Voice inactivity ranking for enhancement of speech on microphone arrays

Motivated by the problem of improving the performance of speech enhancement algorithms in non-stationary acoustic environments with low SNR, a framework is proposed for identifying signal frames of noisy speech that are unlikely to contain voice activity. Such voice-inactive frames can then be incorporated into an adaptation strategy to improve the performance of existing speech enhancement algorithms. This adaptive approach is applicable to single-channel as well as multi-channel algorithms for noisy speech. In both cases, the adaptive versions of the enhancement algorithms are observed to improve SNR levels by 20dB, as indicated by PESQ and WER criteria. In advanced speech enhancement algorithms, it is often of interest to identify some regions of the signal that have a high likelihood of being noise only i.e. no speech present. This is in contrast to advanced speech recognition, speaker recognition, and pitch tracking algorithms in which we are interested in identifying all regions that have a high likelihood of containing speech, as well as regions that have a high likelihood of not containing speech. In other terms, this would mean minimizing the false positive and false negative rates, respectively. In the context of speech enhancement, the identification of some speech-absent regions prompts the minimization of false positives while setting an acceptable tolerance on false negatives, as determined by the performance of the enhancement algorithm. Typically, Voice Activity Detectors (VADs) are used for identifying speech absent regions for the application of speech enhancement. In recent years a myriad of Deep Neural Network (DNN) based approaches have been proposed to improve the performance of VADs at low SNR levels by training on combinations of speech and noise. Training on such an exhaustive dataset is combinatorically explosive. For this dissertation, we propose a voice inactivity ranking framework, where the identification of voice-inactive frames is performed using a machine learning (ML) approach that only uses clean speech utterances for training and is robust to high levels of noise. In the proposed framework, input frames of noisy speech are ranked by ‘voice inactivity score’ to acquire definitely speech inactive (DSI) frame-sequences. These DSI regions serve as a noise estimate and are adaptively used by the underlying speech enhancement algorithm to enhance speech from a speech mixture. The proposed voice-inactivity ranking framework was used to perform speech enhancement in single-channel and multi-channel systems. In the context of microphone arrays, the proposed framework was used to determine parameters for spatial filtering using adaptive beamformers. We achieved an average Word Error Rate (WER) improvement of 50% at SNR levels below 0dB compared to the noisy signal, which is 7±2.5% more than the framework where state-of-the-art VAD decision was used for spatial filtering. For monaural signals, we propose a multi-frame multiband spectral-subtraction (MF-MBSS) speech enhancement system utilizing the voice inactivity framework to compute and update the noise statistics on overlapping frequency bands. The proposed MF-MBSS not only achieved an average PESQ improvement of 16% with a maximum improvement of 56% when compared to the state-of-the-art Spectral Subtraction but also a 5 ± 1.5% improvement in the Word Error Rate (WER) of the spatially filtered output signal, in non-stationary acoustic environments

High-contrast imaging with METIS

The Mid-infrared E-ELT Imager and Spectrograph (METIS) for the European Extremely Large Telescope (E-ELT) consists of diffraction-limited imagers that cover 3 to 14 microns with medium resolution (R 5000) long slit spectroscopy, and an integral field spectrograph for high spectral resolution spectroscopy (R 100,000) over the L and M bands. One of the science cases that METIS addresses is the characterization of faint circumstellar material and exoplanet companions through imaging and spectroscopy. We present our approach for high contrast imaging with METIS, covering diffraction suppression with coronagraphs, the removal of slowly changing optical aberrations with focal plane wavefront sensing, interferometric imaging with sparse aperture masks, and observing strategies for both the imagers and IFU image slicers

Design and analysis of adaptively modulated optical orthogonal frequency division multiple access multiband passive optical networks

The aim of this thesis is to explore innovative technical solutions of utilising Optical Orthogonal Frequency Division Multiplexing (OOFDM) in intensity modulation and direct detection (IMDD) based future access networks to provide multi-service capability with a minimum 1 Gb/s per user. This thesis extensively investigates and analyses the feasibility and performance of adaptively modulated optical orthogonal frequency division multiplexing multiple access passive optical networks (AMOOFDMA PONs) upstream transmission systems by numerically simulating AMOOFDMA PONs using experimentally determined parameters. OOFDM transceivers incorporating reflective semiconductor optical amplifiers (RSOAs) and distributed feedback (DFB) lasers are utilised in the transceivers and intensity modulation and direct detection (IMDD) transmission systems are also employed to achieve a low complexity, high speed and large bandwidth PON as a solution for next generation access networks. Numerical simulations has also being undertaken to improve overall AMOOFDMA PON performance and power budget by incorporating optical band-pass filters (OBPFs) at the output of optical network units (ONUs). A major challenge of making PONs spectrally efficient has been addressed in this thesis by investigating the AMOOFDMA PON with ONUs on a single upstream wavelength. The performance of the single upstream wavelength AMOOFDMA PON is compared to the multiple wavelength AMOOFDMA PON. Another major challenge in AMOOFDMA PONs namely improving system capacity has also been addressed by implementing multiband transmission in an AMOOFDMA PON. Results show that for a multiple upstream OOFDMA IMDD PON system over 25 km single mode fibre (SMF) can achieve an aggregated data rate of 11.25 Gb/s and the minimum wavelength spacing between ONUs are independent of the number of ONUs. Results also show that a single upstream wavelength AMOOFDMA IMDD PON with multiband incorporated at the ONUs can achieve a aggregated line rate of 21.25 Gb/s over 25 km SMF

DNN-Assisted Speech Enhancement Approaches Incorporating Phase Information

Speech enhancement is a widely adopted technique that removes the interferences in a corrupted speech to improve the speech quality and intelligibility. Speech enhancement methods can be implemented in either time domain or time-frequency (T-F) domain. Among various proposed methods, the time-frequency domain methods, which synthesize the enhanced speech with the estimated magnitude spectrogram and the noisy phase spectrogram, gain the most popularity in the past few decades. However, this kind of techniques tend to ignore the importance of phase processing. To overcome this problem, the thesis aims to jointly enhance the magnitude and phase spectra by means of the most recent deep neural networks (DNNs). More specifically, three major contributions are presented in this thesis. First, we present new schemes based on the basic Kalman filter (KF) to remove the background noise in the noisy speech in time domain, where the KF acts as joint estimator for both the magnitude and phase spectra of speech. A DNN-augmented basic KF is first proposed, where DNN is applied for estimating key parameters in the KF, namely the linear prediction coefficients (LPCs). By training the DNN with a large database and making use of the powerful learning ability of DNN, the proposed algorithm is able to estimate LPCs from noisy speech more accurately and robustly, leading to an improved performance as compared to traditional KF based approaches in speech enhancement. We further present a high-frequency (HF) component restoration algorithm to extenuate the degradation in the HF regions of the Kalman-filtered speech, in which the DNN-based bandwidth extension is applied to estimate the magnitude of HF component from the low-frequency (LF) counterpart. By incorporating the restoration algorithm, the enhanced speech suffers less distortion in the HF component. Moreover, we propose a hybrid speech enhancement system that exploits DNN for speech reconstruction and Kalman filtering for further denoising. Two separate networks are adopted in the estimation of magnitude spectrogram and LPCs of the clean speech, respectively. The estimated clean magnitude spectrogram is combined with the phase of the noisy speech to reconstruct the estimated clean speech. A KF with the estimated parameters is then utilized to remove the residual noise in the reconstructed speech. The proposed hybrid system takes advantages of both the DNN-based reconstruction and traditional Kalman filtering, and can work reliably in either matched or unmatched acoustic environments. Next, we incorporate the DNN-based parameter estimation scheme in two advanced KFs: subband KF and colored-noise KF. The DNN-augmented subband KF method decomposes the noisy speech into several subbands, and performs Kalman filtering to each subband speech, where the parameters of the KF are estimated by the trained DNN. The final enhanced speech is then obtained by synthesizing the enhanced subband speeches. In the DNN-augmented colored-noise KF system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the LPCs and the driving noise variances. The LPCs are obtained through training a multi-objective DNN, while the driving noise variances are obtained by solving an optimization problem aiming to minimize the difference between the modelled and observed AR spectra of the noisy speech. The colored-noise Kalman filter with DNN-estimated parameters is then applied to the noisy speech for denoising. A post-subtraction technique is adopted to further remove the residual noise in the Kalman-filtered speech. Extensive computer simulations show that the two proposed advanced KF systems achieve significant performance gains when compared to conventional Kalman filter based algorithms as well as recent DNN-based methods under both seen and unseen noise conditions. Finally, we focus on the T-F domain speech enhancement with masking technique, which aims to retain the speech dominant components and suppress the noise dominant parts of the noisy speech. We first derive a new type of mask, namely constrained ratio mask (CRM), to better control the trade-off between speech distortion and residual noise in the enhanced speech. The CRM is estimated with a trained DNN based on the input noisy feature set and is applied to the noisy magnitude spectrogram for denoising. We further extend the CRM to the complex spectrogram estimation, where the enhanced magnitude spectrogram is obtained with the CRM, while the estimated phase spectrogram is reconstructed with the noisy phase spectrogram and the phase derivatives. Performance evaluation reveals our proposed CRM outperforms several traditional masks in terms of objective metrics. Moreover, the enhanced speech resulting from the CRM based complex spectrogram estimation has a better speech quality than that obtained without using phase reconstruction

Single- and multi-microphone speech dereverberation using spectral enhancement

In speech communication systems, such as voice-controlled systems, hands-free mobile telephones, and hearing aids, the received microphone signals are degraded by room reverberation, background noise, and other interferences. This signal degradation may lead to total unintelligibility of the speech and decreases the performance of automatic speech recognition systems. In the context of this work reverberation is the process of multi-path propagation of an acoustic sound from its source to one or more microphones. The received microphone signal generally consists of a direct sound, reflections that arrive shortly after the direct sound (commonly called early reverberation), and reflections that arrive after the early reverberation (commonly called late reverberation). Reverberant speech can be described as sounding distant with noticeable echo and colouration. These detrimental perceptual effects are primarily caused by late reverberation, and generally increase with increasing distance between the source and microphone. Conversely, early reverberations tend to improve the intelligibility of speech. In combination with the direct sound it is sometimes referred to as the early speech component. Reduction of the detrimental effects of reflections is evidently of considerable practical importance, and is the focus of this dissertation. More specifically the dissertation deals with dereverberation techniques, i.e., signal processing techniques to reduce the detrimental effects of reflections. In the dissertation, novel single- and multimicrophone speech dereverberation algorithms are developed that aim at the suppression of late reverberation, i.e., at estimation of the early speech component. This is done via so-called spectral enhancement techniques that require a specific measure of the late reverberant signal. This measure, called spectral variance, can be estimated directly from the received (possibly noisy) reverberant signal(s) using a statistical reverberation model and a limited amount of a priori knowledge about the acoustic channel(s) between the source and the microphone(s). In our work an existing single-channel statistical reverberation model serves as a starting point. The model is characterized by one parameter that depends on the acoustic characteristics of the environment. We show that the spectral variance estimator that is based on this model, can only be used when the source-microphone distance is larger than the so-called critical distance. This is, crudely speaking, the distance where the direct sound power is equal to the total reflective power. A generalization of the statistical reverberation model in which the direct sound is incorporated is developed. This model requires one additional parameter that is related to the ratio between the direct sound energy and the sound energy of all reflections. The generalized model is used to derive a novel spectral variance estimator. When the novel estimator is used for dereverberation rather than the existing estimator, and the source-microphone distance is smaller than the critical distance, the dereverberation performance is significantly increased. Single-microphone systems only exploit the temporal and spectral diversity of the received signal. Reverberation, of course, also induces spatial diversity. To additionally exploit this diversity, multiple microphones must be used, and their outputs must be combined by a suitable spatial processor such as the so-called delay and sum beamformer. It is not a priori evident whether spectral enhancement is best done before or after the spatial processor. For this reason we investigate both possibilities, as well as a merge of the spatial processor and the spectral enhancement technique. An advantage of the latter option is that the spectral variance estimator can be further improved. Our experiments show that the use of multiple microphones affords a significant improvement of the perceptual speech quality. The applicability of the theory developed in this dissertation is demonstrated using a hands-free communication system. Since hands-free systems are often used in a noisy and reverberant environment, the received microphone signal does not only contain the desired signal but also interferences such as room reverberation that is caused by the desired source, background noise, and a far-end echo signal that results from a sound that is produced by the loudspeaker. Usually an acoustic echo canceller is used to cancel the far-end echo. Additionally a post-processor is used to suppress background noise and residual echo, i.e., echo which could not be cancelled by the echo canceller. In this work a novel structure and post-processor for an acoustic echo canceller are developed. The post-processor suppresses late reverberation caused by the desired source, residual echo, and background noise. The late reverberation and late residual echo are estimated using the generalized statistical reverberation model. Experimental results convincingly demonstrate the benefits of the proposed system for suppressing late reverberation, residual echo and background noise. The proposed structure and post-processor have a low computational complexity, a highly modular structure, can be seamlessly integrated into existing hands-free communication systems, and affords a significant increase of the listening comfort and speech intelligibility

Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

Characterization and design of coherent optical OFDM transmission systems based on Hartley Transform

Nowadays, due to huge deployment of optical transport networks, a continuous increase towards higher data rates up to 100 Gb/s and beyond is observed. Furthermore, an evolution of the current optical networks is forecasted, acquiring new functionalities, e.g. elastic spectrum assignment for the optical signals. The target for these new challenges in transmission is to find techniques ready to deal with a growth of demand for bandwidth continuously asked by network operators, for whom the standard systems do not meet the new functionalities while higher rates are being set up. A solution for covering all of those needs is to adapt techniques capable to deal with such enormous data rates, and ensuring the same high efficiency for long distances and mitigate the optical impairments accumulated along the transmission path. Additionally, these transmission techniques are expected to provide some degree of flexibility, in order to enhance the network flexibility. A promising technology that can fully cope with those requires is the coherent optical orthogonal frequency division multiplexing (CO-OFDM). CO-OFDM provides several advantages, namely high sensitivity and spectral efficiency, simple integration and possibility to fully recover a signal in phase, amplitude and polarization. These systems are composed by digital signal processing (DSP) blocks that easily process data and can equalize and compensate the main impairments, providing high tolerance for dispersion effects. However, CO-OFDM systems are not free from drawbacks. Their high peak-to-average power ratio (PAPR) reduce their tolerance to nonlinearities. Furthermore, CO-OFDM systems are sensitive to any frequency shift and phase offset. Hence, a constant envelope optical OFDM (CE-OFDM) is proposed for significantly reducing the PAPR and solving high sensitivity to nonlinear impairments. It consists in a phase modulated discrete multi-tone signal, which is coherently detected at the receiver side. An alternative transform, the discrete Hartley transform, is proposed to speed up calculations in the DSP and eliminate the need to have a Hermitian symmetry. The optical CE-OFDM by its unique flexibility and rate scalability turns out as a great technology applicable to different configurations, ranging from access to core networks. In case of access solutions, several cases are investigated. First, the optical CE-OFDM is applied for radio access network signals delivery by means of a wavelength division multiplexing (WDM) overlay in deployed access architecture. A decomposed radio access network is deployed over an existing standard passive optical network (PON), capable to avoid interference and cross talks with access signals between network clients. The system exhibited narrow channel spacing, while reducing losses fed into the access equipment path. Next, a full duplex 10 Gb/s bidirectional PON transmission over a single wavelength with RSOA based ONU is investigated. The key point of that system is the upstream transmission, which is achieved re-modulating the phase of a downstream intensity modulated signal after proper saturation. The reported sensitivity performances show a power budget matching the PON standards and an OSNR easy to reach on non-amplified PON. Next, a flexible metropolitan area network of up to 100km with traffic add/drop using WDM is investigated. There the narrowing effect of the optical filters is studied. Finally, an elastic upgrade of the existing Telefonica model of the Spanish national core network is proposed. For that, the transceiver architecture is proposed to be operated featuring polarization multiplexing. Respect to the existing fixed grid, the flexible approach (enabled by the CE-OFDM transceiver) results into reduced bandwidth occupancy and low OSNR requirement.Hoy en día, debido al gran despliegue de las redes de ópticas de transporte, se espera un aumento continuado hacia mayores velocidades de datos, hasta 100 Gb/s y más allá. Por otra parte, la evolución que se prevé para las redes ópticas actuales, incluye la adquisición de nuevas funcionalidades, por ejemplo, la asignación del espectro de forma elástica para las señales ópticas. Por tanto, el claro desafío en cuanto a las tecnologías de transmisión es encontrar técnicas preparadas para hacer frente a un crecimiento de la demanda de ancho de banda; demanda que continuamente se incrementa por parte de los operadores de red, para quienes los sistemas estándar no se acaban de ajustar a las nuevas funcionalidades que esperan para la red. Una solución para cubrir todas estas necesidades es la adaptación de técnicas capaces de hacer frente a estas velocidades de datos enormes, y garantizar el mismo nivel de eficiencia para las largas distancias y mitigar las deficiencias ópticas acumuladas a lo largo de la ruta de transmisión. Además, se espera que estas técnicas de transmisión puedan proporcionar cierto grado de flexibilidad, a fin de mejorar y hacer más eficiente la gestión de la red. Una tecnología prometedora que puede hacer frente a estos requisitos es lo que se llama multiplexación por división de frecuencias ortogonales, combinado con la detección óptica coherente (CO-OFDM). CO-OFDM ofrece varias ventajas, entre otras: alta sensibilidad y eficiencia espectral y, sobre todo, la posibilidad de recuperar por completo de una señal en fase, la amplitud y la polarización. Estos sistemas están compuestos por bloques de procesado de señales digitales (DSP) que permiten detectar los datos fácilmente así como también compensar las principales degradaciones, proporcionando alta tolerancia a los efectos de dispersión. Sin embargo, los sistemas CO-OFDM no están exentos de inconvenientes. Su alta relación de potencia de pico a potencia media (PAPR) reduce sensiblemente la tolerancia no linealidades. Por otra parte, los sistemas CO-OFDM son sensibles a cualquier cambio de frecuencia y desplazamiento de fase. Por tanto, se propone un sistema OFDM de envolvente constante (CE-OFDM) para reducir significativamente la PAPR y solucionar la alta sensibilidad a las degradaciones no lineales. Consiste en una señal OFDM modulada en fase, que se detecta coherentemente en el receptor. Una transformada alternativa, la transformada discreta de Hartley, se propone para acelerar los cálculos en el DSP. El sistema CE-OFDM por su flexibilidad y escalabilidad única, resulta una tecnología aplicable a diferentes escenarios, que van desde las redes de acceso hasta las redes troncales. En el caso de las soluciones de acceso, se investigan varios casos. En primer lugar, el CE-OFDM aplica para el desarrollo y soporte de datos de una red radio, reutilizando una red óptica de acceso ya desplegada. A continuación, se investiga la transmisión bidireccional dúplex a 10 Gb / s sobre una sola longitud de onda empleando un RSOA a las unidades de usuario. El punto clave de este sistema es la transmisión en sentido ascendente, que se consigue re-modulando la fase de una señal de intensidad modulada después de saturar de forma adecuada. A continuación, se estudia una red de área metropolitana flexible de hasta 100 km. Concretamente el efecto de concatenación de filtros ópticos es el objetivo de este estudio. Finalmente, se propone una actualización elástica del modelo de Telefónica I+D para la red troncal española. Por ello, se propone operar el CE-OFDM en multiplexación de polarización. Los resultados muestran que esta combinación reduce sensiblemente el empleo de ancho de banda esto como los requisitos de los enlaces transmisión, reduciendo también los costes tanto de desarrollo como de operación y mantenimiento de la red.Avui dia, a causa del gran desplegament de les xarxes de òptiques de transport, s'espera un augment continuat cap a majors velocitats de dades, fins a 100 Gb/s i més enllà. D'altra banda, l'evolució que es preveu per a les xarxes òptiques actuals, inclou l'adquisició de noves funcionalitats, per exemple, assignació de l'espectre de forma elàstica per als senyals òptics. Per tant, el clar desafiament pel que fa a les tecnologies de transmissió és trobar tècniques preparades per fer front a un creixement de la demanda d'ample de banda; demanda que contínuament es fa per part dels operadors de xarxa, per als qui els sistemes estàndard no s'acaben d'ajustar a les noves funcionalitats que esperen per a la xarxa. Una solució per a cobrir totes aquestes necessitats és l'adaptació de tècniques capaces de fer front a aquestes velocitats de dades enormes, i garantir el mateix nivell d'eficiència per a les llargues distàncies i mitigar les deficiències òptiques acumulades al llarg de la ruta de transmissió. A més, s'espera que aquestes tècniques de transmissió puguin proporcionar cert grau de flexibilitat, per tal de millorar i tornar més eficient la gestió de la xarxa. Una tecnologia prometedora que pot fer front a aquests requisits és el que s'anomena multiplexació per divisió de freqüències ortogonals, combinat amb la detecció òptica coherent (CO-OFDM). CO-OFDM ofereix diversos avantatges, entre altres: alta sensibilitat i eficiència espectral i, sobretot, la possibilitat de recuperar per complet d'una senyal en fase, l'amplitud i la polarització. Aquests sistemes estan compostos per blocs de processament de senyals digitals (DSP) que permeten detectar les dades fàcilment així com també compensar les principals degradacions, proporcionant alta tolerància pels efectes de dispersió. No obstant això, els sistemes CO-OFDM no estan exempts d'inconvenients. La seva alta relació de potència de pic a potència mitjana (PAPR) redueix sensiblement la tolerància a no linealitats. D'altra banda, els sistemes de CO-OFDM són sensibles a qualsevol canvi de freqüència i desplaçament de fase. Per tant, es proposa un sistema OFDM d'envolvent constant (CE-OFDM) per a reduir significativament la PAPR i solucionar l'alta sensibilitat a les degradacions no lineals. Consisteix en un senyal OFDM modulat en fase, que es detecta coherentment en el receptor. Una transformada alternativa, la transformada discreta d'Hartley, es proposa accelerar els càlculs en el DSP. El sistema CE-OFDM per la seva flexibilitat i escalabilitat única, resulta una tecnologia aplicable a diferents escenaris, que van des de les xarxes d'accés fins a les xarxes troncals. En el cas de les solucions d'accés, s'investiguen diversos casos. En primer lloc, el CE-OFDM s'aplica per al desplegament i suport de dades d'una xarxa radio, reutilitzant una xarxa òptica d'accés ja desplegada. A continuació, s'investiga la transmissió bidireccional dúplex a 10 Gb/s sobre una sola longitud d'ona emprant un RSOA a les unitats d'usuari. El punt clau d'aquest sistema és la transmissió en sentit ascendent, que s'aconsegueix re-modulant la fase d'un senyal d'intensitat modulada després de saturar-la de forma adequada. A continuació, s'estudia una xarxa d'àrea metropolitana flexible de fins a 100 km. Concretament l'efecte de concatenació de filtres òptics és l'objectiu d'aquest estudi. Finalment, es proposa una actualització elàstica del model de Telefónica I+D per a la xarxa troncal espanyola. Per això, es proposa operar el CE-OFDM en multiplexació de polarització. Els resultats mostren que aquesta combinació redueix sensiblement l'ocupació d'ample de banda això com també els requisits dels enllaços transmissió, reduint també els costos tant de desplegament com d'operació i manteniment de la xarxa

Near-Instantaneously Adaptive HSDPA-Style OFDM Versus MC-CDMA Transceivers for WIFI, WIMAX, and Next-Generation Cellular Systems

Burts-by-burst (BbB) adaptive high-speed downlink packet access (HSDPA) style multicarrier systems are reviewed, identifying their most critical design aspects. These systems exhibit numerous attractive features, rendering them eminently eligible for employment in next-generation wireless systems. It is argued that BbB-adaptive or symbol-by-symbol adaptive orthogonal frequency division multiplex (OFDM) modems counteract the near instantaneous channel quality variations and hence attain an increased throughput or robustness in comparison to their fixed-mode counterparts. Although they act quite differently, various diversity techniques, such as Rake receivers and space-time block coding (STBC) are also capable of mitigating the channel quality variations in their effort to reduce the bit error ratio (BER), provided that the individual antenna elements experience independent fading. By contrast, in the presence of correlated fading imposed by shadowing or time-variant multiuser interference, the benefits of space-time coding erode and it is unrealistic to expect that a fixed-mode space-time coded system remains capable of maintaining a near-constant BER

Orthogonal frequency division multiplexing multiple-input multiple-output automotive radar with novel signal processing algorithms

Advanced driver assistance systems that actively assist the driver based on environment perception achieved significant advances in recent years. Along with this development, autonomous driving became a major research topic that aims ultimately at development of fully automated, driverless vehicles. Since such applications rely on environment perception, their ever increasing sophistication imposes growing demands on environmental sensors. Specifically, the need for reliable environment sensing necessitates the development of more sophisticated, high-performance radar sensors. A further vital challenge in terms of increased radar interference arises with the growing market penetration of the vehicular radar technology. To address these challenges, in many respects novel approaches and radar concepts are required. As the modulation is one of the key factors determining the radar performance, the research of new modulation schemes for automotive radar becomes essential. A topic that emerged in the last years is the radar operating with digitally generated waveforms based on orthogonal frequency division multiplexing (OFDM). Initially, the use of OFDM for radar was motivated by the combination of radar with communication via modulation of the radar waveform with communication data. Some subsequent works studied the use of OFDM as a modulation scheme in many different radar applications - from adaptive radar processing to synthetic aperture radar. This suggests that the flexibility provided by OFDM based digital generation of radar waveforms can potentially enable novel radar concepts that are well suited for future automotive radar systems. This thesis aims to explore the perspectives of OFDM as a modulation scheme for high-performance, robust and adaptive automotive radar. To this end, novel signal processing algorithms and OFDM based radar concepts are introduced in this work. The main focus of the thesis is on high-end automotive radar applications, while the applicability for real time implementation is of primary concern. The first part of this thesis focuses on signal processing algorithms for distance-velocity estimation. As a foundation for the algorithms presented in this thesis, a novel and rigorous signal model for OFDM radar is introduced. Based on this signal model, the limitations of the state-of-the-art OFDM radar signal processing are pointed out. To overcome these limitations, we propose two novel signal processing algorithms that build upon the conventional processing and extend it by more sophisticated modeling of the radar signal. The first method named all-cell Doppler compensation (ACDC) overcomes the Doppler sensitivity problem of OFDM radar. The core idea of this algorithm is the scenario-independent correction of Doppler shifts for the entire measurement signal. Since Doppler effect is a major concern for OFDM radar and influences the radar parametrization, its complete compensation opens new perspectives for OFDM radar. It not only achieves an improved, Doppler-independent performance, it also enables more favorable system parametrization. The second distance-velocity estimation algorithm introduced in this thesis addresses the issue of range and Doppler frequency migration due to the target’s motion during the measurement. For the conventional radar signal processing, these migration effects set an upper limit on the simultaneously achievable distance and velocity resolution. The proposed method named all-cell migration compensation (ACMC) extends the underlying OFDM radar signal model to account for the target motion. As a result, the effect of migration is compensated implicitly for the entire radar measurement, which leads to an improved distance and velocity resolution. Simulations show the effectiveness of the proposed algorithms in overcoming the two major limitations of the conventional OFDM radar signal processing. As multiple-input multiple-output (MIMO) radar is a well-established technology for improving the direction-of-arrival (DOA) estimation, the second part of this work studies the multiplexing methods for OFDM radar that enable simultaneous use of multiple transmit antennas for MIMO radar processing. After discussing the drawbacks of known multiplexing methods, we introduce two advanced multiplexing schemes for OFDM-MIMO radar based on non-equidistant interleaving of OFDM subcarriers. These multiplexing approaches exploit the multicarrier structure of OFDM for generation of orthogonal waveforms that enable a simultaneous operation of multiple MIMO channels occupying the same bandwidth. The primary advantage of these methods is that despite multiplexing they maintain all original radar parameters (resolution and unambiguous range in distance and velocity) for each individual MIMO channel. To obtain favorable interleaving patterns with low sidelobes, we propose an optimization approach based on genetic algorithms. Furthermore, to overcome the drawback of increased sidelobes due to subcarrier interleaving, we study the applicability of sparse processing methods for the distance-velocity estimation from measurements of non-equidistantly interleaved OFDM-MIMO radar. We introduce a novel sparsity based frequency estimation algorithm designed for this purpose. The third topic addressed in this work is the robustness of OFDM radar to interference from other radar sensors. In this part of the work we study the interference robustness of OFDM radar and propose novel interference mitigation techniques. The first interference suppression algorithm we introduce exploits the robustness of OFDM to narrowband interference by dropping subcarriers strongly corrupted by interference from evaluation. To avoid increase of sidelobes due to missing subcarriers, their values are reconstructed from the neighboring ones based on linear prediction methods. As a further measure for increasing the interference robustness in a more universal manner, we propose the extension of OFDM radar with cognitive features. We introduce the general concept of cognitive radar that is capable of adapting to the current spectral situation for avoiding interference. Our work focuses mainly on waveform adaptation techniques; we propose adaptation methods that allow dynamic interference avoidance without affecting adversely the estimation performance. The final part of this work focuses on prototypical implementation of OFDM-MIMO radar. With the constructed prototype, the feasibility of OFDM for high-performance radar applications is demonstrated. Furthermore, based on this radar prototype the algorithms presented in this thesis are validated experimentally. The measurements confirm the applicability of the proposed algorithms and concepts for real world automotive radar implementations