ﻻ يوجد ملخص باللغة العربية
This letter analyzes the performances of a simple reconstruction method, namely the Projected Back-Projection (PBP), for estimating the direction of a sparse signal from its phase-only (or amplitude-less) complex Gaussian random measurements, i.e., an extension of one-bit compressive sensing to the complex field. To study the performances of this algorithm, we show that complex Gaussian random matrices respect, with high probability, a variant of the Restricted Isometry Property (RIP) relating to the l1 -norm of the sparse signal measurements to their l2 -norm. This property allows us to upper-bound the reconstruction error of PBP in the presence of phase noise. Monte Carlo simulations are performed to highlight the performance of our approach in this phase-only acquisition model when compared to error achieved by PBP in classical compressive sensing.
Conventional optical coherent receivers capture the full electrical field, including amplitude and phase, of a signal waveform by measuring its interference against a stable continuous-wave local oscillator (LO). In optical coherent communications, p
Leveraging recent advances in additive combinatorics, we exhibit explicit matrices satisfying the Restricted Isometry Property with better parameters. Namely, for $varepsilon=3.26cdot 10^{-7}$, large $k$ and $k^{2-varepsilon} le Nle k^{2+varepsilon}$
Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of the wireless propagation environment and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an
In this work, we present near-field image transmission and error vector magnitude measurement in a rich scattering environment in a metal enclosure. We check the effect of loading metal enclosure on the performance of SDR based near-field communicati
We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1-regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict linear speedups, up to a problem-dependent