1,721,481 research outputs found
DSP algorithms for MIMO based Systems
No full textMultiple Input Multiple Output (MIMO) systems are an emerging wireless communication technology that gained popularity due to its capability to enhance spectral efficiency and reliability. Although MIMO enhances system capacity and performance, it could be challenging due to the high number of antennas at both the transmitter and receiver. It has been therefore one of the popular research areas during the last decade, meeting ever-increasing demands of data rates. Nevertheless, serving multiple terminals simultaneously is challenging due to interference among them. The main goal of this research is to mitigate interference among users, gain better energy and spectral efficiency by employing different digital signal processing (DSP) based algorithms in the multi-user MIMO communication paradigm.
Moreover, we have investigated novel algorithms in order to mitigate inter-terminal interference by employing directional beams. In order to do so, it is imperative to perform channel estimation, which can be obtained by using time or frequency duplexing, although, with an increased number of antennas in large scale MIMO (massive MIMO), the problem becomes very complicated in both types of duplexing schemes. The research problem of reducing training and feedback overhead can be addressed properly if high dimensional signals are reduced to low dimensional, by taking the compressive sensing (CS) paradigm into account. A framework is proposed to reduce training and feedback overhead by considering the MIMO channel as sparse in mobile communication. Another important issue in modern MIMO communication systems is related to phase recovery. For this purpose, a reduced complexity Kalman filtering based solution is proposed to address phase recovery problem in cross-polar interference cancellation (XPIC) system, which can be viewed as MIMO 2 x 2 channels. Another interesting application of MIMO based systems is presented for multiple implants in the intra-body network, which utilized beamforming techniques to communicate in an energy-efficient manner.
The comparison with state of the art methods is also exhibited. The research work conducted in this thesis addresses theoretical, methodological and empirical contributions to MIMO based system research problem and attempted to achieve better performance by employing different DSP based algorithms.Multiple Input Multiple Output (MIMO) systems are an emerging wireless communication technology that gained popularity due to its capability to enhance spectral efficiency and reliability. Although MIMO enhances system capacity and performance, it could be challenging due to the high number of antennas at both the transmitter and receiver. It has been therefore one of the popular research areas during the last decade, meeting ever-increasing demands of data rates. Nevertheless, serving multiple terminals simultaneously is challenging due to interference among them. The main goal of this research is to mitigate interference among users, gain better energy and spectral efficiency by employing different digital signal processing (DSP) based algorithms in the multi-user MIMO communication paradigm.
Moreover, we have investigated novel algorithms in order to mitigate inter-terminal interference by employing directional beams. In order to do so, it is imperative to perform channel estimation, which can be obtained by using time or frequency duplexing, although, with an increased number of antennas in large scale MIMO (massive MIMO), the problem becomes very complicated in both types of duplexing schemes. The research problem of reducing training and feedback overhead can be addressed properly if high dimensional signals are reduced to low dimensional, by taking the compressive sensing (CS) paradigm into account. A framework is proposed to reduce training and feedback overhead by considering the MIMO channel as sparse in mobile communication. Another important issue in modern MIMO communication systems is related to phase recovery. For this purpose, a reduced complexity Kalman filtering based solution is proposed to address the phase recovery problem in cross-polar interference cancellation (XPIC) system, which can be viewed as MIMO 2 x 2 channels. Another interesting application of MIMO based systems is presented for multiple implants in the intra-body network which utilized beamforming techniques to communicate in an energy-efficient manner.
The comparison with state of the art methods is also exhibited. The research work conducted in this thesis addresses theoretical, methodological and empirical contributions to MIMO based system research problem and attempted to achieve better performance by employing different DSP based algorithms
Preliminary Assessment of Factors Affecting Accuracy of Snow Layer Thickness Estimation Using BI-Static, Up-Looking Radars in an Avalanche Risk Assessment Context
No full textPilot Reduction Techniques for Sparse Channel Estimation in Massive MIMO Systems
No full textThe current high gain frequency division duplex
(FDD) Massive multiple-input, multiple-output (MIMO) systems
pose several challenges to carry out the downlink beamforming.
Specifically, downlink beamforming requires a channel estimation
that usually needs long training and feedback overhead, scaling
with the number of antennas at the base station (BS). We exploit
compressive sensing (CS) techniques to accurately estimate the
channel, while assuring overhead reduction which is proportional
to the sparsity level of the channel. The sparse virtual channel
representation is obtained through the proposed dictionary design,
which is more flexible, robust and able to estimate the
cell characteristics. We specifically focus on massive MIMOOrthogonal
Frequency-Division Multiplexing (OFDM) systems
that show more robustness to multipath fading, and analyze several
CS algorithms to select among them the best technique with
the proposed dictionary design. Numerical results demonstrate
that greedy solutions approach the basic pursuit bound with
lower complexity and consequent shorter training period. The
normalized hard thresholding pursuit (NHTP) technique is the
greedy algorithm with the best performance complexity trade-off
Performance Evaluation of Interactive Video Streaming over WiMAX Network
Get PDFNowadays, the desire of internet access and the need of digital encodings have influenced quite a large number of users to access high quality video application. Offering multimedia services not only to the wired but to wireless mobile client is becoming more viable. In wireless medium, video-streaming still has high resource requirements, for example, bandwidth, traffic priority, smooth play-backs. Therefore, bandwidth demands of these applications are far exceeding the capacity of 3G and Wireless Local Area Networks (LANs). The current research demonstrates the introductory understanding of the Worldwide Interoperability for Microwave Access (WiMax) network, applications, the mechanisms, its potential features, and techniques used to provide QoS in WiMAX, and lastly the network is simulated to report the diverse requirements of streamed video conferencing traffic and its specifications. For this purpose two input parameters of video traffic are selected, i.e, refresh rate, which is monitored in terms of frames per second and pixel resolutions which basically counts the number of pixels in digital imaging. The network model is developed in OPNET. Different outcomes from simulation based models are analyzed and appropriate reasons are also discussed. Apart from this, the second aim of the current research is to address whether WiMAX access technology for streaming video applications could provide comparable network performance to Asymmetric Digital Subscriber Line (ADSL). For this purpose network metrices such as End to End delay and throughput is taken into consideration for optimization.</jats:p
Speaker recognition by means of acoustic and phonetically informed GMMs
No full textIn this work we assess the recently proposed hybrid Deep Neural Network/Gaussian Mixture Model (DNN/GMM) approach for speaker recognition considering the effects of the granularity of the phonetic DNN model, and of the precision of the corresponding GMM models, which will be referred to as the phonetic GMMs. The aim of this work is to better understand the contributions of the phonetic information provided by the DNN model with respect to the accuracy of the acous tic GMMs in fitting the distribution of the features associated to a given context-dependent phone state. The testbed for this work was the text-independent speaker recognition task defined by NIST for the 2012 Speaker Recognition Evaluation. Our experiment confirms that the acoustic and the phonetic GMMs are complementary. Thus, their score combination yields very good results if the DNN is trained on data collected in an environment similar to the one that is used for testing. We show, however, that using a single Gaussian per DNN state is not the best choice: the best single system has been obtained balancing the phonetic and acoustic precision of a DNN/GMM syste
Reduced complexity Kalman filtering for phase recovery in XPIC systems
No full textReduced-complexity Kalman-based algorithms are proposed to recover the phase of cross-polar interference cancellation (XPIC) receivers in microwave radio relay links. In particular, two completely independent radio frequency (RF) transceiver chains are considered for the two different polarizations, in order to have the maximum flexibility to connect different single carrier transceivers to dual-polarized antennas. A one-state Kalman model is proposed, which is of low complexity and thus suitable for a modern higher data rates M-ary quadrature amplitude modulation (M-QAM) receiver. Moreover, a further reduced complexity version is developed that uses a lower amount of information to recover the phase at the receiver, as well as a downsampling procedure to speed up the Kalman algorithm, and an alternative error computation that is essential to ease the Kalman implementation. It is worth noting that the three last simplifications are general and can be applied not only to a one-state Kalman model. Simulation results compare the proposed simplified Kalman solutions to typical phase-locked loop (PLL) algorithms proving their comparable performance with the benefit of lower complexity. Finally, the relationships between the Extended Kalman and the PLL approaches are investigated. The obtained relation is essential for the cross-polar phase recovery, since, as far as the authors know, there are not closed-form solutions for the PLL parameter optimization in cross schemes. (C) 2018 Elsevier B.V. All rights reserved
EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder
Full text linkA Brain-Computer Interface (BCI) provides an alternative communication interface between the human brain and a computer. The Electroencephalogram (EEG) signals are acquired, processed and machine learning algorithms are further applied to extract useful information. During EEG acquisition, artifacts are induced due to involuntary eye movements or eye blink, casting adverse effects on system performance. The aim of this research is to predict eye states from EEG signals using Deep learning architectures and present improved classifier models. Recent studies reflect that Deep Neural Networks are trending state of the art Machine learning approaches. Therefore, the current work presents the implementation of Deep Belief Network (DBN) and Stacked AutoEncoders (SAE) as Classifiers with encouraging performance accuracy. One of the designed SAE models outperforms the performance of DBN and the models presented in existing research by an impressive error rate of 1.1% on the test set bearing accuracy of 98.9%. The findings in this study, may provide a contribution towards the state of the art performance on the problem of EEG eye state classification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Novel Galvanic Coupling Testbed Based on PC Sound Card for Intra-body Communication Links
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