Fluid Antenna Multiple Access

Fluid antenna system represents an emerging technology that enables an antenna to switch its physical location in a predefined space. This paper explores the potential of using a single fluid antenna at each mobile user for multiple access, which we refer to it as fluid antenna multiple access (FAMA). FAMA exploits spatial moments of deep fade suffered by the interference to achieve a favourable channel condition for the desired signal, without requiring sophisticated signal processing. We analyze the FAMA network by first deriving the outage probability of the signal-to-interference ratio (SIR) in a double integral form. We then obtain an outage probability upper bound in closed form and an average outage rate lower bound for the FAMA system, with an arbitrary number of interferers, from which the multiplexing gain of FAMA is characterized. We also estimate how large the number of locations is required to achieve a given multiplexing gain using fluid antennas with a given size. Results show that it is possible for FAMA to support hundreds of users using only one fluid antenna of a few wavelengths of space at each user, giving rise to significant gain in the average network outage rate

Instrumental variables estimation of heteroskedastic linear models using all lags of instruments

estimation;linear models

Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models

Estimating a covariance matrix efficiently and discovering its structure are important statistical problems with applications in many fields. This article takes a Bayesian approach to estimate the covariance matrix of Gaussian data. We use ideas from Gaussian graphical models and model selection to construct a prior for the covariance matrix that is a mixture over all decomposable graphs, where a graph means the configuration of nonzero offdiagonal elements in the inverse of the covariance matrix. Our prior for the covariance matrix is such that the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We give empirical results that show the prior that assigns equal probability over graph sizes outperforms the prior that assigns equal probability over all graphs, both in identifying the correct decomposable graph and in more efficiently estimating the covariance matrix. The advantage is greatest when the number of observations is small relative to the dimension of the covariance matrix. The article also shows empirically that there is minimal change in statistical efficiency in using the mixture over decomposable graphs prior for estimating a general covariance compared to the Bayesian estimator by Wong et al. (2003), even when the graph of the covariance matrix is nondecomposable. However, our approach has some important advantages over that of Wong et al. (2003). Our method requires the number of decomposable graphs for each graph size. We show how to estimate these numbers using simulation and that the simulation results agree with analytic results when such results are known. We also show how to estimate the posterior distribution of the covariance matrix using Markov chain Monte Carlo with the elements of the covariance matrix integrated out and give empirical results that show the sampler is computationally efficient and converges rapidly. Finally, we note that both the prior and the simulation method to evaluate the prior apply generally to any decomposable graphical model.Covariance selection; Graphical models; Reduced conditional sampling; Variable selection

Enhancing and Localizing Surface Wave Propagation with Reconfigurable Surfaces

As an attempt to develop a reconfigurable surface architecture that can use liquid metal such as Galinstan to shape surface channels on demand, this paper considers a punctured surface where cavities are evenly distributed and can be filled with liquid metal potentially via digitally controlled pumps. In this paper, we look at the benefits of such architecture in terms of surface-wave signal enhancement and isolation, and examine how various system parameters impact the performance using full wave 3-dimensional electromagnetic simulations. It is shown that extraordinary signal shaping can be obtained.Comment: Submitted to 2021 IEEE International Symposium on Antennas and Propagation, Taipei, Taiwan,202

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging

Precise LIGO Lensing Rate Predictions for Binary Black Holes

We show how LIGO is expected to detect coalescing binary black holes at $z>1$, that are lensed by the intervening galaxy population. Gravitational magnification, $\mu$, strengthens gravitational wave signals by $\sqrt{\mu}$, without altering their frequencies, which if unrecognised leads to an underestimate of the event redshift and hence an overestimate of the binary mass. High magnifications can be reached for coalescing binaries because the region of intense gravitational wave emission during coalescence is so small ($\sim$100km), permitting very close projections between lensing caustics and gravitational-wave events. Our simulations incorporate accurate waveforms convolved with the LIGO power spectral density. Importantly, we include the detection dependence on sky position and orbital orientation, which for the LIGO configuration translates into a wide spread in observed redshifts and chirp masses. Currently we estimate a detectable rate of lensed events \rateEarly{}, that rises to \rateDesign{}, at LIGO's design sensitivity limit, depending on the high redshift rate of black hole coalescence.Comment: 5 pages, 4 figure

Fully nonlinear excitations of non-Abelian plasma

We investigate fully nonlinear, non-Abelian excitations of quark-antiquark plasma, using relativistic fluid theory in cold plasma approximation. There are mainly three important nonlinearities, coming from various sources such as non-Abelian interactions of Yang-Mills (YM) fields, Wong's color dynamics and plasma nonlinearity, in our model. By neglecting nonlinearities due to plasma and color dynamics we get back the earlier results of Blaizot {\it et. al.}, Phys. Rev. Lett. 72, 3317 (1994). Similarly, by neglecting YM fields nonlinearity and plasma nonlinearity, it reduces to the model of Gupta {\it et. al.}, Phys. Lett. B498, 223 (2005). Thus we have the most general non-Abelian mode of quark-gluon plasma (QGP). Further, our model resembles the problem of propagation of laser beam through relativistic plasma, Physica 9D, 96 (1983). in the absence of all non-Abelian interactions.Comment: 8 pages, 2 figures, articl