pwc-archive/papers-with-abstracts huggingface dataset:
Agglomeration triggered by the effect of the number of regions: A model in NEG with a quadratic subutility
Authors: Kensuke Ohtake
Date: 2021-12-06 00:00:00

We extend the mathematical model proposed by Ottaviano-Tabuchi-Thisse (2002) to a multi-regional case and investigate the stability of the homogeneous stationary solution of the model in a one-dimensional periodic space. When the number of regions is two and three, the homogeneous stationary solution is stable under sufficiently high transport cost. On the other hand, when the number of regions is a multiple of four, the homogeneous stationary solution is unstable under any values of the transport cost.
Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from 3D magnetic resonance images
Authors: Jessica M. Bugeja, Ying Xia, Shekhar S. Chandra, Nicholas J. Murphy, Jillian Eyles, Libby Spiers, Stuart Crozier, David J. Hunter, Jurgen Fripp, Craig Engstrom
Date: 2021-12-06 00:00:00
Tasks: Segmentation

Femoroacetabular impingement (FAI) cam morphology is routinely assessed using two-dimensional alpha angles which do not provide specific data on cam size characteristics. The purpose of this study is to implement a novel, automated three-dimensional (3D) pipeline, CamMorph, for segmentation and measurement of cam volume, surface area and height from magnetic resonance (MR) images in patients with FAI. The CamMorph pipeline involves two processes: i) proximal femur segmentation using an approach integrating 3D U-net with focused shape modelling (FSM); ii) use of patient-specific anatomical information from 3D FSM to simulate healthy femoral bone models and pathological region constraints to identify cam bone mass. Agreement between manual and automated segmentation of the proximal femur was evaluated with the Dice similarity index (DSI) and surface distance measures. Independent t-tests or Mann-Whitney U rank tests were used to compare the femoral head volume, cam volume, surface area and height data between female and male patients with FAI. There was a mean DSI value of 0.964 between manual and automated segmentation of proximal femur volume. Compared to female FAI patients, male patients had a significantly larger mean femoral head volume (66.12cm3 v 46.02cm3, p<0.001). Compared to female FAI patients, male patients had a significantly larger mean cam volume (1136.87mm3 v 337.86mm3, p<0.001), surface area (657.36mm2 v 306.93mm2 , p<0.001), maximum-height (3.89mm v 2.23mm, p<0.001) and average-height (1.94mm v 1.00mm, p<0.001). Automated analyses of 3D MR images from patients with FAI using the CamMorph pipeline showed that, in comparison with female patients, male patients had significantly greater cam volume, surface area and height.
Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma
Authors: Mihir Rao
Date: 2021-11-30 00:00:00

Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance as it has a significant effect on the efficacy of Temozolomide, the primary chemotherapy treatment administered to glioblastoma patients. Currently, MGMT methylation is determined through an invasive brain biopsy and subsequent genetic analysis of the extracted tumor tissue. In this work, we present novel Bayesian classifiers that make probabilistic predictions of MGMT methylation status based on radiomic features extracted from FLAIR-sequence magnetic resonance imagery (MRIs). We implement local radiomic techniques to produce radiomic activation maps and analyze MRIs for the MGMT biomarker based on statistical features of raw voxel-intensities. We demonstrate the ability for simple Bayesian classifiers to provide a boost in predictive performance when modelling local radiomic data rather than global features. The presented techniques provide a non-invasive MRI-based approach to determining MGMT methylation status in glioblastoma patients.
On the Design of Magnetic Resonant Coupling for Wireless Power Transfer in Multi-Coil Networks
Authors: Eleni Demarchou, Constantinos Psomas, Ioannis Krikidis
Date: 2021-12-06 00:00:00

Wireless power transfer (WPT) is a promising technology for powering up distributed devices in machine type networks. Over the last decade magnetic resonant coupling (MRC) received significant interest from the research community, since it is suitable for realizing mid-range WPT. In this paper, we investigate the performance of a single cell MRC-WPT network with multiple receivers, each equipped with an electromagnetic coil and a load. We first consider pre-adjusted loads for the receivers and by taking into account spatial randomness, we derive the harvesting outage probability of a receiver; for both the strong and loosely coupling regions. Then, we develop a non-cooperative game for a fixed receiver topology, in order to acquire the optimal load which maximizes each receiver's harvested power. Throughout our work, we obtain insights for key design parameters and present numerical results which validate our analysis.
A Novel Full-Polarization SAR Images Ship Detector Based on the Scattering Mechanisms and the Wave Polarization Anisotropy
Authors: Chuan Zhang, Gui Gao, Linlin Zhang, C. Chen, S. Gao, Libo Yao, Shiquan Gou
Date: 2021-12-06 00:00:00
Tasks: Earth Observation

Synthetic aperture radar (SAR) is considered being a good option for earth observation with its unique advantages. In this paper, we proposed an adaptive ship detector using full-polarization SAR images. First, by thoroughly investigating the scattering characteristics between ships and their background, and the wave polarization anisotropy, a novel ship detector is proposed by jointing the two characteristics, named Scattering-Anisotropy joint (joint-SA). Based on the theoretical analysis, we showed that the joint-SA is an effective physical quantity to show the difference between the ship and its background, and thus joint-SA can be used for ship detection of full-polarization image data. Second, the generalized Gamma distribution was used to characterize the joint-SA statistics of sea clutter with a large range of homogeneity. As a result, an adaptive constant false alarm rate (CFAR) method was implemented based on the joint-SA. Finally, RADARSAT-2 and GF-3 data in C-band and ALOS data in L-band are used for verification. We tested on five datasets, and the experimental results verify the correctness and superiority of the constant false alarm rate (CFAR) method based on the joint-SA. In addition, the experimental results also showed that the signal-clutter ratio (SCR) of the proposed ship detector joint-SA (33.17 dB, 35.98 dB, 57.25 dB) is better than that of DBSP (8.92 dB, 3.43 dB, 25.40 dB) and RsDVH (17.28 dB, 11.17 dB, 54.55 dB). More importantly, the proposed detector joint-SA has higher detection accuracy and a lower false alarm rate.
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO
Authors: Adrian Jarret, Julien Fageot, Matthieu Simeoni
Date: 2021-12-06 00:00:00
Tasks: compressed sensing, Efficient Exploration

We propose a fast and scalable Polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. The latter improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. linear combinations of multiple atoms) update directions, hence allowing for a more efficient exploration of the search space. To preserve sparsity of the intermediate iterates, we re-optimize the LASSO problem over the set of selected atoms at each iteration. For efficiency reasons, the accuracy of this re-optimization step is relatively low for early iterations and gradually increases with the iteration count. We provide convergence guarantees for our algorithm and validate it in simulated compressed sensing setups. Our experiments reveal that P-FW outperforms state-of-the-art methods in terms of runtime, both for FW methods and optimal first-order proximal gradient methods such as the Fast Iterative Soft-Thresholding Algorithm (FISTA).
Learning Proximal Operator Methods for Massive Connectivity in IoT Networks
Authors: Yinan Zou, Yong Zhou, Yuanming Shi, Xu Chen
Date: 2021-12-06 00:00:00
Tasks: Action Detection, Activity Detection

Grant-free random access has the potential to support massive connectivity in Internet of Things (IoT) networks, where joint activity detection and channel estimation (JADCE) is a key issue that needs to be tackled. The existing methods for JADCE usually suffer from one of the following limitations: high computational complexity, ineffective in inducing sparsity, and incapable of handling complex matrix estimation. To mitigate all the aforementioned limitations, we in this paper develop an effective unfolding neural network framework built upon the proximal operator method to tackle the JADCE problem in IoT networks, where the base station is equipped with multiple antennas. Specifically, the JADCE problem is formulated as a group-sparse-matrix estimation problem, which is regularized by non-convex minimax concave penalty (MCP). This problem can be iteratively solved by using the proximal operator method, based on which we develop a unfolding neural network structure by parameterizing the algorithmic iterations. By further exploiting the coupling structure among the training parameters as well as the analytical computation, we develop two additional unfolding structures to reduce the training complexity. We prove that the proposed algorithm achieves a linear convergence rate. Results show that our proposed three unfolding structures not only achieve a faster convergence rate but also obtain a higher estimation accuracy than the baseline methods.
Modelling Quantum Channels Carrying Classical Information
Authors: Indrakshi Dey, Simon L. Cotton
Date: 2021-12-05 00:00:00

We use the concept of coupled quantum harmonic oscillators to model the propagation environment in which a quantum link carrying either classical or quantum information operates. Using the analogy between the paraxial optical wave equation and the stationary Schrodinger equation and applying the Caldirola-Kanai Hamiltonian for solving the time-dependent Schrodinger equation; we calculate the propagation field strength and the corresponding average received signal energy.
A Tensor-BTD-based Modulation for Massive Unsourced Random Access
Authors: Zhenting Luan, Yuchi Wu, Shansuo Liang, Liping Zhang, Wei Han, Bo Bai
Date: 2021-12-05 00:00:00
Tasks: Tensor Decomposition

In this letter, we propose a novel tensor-based modulation scheme for massive unsourced random access. The proposed modulation can be deemed as a summation of third-order tensors, of which the factors are representatives of subspaces. A constellation design based on high-dimensional Grassmann manifold is presented for information encoding. The uniqueness of tensor decomposition provides theoretical guarantee for active user separation. Simulation results show that our proposed method outperforms the state-of-the-art tensor-based modulation.
Enhancement of a state-of-the-art RL-based detection algorithm for Massive MIMO radars
Authors: Francesco Lisi, Stefano Fortunati, Maria Sabrina Greco, Fulvio Gini
Date: 2021-12-05 00:00:00
Tasks: Reinforcement Learning (RL)

In the present work, a reinforcement learning (RL) based adaptive algorithm to optimise the transmit beampattern for a colocated massive MIMO radar is presented. Under the massive MIMO regime, a robust Wald type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and data driven scheme for the selection of the hyper-parameters involved in the RL algorithm. Such an adaptive selection makes the Wald RL based detector independent of any ad hoc, and potentially suboptimal, manual tuning of the hyper-parameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.
C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction
Authors: Zhuoran Xiao, Zhaoyang Zhang, Chongwen Huang, Xiaoming Chen, Caijun Zhong, Mérouane Debbah
Date: 2021-12-05 00:00:00

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user's position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.
CRLB Approaching Pilot-aided Phase and Channel Estimation Algorithm in MIMO Systems with Phase Noise and Quasi-Static Channel Fading
Authors: Yiming Li, Zhouyi Hu, Andrew Ellis
Date: 2021-12-05 00:00:00

This paper derives a novel pilot-aided phase and channel estimation algorithm for multiple-input multiple-output (MIMO) systems with phase noise and quasi-static channel fading. Our novel approach allows, for the first time, carrier phase estimation and recovery to be performed before full channel estimation. This in turn enables the channel estimation to be calculated using the whole frame, significantly improving its accuracy. The proposed algorithm is a sequential combination of several linear algorithms, which greatly reduces the computational complexity. Moreover, we also derive, for the first time, the Cramer-Rao lower bound (CRLB) for a MIMO system, where phase noise is estimated using only angular information. Our numerical results show that the performance of our phase estimation algorithm is close to the proposed CRLB. Moreover, when compared with the conventional Kalman based algorithms, our proposed algorithm significantly improves the system BER performance.
Deep Open Set Identification for RF Devices
Authors: Qing Wang, Qing Liu, Zihao Zhang, HaoYu Fang, Xi Zheng
Date: 2021-12-05 00:00:00

Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process. However, existing approaches rely on the assumption that we can learn all the classes from the training set, namely, closed-set classification. To overcome the closed-set limitation, we propose a novel open set RF device identification method to classify unseen classes in the testing set. First, we design a specific convolution neural network (CNN) with a short-time Fourier transforming (STFT) pre-processing module, which efficiently recognizes the differences of feature maps learned from various RF device signals. Then to generate a representation of known class bounds, we estimate the probability map of the open-set via the OpenMax function. We conduct experiments on sampled data and voice signal sets, considering various pre-processing schemes, network structures, distance metrics, tail sizes, and openness degrees. The simulation results show the superiority of the proposed method in terms of robustness and accuracy.
Analysis of Pointing Loss Effects in Deep Space Optical Links
Authors: Lorenzo Valentini, Alberto Faedi, Enrico Paolini, Marco Chiani
Date: 2021-12-05 00:00:00

Owing to the extremely narrow beams, a main issue in optical deep space communications is represented by miss-pointing errors, which may severely degrade the system performance and availability. In this paper, we address pointing losses in the case in which both the receiver and the transmitter are affected by angular errors. Pointing losses are evaluated through two approaches. The first approach is deterministic and only requires knowledge of a maximum angular error. The second approach requires knowledge of the angular error statistical distribution and tackles the problem from an outage probability viewpoint. These tools are then applied to analyze the impact of pointing losses in deep space optical links in which both terminals suffer from miss-pointing effects. The antenna gains are first optimized to maximize the effective system gain. The optimum antenna gains are then applied to evaluate maximum achievable ranges and to perform link design by means of optical link budgets.
Double-Phase-Shifter based Hybrid Beamforming for mmWave DFRC in the Presence of Extended Target and Clutters
Authors: Ziyang Cheng, Linlong Wu, Bowen Wang, Bhavani Shankar M. R., Björn Ottersten
Date: 2021-12-05 00:00:00

In millimeter-wave (mmWave) dual-function radar-communication (DFRC) systems, hybrid beamforming (HBF) is recognized as a promising technique utilizing a limited number of radio frequency chains. In this work, in the presence of extended target and clutters, a HBF design based on the subarray connection architecture is proposed for a multiple-input multiple-output (MIMO) DFRC system. In this HBF, the double-phase-shifter (DPS) structure is embedded to further increase the design flexibility. We derive the communication spectral efficiency (SE) and radar signal-to-interference-plus-noise-ratio (SINR) with respect to the transmit HBF and radar receiver, and formulate the HBF design problem as the SE maximization subjecting to the radar SINR and power constraints. To solve the formulated nonconvex problem, the joinT Hybrid bRamforming and Radar rEceiver OptimizatioN (THEREON) is proposed, in which the radar receiver is optimized via the generalized eigenvalue decomposition, and the transmit HBF is updated with low complexity in a parallel manner using the consensus alternating direction method of multipliers (consensus-ADMM). Furthermore, we extend the proposed method to the multi-user multiple-input single-output (MU-MISO) scenario. Numerical simulations demonstrate the efficacy of the proposed algorithm and show that the solution provides a good trade-off between number of phase shifters and performance gain of the DPS HBF.
RIS-Aided D2D Communications Relying on Statistical CSI with Imperfect Hardware
Authors: Zhangjie Peng, Tianshu Li, Cunhua Pan, Hong Ren, Jiangzhou Wang
Date: 2021-12-04 00:00:00

In this letter, we investigate a reconfigurable intelligent surfaces (RIS)-aided device to device (D2D) communication system over Rician fading channels with imperfect hardware including both hardware impairment at the transceivers and phase noise at the RISs. This paper has optimized the phase shift by a genetic algorithm (GA) method to maximize the achievable rate for the continuous phase shifts (CPSs) and discrete phase shifts (DPSs). We also consider the two special cases of no RIS hardware impairments (N-RIS-HWIs) and no transceiver hardware impairments (N-T-HWIs). We present closed-form expressions for the achievable rate of different cases and study the impact of hardware impairments on the communication quality. Finally, simulation results validate the analytic work.
A Novel Two-stage Design Scheme of Equalizers for Uplink FBMC/OQAM-based Massive MIMO Systems
Authors: Yuhao Qi, Jian Dang, Zaichen Zhang, Liang Wu, Yongpeng Wu
Date: 2021-12-04 00:00:00

The self-equalization property has raised great concern in the combination of offset-quadratic-amplitude-modulation-based filter bank multi-carrier (FBMC/OQAM) and massive multiple-input multiple-output (MIMO) system, which enables to decrease the interference brought by the highly frequency-selective channels as the number of base station (BS) antennas increases. However, existing works show that there remains residual interference after single-tap equalization even with infinite number of BS antennas, leading to a limitation of achievable signal-to-interference-plus-noise ratio (SINR) performance. In this paper, we propose a two-stage design scheme of equalizers to remove the above limitation. In the first stage, we design high-rate equalizers working before FBMC demodulation to avoid the potential loss of channel information obtained at the BS. In the second stage, we transform the high-rate equalizers into the low-rate equalizers after FBMC demodulation to reduce the implementation complexity. Compared with prior works, the proposed scheme has affordable complexity under massive MIMO and only requires instantaneous channel state information (CSI) without statistical CSI and additional equalizers. Simulation results show that the scheme can bring improved SINR performance. Moreover, even with finite number of BS antennas, the interference brought by the channels can be almost eliminated.
Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems
Authors: Yanzhen Liu, Qiyu Hu, Yunlong Cai, Guanding Yu, Geoffrey Ye Li
Date: 2021-12-04 00:00:00

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.