Joint Beamforming Design for RIS-enabled Integrated Positioning and Communication in Millimeter Wave Systems

Integrated positioning and communication (IPAC) system and reconfigurable intelligent surface (RIS) are both considered to be key technologies for future wireless networks. Therefore, in this paper, we propose a RIS-enabled IPAC scheme with the millimeter wave system. First, we derive the explicit expressions of the time-of-arrival (ToA)-based Cram\'er-Rao bound (CRB) and positioning error bound (PEB) for the RIS-aided system as the positioning metrics. Then, we formulate the IPAC system by jointly optimizing active beamforming in the base station (BS) and passive beamforming in the RIS to minimize the transmit power, while satisfying the communication data rate and PEB constraints. Finally, we propose an efficient two-stage algorithm to solve the optimization problem based on a series of methods such as the exhaustive search and semidefinite relaxation (SDR). Simulation results show that by changing various critical system parameters, the proposed RIS-enabled IPAC system can cater to both reliable data rates and high-precision positioning in different transmission environments

Three-dimensional structure of the milky way dust: modeling of LAMOST data

We present a three-dimensional modeling of the Milky Way dust distribution by fitting the value-added star catalog of LAMOST spectral survey. The global dust distribution can be described by an exponential disk with scale-length of 3,192 pc and scale height of 103 pc. In this modeling, the Sun is located above the dust disk with a vertical distance of 23 pc. Besides the global smooth structure, two substructures around the solar position are also identified. The one located at $150^{\circ}<l<200^{\circ}$ and $-5^{\circ}<b<-30^{\circ}$ is consistent with the Gould Belt model of \citet{Gontcharov2009}, and the other one located at $140^{\circ}<l<165^{\circ}$ and $0^{\circ}<b<15^{\circ}$ is associated with the Camelopardalis molecular clouds.Comment: 15 pages, 6 figure, accepted by Ap

Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors

Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the performance metric for location sensing. Then, we analyze the impact of the location sensing error (LSE) on communications, revealing the inherent coupling relationship between communication and sensing. Moreover, we formulate three robust communication and sensing power allocation problems by respectively characterizing the LSE as an ellipsoidal distributed model, a Gaussian distributed model, and an arbitrary distributed model. Notably, the optimization problems seek to minimize the CRB, subject to data rate and total power constraints. However, these problems are non-convex and intractable. To address the challenges related to the three aforementioned LSE models, we respectively propose to use the ${\cal{S}}$-Procedure and alternating optimization (${\cal{S}}$-AO) method, Bernstein-type inequality and successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and AO (CVaR-AO) method to solve these problems. Finally, simulation results demonstrate the robustness of our proposed UAV-aided ISAC system against the LSE by comparing with the non-robust design, and evaluate the trade-off between communication and sensing in the ISAC system

Scaling law for three-body collisions near a narrow s-wave Feshbach resonance

Ultracold atomic gases provide a controllable system to study the inelastic processes for three-body systems, where the three-body recombination rate depends on the scattering length scaling. Such scalings have been confirmed in bosonic systems with various interaction strengths, but their existence with fermionic atoms remains elusive. In this work, we report on an experimental investigation of the scaling law for the three-body atomic loss rate $L_3$ in a two-component $^6$Li Fermi gas with the scattering length $a<0$. The scaling law is validated within a certain range of $a$ near the narrow $s$-wave Feshbach resonance, where $L_3\propto T|a|^{2.60(5)}$, and $T$ is the gas temperature. The scaling law is observed to have an upper and a lower bound in terms of the scattering length. For the upper bound, when $a\rightarrow \infty$, the power-law scaling is suppressed by the unitary behavior of the resonance caused by the strong three-body collisions. For the lower bound, $a\rightarrow 0$, the finite range effect modifies the scaling law by the effective scattering length $L_e$. These results indicate that the three-body recombination rate in a fermionic system could be characterized by the scaling law associated with the generalized Efimov physics.Comment: 11 pages, 3 figures, 1 tabl

High-density lipoprotein subclass and particle size in coronary heart disease patients with or without diabetes

BACKGROUND: A higher prevalence of coronary heart disease (CHD) in people with diabetes. We investigated the high-density lipoprotein (HDL) subclass profiles and alterations of particle size in CHD patients with diabetes or without diabetes. METHODS: Plasma HDL subclasses were quantified in CHD by 1-dimensional gel electrophoresis coupled with immunodetection. RESULTS: Although the particle size of HDL tend to small, the mean levels of low density lipoprotein cholesterol(LDL-C) and total cholesterol (TC) have achieved normal or desirable for CHD patients with or without diabetes who administered statins therapy. Fasting plasma glucose (FPG), triglyceride (TG), TC, LDL-C concentrations, and HDL(3) (HDL(3b) and (3a)) contents along with Gensini Score were significantly higher; but those of HDL-C, HDL(2b+preβ2), and HDL(2a) were significantly lower in CHD patients with diabetes versus CHD patients without diabetes; The preβ(1)-HDL contents did not differ significantly between these groups. Multivariate regression analysis revealed that Gensini Score was significantly and independently predicted by HDL(2a), and HDL(2b+preβ2). CONCLUSIONS: The abnormality of HDL subpopulations distribution and particle size may contribute to CHD risk in diabetes patients. The HDL subclasses distribution may help in severity of coronary artery and risk stratification, especially in CHD patients with therapeutic LDL, TG and HDL levels

Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

In this paper, we investigate the design and implementation of machine learning (ML) based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN based demodulator contains three restricted Boltzmann machines (RBMs) to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k-nearest neighbor (KNN) algorithm. These three demodulators are trained and tested by our online open dataset. Experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance