules. There are two boxes each for the RF electronics and DEEP for segmentation, and the sizes of the boxes are 13 in: 13 in: 9 in: for the RF electronics and 15.5 in: 11.5 in: 9.5 in: for the DEEP. The estimated masses are 25.1 kgbox for the RF electronics box and 24.1 kgbox for the DEEP box. The maximum operating power for the RF electronics is 141 W or 70.5 Wbox, and for the DEEP box is 615 W or 308 Wbox. The overall power for those modules is 756 W. We describe the detail of the designs as well as the approaches to the estimation of resources, sizes, masses, and powers

Multichannel demultiplexer-demodulator

One of the critical satellite technologies in a meshed VSAT (very small aperture terminal) satellite communication networks utilizing FDMA (frequency division multiple access) uplinks is a multichannel demultiplexer/demodulator (MCDD). TRW Electronic Systems Group developed a proof-of-concept (POC) MCDD using advanced digital technologies. This POC model demonstrates the capability of demultiplexing and demodulating multiple low to medium data rate FDMA uplinks with potential for expansion to demultiplexing and demodulating hundreds to thousands of narrowband uplinks. The TRW approach uses baseband sampling followed by successive wideband and narrowband channelizers with each channelizer feeding into a multirate, time-shared demodulator. A full-scale MCDD would consist of an 8 bit A/D sampling at 92.16 MHz, four wideband channelizers capable of demultiplexing eight wideband channels, thirty-two narrowband channelizers capable of demultiplexing one wideband signal into 32 narrowband channels, and thirty-two multirate demodulators. The POC model consists of an 8 bit A/D sampling at 23.04 MHz, one wideband channelizer, 16 narrowband channelizers, and three multirate demodulators. The implementation loss of the wideband and narrowband channels is 0.3dB and 0.75dB at 10(exp -7) E(sub b)/N(sub o) respectively

CMB Observations with a Compact Heterogeneous 150 GHz Interferometer in Chile

We report on the design, first observing season, and analysis of data from a new prototype millimeter-wave interferometer, MINT. MINT consists of four 145 GHz SIS mixers operating in double-sideband mode in a compact heterogeneous configuration. The signal band is subdivided by a monolithic channelizer, after which the correlations between antennas are performed digitally. The typical receiver sensitivity in a 2 GHz band is 1.4 mK sqrt(s). MINT observed the cosmic microwave background (CMB) from the Chilean Altiplano. The site has a median nighttime atmospheric temperature of 9 K at zenith (exclusive of the CMB). Observations of Mars, Jupiter, and a telescope-mounted calibration source establish the system's phase and magnitude stability. MINT is the first CMB-dedicated interferometer to operate above 50 GHz. The same type of system can be used to probe the Sunyaev-Zel'dovich effect in galaxy clusters near the SZ null at 217 GHz. We present an analysis of sideband-separated, digitally sampled data recorded by the array. Based on 215 hours of data taken in late 2001, we set an upper limit on the CMB anisotropy in a band of width Delta ell=700 around ell=1540 of delta T < 105 microK (95% conf). Increased sensitivity can be achieved with more integration time, greater bandwidth, and more elements.Comment: 12 pages, 4 figures. v2: Final ApJS version; rewritten analysis section made more clea

Channelization for Multi-Standard Software-Defined Radio Base Stations

As the number of radio standards increase and spectrum resources come under more pressure, it becomes ever less efficient to reserve bands of spectrum for exclusive use by a single radio standard. Therefore, this work focuses on channelization structures compatible with spectrum sharing among multiple wireless standards and dynamic spectrum allocation in particular. A channelizer extracts independent communication channels from a wideband signal, and is one of the most computationally expensive components in a communications receiver. This work specifically focuses on non-uniform channelizers suitable for multi-standard Software-Defined Radio (SDR) base stations in general and public mobile radio base stations in particular. A comprehensive evaluation of non-uniform channelizers (existing and developed during the course of this work) shows that parallel and recombined variants of the Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB) represent the best trade-off between computational load and flexibility for dynamic spectrum allocation. Nevertheless, for base station applications (with many channels) very high filter orders may be required, making the channelizers difficult to physically implement. To mitigate this problem, multi-stage filtering techniques are applied to the GDFT-FB. It is shown that these multi-stage designs can significantly reduce the filter orders and number of operations required by the GDFT-FB. An alternative approach, applying frequency response masking techniques to the GDFT-FB prototype filter design, leads to even bigger reductions in the number of coefficients, but computational load is only reduced for oversampled configurations and then not as much as for the multi-stage designs. Both techniques render the implementation of GDFT-FB based non-uniform channelizers more practical. Finally, channelization solutions for some real-world spectrum sharing use cases are developed before some final physical implementation issues are considered

Smart antennas in software radio base stations

The application of adaptive antenna techniques to fixed-architecture base stations has been shown to offer wide-ranging benefits, including interference rejection capabilities or increased coverage and spectral efficiency. Unfortunately, the actual implementation of these techniques to mobile communication scenarios has traditionally been set back by two fundamental reasons. On one hand, the lack of flexibility of current transceiver architectures does not allow for the introduction of advanced add-on functionalities. On the other hand, the often oversimplified models for the spatiotemporal characteristics of the radio communications channel generally give rise to performance predictions that are, in practice, too optimistic. The advent of software radio architectures represents a big step toward the introduction of advanced receive/transmit capabilities. Thanks to their inherent flexibility and robustness, software radio architectures are the appropriate enabling technology for the implementation of array processing techniques. Moreover, given the exponential progression of communication standards in coexistence and their constant evolution, software reconfigurability will probably soon become the only costefficient alternative for the transceiver upgrade. This article analyzes the requirements for the introduction of software radio techniques and array processing architectures in multistandard scenarios. It basically summarizes the conclusions and results obtained within the ACTS project SUNBEAM,1 proposing algorithms and analyzing the feasibility of implementation of innovative and softwarereconfigurable array processing architectures in multistandard settings.Peer Reviewe

Back-end Design of the Readout System for Cryogenic Particle Detectors

Diese Arbeit widmet sich dem Design und der Entwicklung des digitalen Back-Ends (D-BE) für Raumtemperatur-Ausleseelektronik, die in kryogenen Quantendetektoren verwendet wird. Der Schwerpunkt liegt auf Anwendungen im Zusammenhang mit Experimenten zur Kosmischen Hintergrundstrahlung (CMB, im Englischen \textit{Cosmic Microwave Background radiation} genannt), jedoch ist die Technologie anpassbar für Partikeldetektionsexperimente. Zwei Schlüsselprojekte stehen im Mittelpunkt dieser Forschung: das QUBIC-Projekt zur Erkennung der B-Mode-Polarisation des CMB und das ECHo-Experiment, das darauf abzielt, eine neue Obergrenze für die Bestimmung der Neutrinomasse im Sub-eV-Bereich festzulegen. In diesen Projekten werden Übergangskanten-Sensoren (TES) und magnetische Mikrokalorimeter (MMCs) eingesetzt. Im Fall des QUBIC-Projekts werden die TES unter Verwendung von Zeitaufteilungsmultiplexing (TDM) gemultiplext. Es wurde jedoch ein Vorschlag für einen neuen Bolo\-meter-Typ namens Magnetischer Mikrobolometer (MMB) in der QUBIC-Kollaboration vorgestellt, der die Implementierung eines Frequenzaufteilungsmultiplexing (FDM)-Sys\-tems ermöglicht. Dies könnte durch die Verwendung eines Mikrowellen-Supraleiter-Quan\-teninterferenzgerät (SQUID)-Multiplexers ($\mu$MUX) erreicht werden, ähnlich wie bei den MMCs im ECHo-Experiment. Zur Erleichterung der Auslese der gemultiplexten Detektoren wird ein mehrtoniges Signal erzeugt, wobei jede Frequenztonkomponente einen $\mu$MUX-Kanal innerhalb des Kryostaten überwacht. Dieses Signal passiert dann einen rauscharmen Verstärker (LNA, im Englischen \textit{Low-Noise Amplifier} genannt), der in der Regel in der 4 K-Stufe liegt, bevor es das Hochfrequenz-Front-End (RF-FE) erreicht. Das RF-FE umfasst Hochfrequenzelektronik, die sowohl mit dem D-BE als auch mit der Elektronik im Kryostaten verbunden ist. Diese Arbeit stellt eine neuartige Anwendung des Goertzel-Filters zur Kanalisierung von mehrtonigen Signalen vor. Durch Simulationen, die mit einem in dieser Arbeit entwickelten auf Python basierenden Softwarepaket durchgeführt wurden, wurde die optimale Konfiguration für die Signalgenerierung und -erfassung in Bezug auf Rauschleistung, Abschirmung gegen Übersprechen und Systemlinearität ermittelt. Diese Arbeit zeigt, wie dieser Ansatz effizient in einem Field Programmable Gate Array (FPGA) implementiert werden kann, was die Skalierbarkeit bei der Auslese mehrerer Sensoren ermöglicht. Diese Skalierung is im Besonderen in Anwendungen wie Radioteleskopen für CMB-Messungen, kryogenen Kalorimetern für die Partikeldetektion und Quantencomputing entscheidend. Umfangreiche Validierungsexperimente zeigen, wie die Implementierung dieses Filtersatzes die Kanalisierung des mehrtonigen Eingangssignals zur Wiederherstellung der von den Detektoren aufgezeichneten Daten ermöglicht

DESIGN SPACE EXPLORATION FOR SIGNAL PROCESSING SYSTEMS USING LIGHTWEIGHT DATAFLOW GRAPHS

Digital signal processing (DSP) is widely used in many types of devices, including mobile phones, tablets, personal computers, and numerous forms of embedded systems. Implementation of modern DSP applications is very challenging in part due to the complex design spaces that are involved. These design spaces involve many kinds of configurable parameters associated with the signal processing algorithms that are used, as well as different ways of mapping the algorithms onto the targeted platforms. In this thesis, we develop new algorithms, software tools and design methodologies to systematically explore the complex design spaces that are involved in design and implementation of signal processing systems. To improve the efficiency of design space exploration, we develop and apply compact system level models, which are carefully formulated to concisely capture key properties of signal processing algorithms, target platforms, and algorithm-platform interactions. Throughout the thesis, we develop design methodologies and tools for integrating new compact system level models and design space exploration methods with lightweight dataflow (LWDF) techniques for design and implementation of signal processing systems. LWDF is a previously-introduced approach for integrating new forms of design space exploration and system-level optimization into design processes for DSP systems. LWDF provides a compact set of retargetable application programming interfaces (APIs) that facilitates the integration of dataflow-based models and methods. Dataflow provides an important formal foundation for advanced DSP system design, and the flexible support for dataflow in LWDF facilitates experimentation with and application of novel design methods that are founded in dataflow concepts. Our developed methodologies apply LWDF programming to facilitate their application to different types of platforms and their efficient integration with platform-based tools for hardware/software implementation. Additionally, we introduce novel extensions to LWDF to improve its utility for digital hardware design and adaptive signal processing implementation. To address the aforementioned challenges of design space exploration and system optimization, we present a systematic multiobjective optimization framework for dataflow-based architectures. This framework builds on the methodology of multiobjective evolutionary algorithms and derives key system parameters subject to time-varying and multidimensional constraints on system performance. We demonstrate the framework by applying LWDF techniques to develop a dataflow-based architecture that can be dynamically reconfigured to realize strategic configurations in the underlying parameter space based on changing operational requirements. Secondly, we apply Markov decision processes (MDPs) for design space exploration in adaptive embedded signal processing systems. We propose a framework, known as the Hierarchical MDP framework for Compact System-level Modeling (HMCSM), which embraces MDPs to enable autonomous adaptation of embedded signal processing under multidimensional constraints and optimization objectives. The framework integrates automated, MDP-based generation of optimal reconfiguration policies, dataflow-based application modeling, and implementation of embedded control software that carries out the generated reconfiguration policies. Third, we present a new methodology for design and implementation of signal processing systems that are targeted to system-on-chip (SoC) platforms. The methodology is centered on the use of LWDF concepts and methods for applying principles of dataflow design at different layers of abstraction. The development processes integrated in our approach are software implementation, hardware implementation, hardware-software co-design, and optimized application mapping. The proposed methodology facilitates development and integration of signal processing hardware and software modules that involve heterogeneous programming languages and platforms. Through three case studies involving complex applications, we demonstrate the effectiveness of the proposed contributions for compact system level design and design space exploration: a digital predistortion (DPD) system, a reconfigurable channelizer for wireless communication, and a deep neural network (DNN) for vehicle classification