2,614 research outputs found
Iterative Interference Cancellation for Time Reversal Division Multiple Access
Time Reversal (TR) has been proposed as a competitive precoding strategy for
low-complexity devices, relying on ultra-wideband waveforms. This transmit
processing paradigm can address the need for low power and low complexity
receivers, which is particularly important for the Internet of Things, since it
shifts most of the communications signal processing complexity to the
transmitter side. Due to its spatio-temporal focusing property, TR has also
been used to design multiple access schemes for multi-user communications
scenarios. However, in wideband time-division multiple access schemes, the
signals received by users suffer from significant levels of inter-symbol
interference as well as interference from uncoordinated users, which often
require additional processing at the receiver side. This paper proposes an
iterative TR scheme that aims to reduce the level of interference in wideband
multi-user settings, while keeping the processing complexity only at the
transmitter side. The performance of the proposed TR-based protocol is
evaluated using analytical derivations. In addition, its superiority over the
conventional Time Reversal Division Multiple Access (TRDMA) scheme is
demonstrated through simulations as well as experimental measurements at
GHz carrier frequency with variable bandwidth values.Comment: 7 pages, 8 figures, published in an IEEE Journa
Performance evaluation of non-prefiltering vs. time reversal prefiltering in distributed and uncoordinated IR-UWB ad-hoc networks
Time Reversal (TR) is a prefiltering scheme mostly analyzed in the context of centralized and synchronous IR-UWB networks, in order to leverage the trade-off between communication performance and device complexity, in particular in presence of multiuser interference. Several strong assumptions have been typically adopted in the analysis of TR, such as the absence of Inter-Symbol / Inter-Frame Interference (ISI/IFI) and multipath dispersion due to complex signal propagation. This work has the main goal of comparing the performance of TR-based systems with traditional non-prefiltered schemes, in the novel context of a distributed and uncoordinated IR-UWB network, under more realistic assumptions including the presence of ISI/IFI and multipath dispersion. Results show that, lack of power control and imperfect channel knowledge affect the performance of both non-prefiltered and TR systems; in these conditions, TR prefiltering still guarantees a performance improvement in sparse/low-loaded and overloaded network scenarios, while the opposite is true for less extreme scenarios, calling for the developement of an adaptive scheme that enables/disables TR prefiltering depending on network conditions
Time-Reversal Routing for Dispersion Code Multiple Access (DCMA) Communications
We present the modeling and characterization of a time-reversal routing
dispersion code multiple access (TR-DCMA) system. We show that this system
maintains the low complexity advantage of DCMA transceivers while offering
dynamic adaptivity for practial communication scenarios. We first derive the
mathematical model and explain operation principles of the system, and then
characterize its interference, signal to interference ratio, and bit error
probability characteristics
Numerical Study on Indoor Wideband Channel Characteristics with Different Internal Wall
Effects of material and configuration of the internal wall on the performance of wideband channel are investigated by using the Finite Difference Time-Domain (FDTD) method. The indoor wideband channel characteristics, such as the path-loss, Root-Mean-Square (RMS) delay spread and number of the multipath components (MPCs), are presented. The simulated results demonstrate that the path-loss and MPCs are affected by the permittivity, dielectric loss tangent and thickness of the internal wall, while the RMS delay spread is almost not relevant with the dielectric permittivity. Furthermore, the comparison of simulated result with the measured one in a simple scenario has validated the simulation study
Limiting Performance of Conventional and Widely Linear DFT-precoded-OFDM Receivers in Wideband Frequency Selective Channels
This paper describes the limiting behavior of linear and decision feedback
equalizers (DFEs) in single/multiple antenna systems employing
real/complex-valued modulation alphabets. The wideband frequency selective
channel is modeled using a Rayleigh fading channel model with infinite number
of time domain channel taps. Using this model, we show that the considered
equalizers offer a fixed post signal-to-noise-ratio (post-SNR) at the equalizer
output that is close to the matched filter bound (MFB). General expressions for
the post-SNR are obtained for zero-forcing (ZF) based conventional receivers as
well as for the case of receivers employing widely linear (WL) processing.
Simulation is used to study the bit error rate (BER) performance of both MMSE
and ZF based receivers. Results show that the considered receivers
advantageously exploit the rich frequency selective channel to mitigate both
fading and inter-symbol-interference (ISI) while offering a performance
comparable to the MFB
Range-Point Migration-Based Image Expansion Method Exploiting Fully Polarimetric Data for UWB Short-Range Radar
Ultrawideband radar with high-range resolution is a promising technology for use in short-range 3-D imaging applications, in which optical cameras are not applicable. One of the most efficient 3-D imaging methods is the range-point migration (RPM) method, which has a definite advantage for the synthetic aperture radar approach in terms of computational burden, high accuracy, and high spatial resolution. However, if an insufficient aperture size or angle is provided, these kinds of methods cannot reconstruct the whole target structure due to the absence of reflection signals from large part of target surface. To expand the 3-D image obtained by RPM, this paper proposes an image expansion method by incorporating the RPM feature and fully polarimetric data-based machine learning approach. Following ellipsoid-based scattering analysis and learning with a neural network, this method expresses the target image as an aggregation of parts of ellipsoids, which significantly expands the original image by the RPM method without sacrificing the reconstruction accuracy. The results of numerical simulation based on 3-D finite-difference time-domain analysis verify the effectiveness of our proposed method, in terms of image-expansion criteria
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