Poly[bis­[μ-1-cyclo­propyl-6-fluoro-4-oxido-7-(1-piperazin­yl)-1,4-dihydro­quinoline-3-carboxyl­ato]nickel(II)]

In the title compound, [Ni(C17H17FN3O3)2]n, the NiII atom exists in a distorted trans-NiN2O4 octa­hedral geometry defined by two monodentate N-bonded and two bidentate O,O-bonded 1-cyclo­propyl-6-fluoro-4-oxido-7-(1-piperazin­yl)-1,4-dihydro­quinoline-3-carboxyl­ate (ciprofloxacinium) monoanions. The extended two-dimensional structure is a square grid. The Ni atom lies on a center of inversion

Hierarchical Radio Resource Optimization for Heterogeneous Networks with Enhanced Inter-cell Interference Coordination (eICIC)

Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multi-tier topological structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a macro BS, a resource block (RB) with blank payload is transmitted and this eliminates the interference from this macro BS to the pico BSs. We study a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABRB control. The long term controls, such as dynamic ABRB, are adaptive to the large scale fading at a RRM server for co-Tier and cross-Tier interference control. The short term control (user scheduling) is adaptive to the local channel state information within each BS to exploit the multi-user diversity. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the interference graph of the HetNet topology and derive structural properties for the optimal ABRB control. Based on that, we propose a two timescale alternative optimization solution for the user scheduling and ABRB control. The solution has low complexity and is asymptotically optimal at high SNR. Simulations show that the proposed solution has significant gain over various baselines.Comment: 14 pages, 8 figure

Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

In connected vehicular networks, it is vital to have vehicular nodes that are capable of sensing about surrounding environments and exchanging messages with each other for automating and coordinating purpose. Towards this end, integrated sensing and communication (ISAC), combining both sensing and communication systems to jointly utilize their resources and to pursue mutual benefits, emerges as a new cost-effective solution. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a general point-to-point ISAC model is proposed to account for the scenarios that the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach

Capacity-CRB Tradeoff in OFDM Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) has emerged as a key technology for future communication systems. In this paper, we provide a general framework to reveal the fundamental tradeoff between sensing and communication in OFDM systems, where a unified ISAC waveform is exploited to perform both tasks. In particular, we define the Capacity-Bayesian Cramer Rao Bound (BCRB) region in the asymptotically case when the number of subcarriers is large. Specifically, we show that the asymptotically optimal input distribution that achieves the Pareto boundary point of the Capacity-BCRB region is Gaussian and the entire Pareto boundary can be obtained by solving a convex power allocation problem. Moreover, we characterize the structure of the sensing-optimal power allocation in the asymptotically case. Finally, numerical simulations are conducted to verify the theoretical analysis and provide useful insights

Measurement to radius of Newton’s ring fringes using polar coordinate transform

Background: Newton’s ring method is often used to measure many physical parameters. And some measured physical quantity can be extracted by calculating the radius parameter of circular fringes from Newton's ring configuration. Methods: The paper presents a new measuring method for radius of circular fringes, which includes three main steps, i.e., determination of center coordinates of circular fringes, polar coordinates transformation of circular fringes, and gray projection of the transformed result which along the horizontal direction. Then the radius values of each order ring are calculated. Results: The simulated results indicate that the measuring accuracy of the radius under the effect of random noise can keep the degree of less than 0.5 pixels. Conclusions: The proposed method can obtain the radius data of each order closed circular fringes. Also, it has several other advantages, including ability of good anti-noise, sub-pixel accuracy and high reliability, and easy to in-situ use

Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations

While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness. This is especially manifested as significant degradation in performance when faced with out-of-distribution data. Recent solutions that rely on counterfactually augmented datasets show promising results, but they are inherently limited because of the lack of access to explicit causal structure. In this paper, we present an alternative approach that relies on non-counterfactual data augmentation. Our proposal instead relies on using noisy, cost-efficient data augmentations that preserve semantics associated with the target aspect. Our approach then relies on modelling invariances between different versions of the data to improve robustness. A comprehensive suite of experiments shows that our proposal significantly improves upon strong pre-trained baselines on both standard and robustness-specific datasets. Our approach further establishes a new state-of-the-art on the ABSA robustness benchmark and transfers well across domains.Comment: 10pages,1 figure,10 table

Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless Communication

A Two-stage Multiband Radar Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference

Multiband fusion is an important technique for radar sensing, which jointly utilizes measurements from multiple non-contiguous frequency bands to improve the sensing performance. In the multi-band radar sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component, which makes it difficult to obtain high-accuracy parameter estimation. To cope with this challenge, we divide the radar target parameter estimation into two stages equipped with different but equivalent signal models, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, in the coarse estimation stage, we employ a weighted root MUSIC algorithm to achieve initial estimation. Then, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar target parameters in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only the probability of each particle is optimized and the per-iteration computational complexity increases exponentially with the number of particles, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, it is shown that the proposed SPVBI can achieve a better performance than the conventional PVBI with a much smaller number of particles and per-iteration complexity. Finally, extensive simulations verify the advantage of the proposed algorithm over various baseline algorithms

1-{2-[(4-Chloro­benzyl­idene)amino]phen­yl}-3-phenyl­thio­urea

The asymmetric unit of the title compound, C20H16ClN3S, contains two independent mol­ecules, A and B. In mol­ecule A, the dihedral angles between the central benzene ring and the pendant chloro­benzene and phenyl rings are 6.37 (15) and 64.79 (15)°, respectively. The corresponding values in mol­ecule B are 28.21 (14) and 82.11 (16)°, respectively. Each mol­ecule features an intra­molecular N—H⋯N hydrogen bond, which generates an S(5) ring. In the crystal, mol­ecules A and B form dimers, being linked by two N—H⋯S hydrogen bonds with graph-set notation R 2 2(8)

2,4-Diiodo-6-[(propyl­imino)­meth­yl]phenol

The title compound, C10H11I2NO, was prepared by the reaction of 3,5-diiodo­salicyl­aldehyde with propyl­amine in ethanol. The mol­ecule adopts an E conformation with respect to the C=N bond and the aromatic ring. The aromatic ring and the imino unit are close to being coplanar, with a dihedral angle of 2.6 (3)° between their planes. This planarity is assisted by the formation of an intra­molecular O—H⋯O hydrogen bond