1,183 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
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
Eredoctoraat prof. mr. Geert Corstens
No full textOn October 17, 2023, during the 100th Dies Natalis of Radboud University, Geert Corstens received an honorary doctorate from Radboud University. This honorary doctorate was awarded to him because of his tireless efforts for a strong, fair and equal constitutional state. This edition includes the laudation of honorary supervisor Roel Schutgens, the acceptance speech of Geert Corstens and the Van der Grint lecture given by Geert Corstens during the week of the Dies Natalis.
Geert Corstens was president of the Supreme Court of the Netherlands from 2008 to 2014. Immediately after his appointment, he was known for his assertive attitude towards the political apparatus. The independence of the constitutional state should not be threatened by the wishes and needs of politicians and other outsiders, Corstens believed. He has also made a formidable contribution to public-oriented communication and explanation of the rule of law. Corstens also wrote, among other things, the books Our constitutional state and The judge seizes power: And other misconceptions about the democratic constitutional state to promote general communication about rights and democracy in the Netherlands. Corstens is also the author of the standard work Dutch criminal procedural law and was professor of criminal law at Radboud University from 1982 to 1995
Basis expansion adaptive filters for time-varying system identification
No full textElectrical Engineering, Mathematics and Computer Scienc
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
Adaptive graph signal processing: algorithms and optimal sampling strategies
No full textThe goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and recovery strategies for (distributed) adaptive learning of signals defined over graphs
Frequency Agile Waveform Adaptation for Cognitive Radios
No full textIn the context of cognitive radio (CR) networks, this paper develops a frequency-agile waveform adaptation technique that dynamically adjusts the spectral shape, power and frequency of transmission waveforms for efficient network spectrum utilization. A general framework is presented based on generalized transmitter and receiver basis functions, which allow to jointly carry out dynamic resource allocation (DRA) and waveform adaptation, two procedures that are traditionally carried out separately. New objective function and cognitive spectral mask constraints are formulated for DRA optimization tailored to CR applications. The joint DRA and waveform adaptation approach permits distributed games in multiple access networks, in which participating CR users optimize their respective local utility functions by taking actions from the action space defined by allowable basis function parameters. It results in not only enhanced radio spectrum resource efficiency due to joint optimization, but also affordable complexity scalable in the network size, by virtue of the distributed game approach adopted
Synchronization-Free Data Detection for UWB Communications
No full textThe dense multipath scattering typical of ultra-wideband (UWB) wireless channels provides very large multipath diversity, but from the other side makes the receiver design a demanding task as far as channel estimation and timing recovery are concerned. The contribution of this paper is to derive a novel receiver structure based on the multiple symbols differential detection (MSDD) framework with particular emphasis on bypassing costly channel estimation and relaxing the stringent requirements. imposed on timing recovery. The computational complexity of the proposed detection scheme, that becomes quite impractical as the data block size increases, is then circumvented by resorting to an efficient implementation based on the sphere decoding (SD) algorithm. Simulation results carried out in typical multipath propagation scenarios verify that appealing detection performance is achieved at affordable receiver complexity
Joint Dynamic Resource Allocation and Waveform Adaptation in Cognitive Radio Networks
Full text linkThis paper discusses the issue of dynamic resource allocation (DRA) in the context of cognitive radio (CR) networks. We present a general framework adopting generalized transmitter and receiver signal-expansion functions, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Moreover, the proposed DRA can handle many types of expansion functions or even combinations of different types of functions. An iterative game approach is adopted to perform multi-player DRA, and the best-response strategies of players are derived and characterized using convex optimization. To reduce the implementation costs of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints for dynamic function selection. Generally, it incurs little rate-performance loss since the effective resources required by a CR are in fact sparse
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