Bandwidth (signal processing)

About: Bandwidth (signal processing) is a research topic. Over the lifetime, 48550 publications have been published within this topic receiving 600741 citations. The topic is also known as: Bandwidth (signal processing) & bandwidth.

Filtered OFDM: A new waveform for future wireless systems

Abstract: A spectrally-localized waveform is proposed based on filtered orthogonal frequency division multiplexing (f-OFDM). By allowing the filter length to exceed the cyclic prefix (CP) length of OFDM and designing the filter appropriately, the proposed f-OFDM waveform can achieve a desirable frequency localization for bandwidths as narrow as a few tens of subcarriers, while keeping the inter-symbol interference/inter-carrier interference (ISI/ICI) within an acceptable limit. Enabled by the proposed f-OFDM, an asynchronous filtered orthogonal frequency division multiple access (f-OFDMA)/filtered discrete-Fourier transform-spread OFDMA (f-DFT-S-OFDMA) scheme is introduced, which uses the spectrum shaping filter at each transmitter for side lobe leakage elimination and a bank of filters at the receiver for inter-user interference rejection. Per-user downsampling and short fast Fourier transform (FFT) are used at the receiver to ensure a reasonable complexity of implementation. The proposed scheme removes the inter-user time-synchronization overhead required in the synchronous OFDMA/DFT-S-OFDMA. The performance of the asynchronous f-OFDMA is evaluated and compared with that of the universal-filtered OFDM (UF-OFDM), proposed in [1], [2].

Analysis of time-varying signals with small BT values

Abstract: We compare four different methods for analyzing time-varying signals, the frequency and amplitude of which are both varying (small BT signals, where B is the bandwidth and T the duration). All four methods give results which depend on the frequency bandwidth of the analyzing filter. But the modified moving window method (MMWM) (Kodera et al. [10]) is the one which gives the most significant result whatever the value of the filtering bandwidth. This is demonstrated both by a mathematical treatment and by a numerical simulation. In general, there are two characteristic curves in the frequency-time plane: one which gives the instantaneous frequency as a function of time and the other which gives the group delay time as a function of frequency. For signals with small BT values, these two curves are distinct and the different analyzing methods approach one or the other curve, or neither of them, depending on the bandwidth of the filtering window. The ability of each method to resolve multicomponent signals is discussed. The influence of the scaling factors used for the visualization of the signal in a two-dimensional plane is also studied.

Plug-in bandwidth matrices for bivariate kernel density estimation

Abstract: We consider bandwidth matrix selection for bivariate kernel density estimators. The majority of work in this area has been directed towards selection of diagonal bandwidth matrices, but full bandwidth matrices can give markedly better performance for some types of target density. Our methodological contribution has been to develop a new version of the plug-in selector for full bandwidth matrices. Our approach has the advantage, in comparison to existing full bandwidth matrix plug-in techniques, that it will always produce a finite bandwidth matrix. Furthermore, it requires computation of significantly fewer pilot bandwidths. Numerical studies indicate that the performance of our bandwidth selector is best when implemented with two pilot estimation stages and applied to sphered data. In this case our methodology performs at least as well as any competing method considered, while being simpler to implement than its competitors.

A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression.

Abstract: A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. Level-dependent gain (compression), bandwidth, and phase properties were implemented with a control path that varied the gain and bandwidth of tuning in the signal-path filter. By making the bandwidth of the control path broad with respect to the signal path, the wide frequency range of two-tone suppression was included. By making the control-path filter level dependent and tuned to a frequency slightly higher than the signal-path filter, other properties of two-tone suppression were also included. These properties included the asymmetrical growth of suppression above and below the characteristic frequency and the frequency offset of the suppression tuning curve with respect to the excitatory tuning curve. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.

Cross-validation bandwidth matrices for multivariate kernel density estimation

Abstract: The performance of multivariate kernel density estimates depends crucially on the choice of bandwidth matrix, but progress towards developing good bandwidth matrix selectors has been relatively slow. In particular, previous studies of cross-validation (CV) methods have been restricted to biased and unbiased CV selection of diagonal bandwidth matrices. However, for certain types of target density the use of full (i.e. unconstrained) bandwidth matrices offers the potential for significantly improved density estimation. In this paper, we generalize earlier work from diagonal to full bandwidth matrices, and develop a smooth cross-validation (SCV) meth- odology for multivariate data. We consider optimization of the SCV technique with respect to a pilot bandwidth matrix. All the CV methods are studied using asymptotic analysis, simulation experiments and real data analysis. The results suggest that SCV for full bandwidth matrices is the most reliable of the CV methods. We also observe that experience from the univariate setting can sometimes be a misleading guide for understanding bandwidth selection in the multivariate case.