1,721,059 research outputs found
A Novel Approach to UWB Data Detection with Symbol-level Synchronization
Full text linkDifferentially modulated ultra-wideband (UWB) systems have recently attracted a lot of attention since they can avoid the costly channel estimation required by coherent schemes. The conventional differential-detector (DD), however, shows an inevitable 3 dB performance loss and suffers from multiple access and intersymbol interference. Multiple symbol differential detection (MSDD) provides an attractive solution that alleviates the SNR loss, but still calls for accurate timing recovery. In this paper, we show how to relax the severe timing requirements of the MSDD thereby only relying on symbol-level synchronization. Further, the detection complexity can be kept at an affordable level by pursuing a sphere decoding approach. Simulation results corroborate the effectiveness of the proposed system when operating in typical dense multipath propagation scenarios
Joint Dynamic Resource Allocation and Waveform Adaptation for Cognitive Networks
No full textThis paper investigates the issue of dynamic resource allocation (DRA) in the context of multi-user cognitive radio networks. We present a general framework adopting generalized signal expansion functions for representation of physical-layer radio resources as well as for synthesis of transmitter and receiver waveforms, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Based on the signal expansion framework, we develop noncooperative games for distributed DRA, which seek to improve the spectrum utilization on a per-user basis under both transmit power and cognitive spectral mask constraints. The proposed DRA games can handle many radio platforms such as frequency, time or code division multiplexing (FDM, TDM, CDM), and even agile platforms with combinations of different types of expansion functions. To avoid the complications of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints. Generally, the sparsity-constrained DRA approach improves convergence of distributed games at little performance loss, since the effective resources required by a cognitive radio are in fact sparse. Finally, to acquire the channel and interference parameters needed for DRA, we develop compressed sensing techniques that capitalize on the sparse properties of the wideband signals to reduce the number of samples used for sensing and hence the sensing time
Compressive sampling based differential detection for UWB impulse radio signals
Full text linkNoncoherent detectors significantly contribute to the practical realization of the ultra-wideband (UWB) impulse-radio (IR) concept, in that they allow avoiding channel estimation and provide highly efficient reception capabilities. Complexity can be reduced even further by resorting to an all-digital implementation, but Nyquist-rate sampling of the received signal is still required. The current paper addresses this issue by proposing a novel differential detection (DD) scheme, which exploits the compressive sampling (CS) framework to reduce the sampling rate much below the Nyquist-rate. The optimization problem is formulated to jointly recover the sparse received signal as well as the differentially encoded data symbols, and is compared with both the separate approach and the scheme using the compressed received signal directly, i.e., without reconstruction. Finally, a maximum a posteriori based detector using the compressed symbols is developed for a Laplacian distributed channel, as a reference to compare the performance of the proposed approaches. Simulation results show that the proposed joint CS-based DD brings the considerable advantage of reducing the sampling rate without degrading the performance, compared with the optimal MAP detector
Editorial of Special Issue on improved CDMA detection techniques for future wireless systems
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Design of a Deep Sea LiDAR System: Laser Pulse Reception and LiDAR Control Logic
No full textCurrent subsea LiDAR implementations are inherently depth limited, and make LiDAR applications in the deep-sea costly. To this end, the SLiDAR project aims to develop a pressure tolerant LiDAR system for use at any ocean depth. This thesis elaborates the high-level system design of the LiDAR system, as well as the design and implementation of the laser pulse reception stage and the onboard central control unit. Due to the short time frame of the project and the high work load, the LiDAR system as a whole and its subsystems are not tested in practise. Hence, this thesis aims to provide a basis for future development, testing and verification of both the LiDAR system, its laser reception stage, and its central control unit.SLiDARElectrical Engineering | Circuits and System
Improving Ultrafast Doppler Imaging using Subspace Tracking
No full textHigh frame rate Doppler ultrasound imaging provides a new way to image blood motion at thousands of frames per second. It has gained popularity due to its high spatio-temporal resolution, which is re- quired to distinguish blood motion from clutter signals caused by slow moving tissue. Since the flow of blood inside the brain is coupled to neural activity it is now possible to study brain function with the use of Ultrafast Doppler. This technique is called functional UltraSound (fUS), and forms a new and exciting research area. fUS relies heavily on optimized signal processing techniques to acquire and process a large amount of high frame-rate images in real-time. This thesis is about establishing the software backbone to allow for fUS experiments. Furthermore, it describes the development and implementation of a computationally efficient method of obtaining vascular images, based on the Projection Approximation Subspace Tracking (PAST) method. The PAST algorithm is able to display accurate representations of the blood subspace, while maintaining a lower computational complexity than the state-of-the-art method, making it suitable for Doppler imaging. When applied to fUS, the ex- ponentially weighted PASTd method achieves a similar performance in highlighting the functional areas of the brain as compared to the current state-of-the-art method, over multiple functional experiments, however with the benefit of lower computational complexity. These findings highlight the potential of applying PAST methods to Ultra- fast Doppler imaging.Electrical Engineering | Signals and System
Distributed wiener-based reconstruction of graph signals
No full textThis paper proposes strategies for distributed Wiener-based reconstruction of graph signals from subsampled measurements. Given a stationary signal on a graph, we fit a distributed autoregressive moving average graph filter to a Wiener graph frequency response and propose two reconstruction strategies: i) reconstruction from a single temporal snapshot; ii) recursive signal reconstruction from a stream of noisy measurements. For both strategies, a mean square error analysis is performed to highlight the role played by the filter response and the sampled nodes, and to propose a graph sampling strategy. Our findings are validated with numerical results, which illustrate the potential of the proposed algorithms for distributed reconstruction of graph signals
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