Searcharxiv⌕ Search

arXiv subjects

Rui Zhang

Publications and source records attributed to Rui Zhang.

At least 1,045 records · Page 58Linked to original sources
arXiv2020

Joint Power Control and Passive Beamforming in IRS-Assisted Spectrum Sharing

In cognitive radio (CR) communication systems, achieving high secondary user (SU) rate in the presence of strong cross-link interference with the primary user (PU) is challenging. In this letter, we exploit the emerging intelligent reflecting surface (IRS) technology to tackle this problem. Specifically, we investigate an IRS-assisted CR communication system where an IRS is deployed to assist in the spectrum sharing between a PU link and an SU link. We aim to maximize the achievable SU rate subject to a given signal-to-interference-plus-noise ratio target for the PU link, by jointly optimizing the SU transmit power and IRS reflect beamforming. Since the formulated problem is difficult to solve due to its non-convexity and coupled variables, we propose an efficient algorithm based on alternating optimization and successive convex approximation techniques to solve it sub-optimally, along with some heuristic designs for lower complexity. Simulation results show that IRS is able to significantly improve the SU rate, even for the scenarios deemed most challenging in conventional CR systems without using IRS.

cs.IT↗
arXiv2020

Spatial Throughput Characterization for Intelligent Reflecting Surface Aided Multiuser System

Intelligent Reflecting Surface (IRS) has been recently proposed as a promising solution to enhance the spectral and energy efficiency of future wireless networks by tuning a massive number of low-cost passive reflecting elements and thereby constructing favorable wireless propagation environment. Different from the prior works that focus on link-level performance optimization for IRS-aided wireless systems, this letter characterizes the spatial throughput of a single-cell multiuser system aided by multiple IRSs that are randomly deployed in the cell. It is shown by simulation that our analysis is valid and the IRS-aided system outperforms the full-duplex relay-aided counterpart system in terms of spatial throughput when the number of IRSs exceeds a certain value. Moreover, it is shown that different deploying strategies for IRSs/active relays should be adopted for their respective throughput maximization. Finally, it is revealed that given the total number of reflecting elements for IRSs, the system spatial throughput increases when fewer IRSs are deployed each with more reflecting elements, but at the cost of more spatially varying user rates.

cs.IT↗
arXiv2020

Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

Intelligent reflecting surface (IRS) that enables the control of the wireless propagation environment has been looked upon as a promising technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Applying this new model to an IRS-aided wireless system, we formulate a problem to maximize its achievable rate by jointly optimizing the transmit beamforming and the IRS reflect beamforming. The formulated problem is non-convex and difficult to be optimally solved in general, for which we propose a low-complexity suboptimal solution based on the alternating optimization (AO) technique. Simulation results unveil a substantial performance gain achieved by the joint beamforming optimization based on the proposed phase shift model as compared to the conventional ideal model.

eess.SP↗
arXiv2020

Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper the practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Based on the proposed model and considering an IRS-aided multiuser system with an IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) or penalty-based optimization technique. Moreover, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model.

cs.IT↗
arXiv2020

Machine-Learning Prediction for Quasi-PDF Matrix Elements

There have been rapid developments in the direct calculation in lattice QCD (LQCD) of the Bjorken-$x$ dependence of hadron structure through large-momentum effective theory (LaMET). LaMET overcomes the previous limitation of LQCD to moments (that is, integrals over Bjorken-$x$) of hadron structure, allowing LQCD to directly provide the kinematic regions where the experimental values are least known. LaMET requires large-momentum hadron states to minimize its systematics and allow us to reach small-$x$ reliably. This means that very fine lattice spacing to minimize lattice artifacts at order $(P_z a)^n$ will become crucial for next-generation LaMET-like structure calculations. Furthermore, such calculations require operators with long Wilson-link displacements (in finer lattice units), increasing the communication costs relative to that of the propagator inversion. In this work, we explore whether machine-learning (ML) algorithms can make correlator predictions to reduce the computational cost of these LQCD calculations. We consider two algorithms, gradient-boosting decision tree and linear models, applied to LaMET data, the matrix elements needed to determine the kaon and $η_s$ unpolarized parton distribution functions (PDFs), meson distribution amplitude (DA), and the nucleon gluon PDF. We find that both algorithms can reliably predict the target observables with different fit quality and systematic errors. The predictions from smaller displacement $z$ to larger ones work better than those for momentum $p$ due to the higher correlation among the data.

hep-lat↗
arXiv2020

Multi-Antenna UAV Data Harvesting: Joint Trajectory and Communication Optimization

Unmanned aerial vehicle (UAV)-enabled communication is a promising technology to extend coverage and enhance throughput for traditional terrestrial wireless communication systems. In this paper, we consider a UAV-enabled wireless sensor network (WSN), where a multi-antenna UAV is dispatched to collect data from a group of sensor nodes (SNs). The objective is to maximize the minimum data collection rate from all SNs via jointly optimizing their transmission scheduling and power allocations as well as the trajectory of the UAV, subject to the practical constraints on the maximum transmit power of the SNs and the maximum speed of the UAV. The formulated optimization problem is challenging to solve as it involves non-convex constraints and discrete-value variables. To draw useful insight, we first consider the special case of the formulated problem by ignoring the UAV speed constraint and optimally solve it based on the Lagrange duality method. It is shown that for this relaxed problem, the UAV should hover above a finite number of optimal locations with different durations in general. Next, we address the general case of the formulated problem where the UAV speed constraint is considered and propose a traveling salesman problem (TSP)-based trajectory initialization, where the UAV sequentially visits the locations obtained in the relaxed problem with minimum flying time. Given this initial trajectory, we then find the corresponding transmission scheduling and power allocations of the SNs and further optimize the UAV trajectory by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Finally, numerical results are provided to illustrate the spectrum and energy efficiency gains of the proposed scheme for multi-antenna UAV data harvesting, as compared to benchmark schemes.

cs.IT↗
arXiv2020

Nanoscale magnetic resonance imaging of proteins in a single cell

Magnetic resonance imaging (MRI) is a non-invasive and label-free technique widely used in medical diagnosis and life science research, and its success has benefited greatly from continuing efforts on enhancing contrast and resolution. Here we reported nanoscale MRI in a single cell using an atomic-size quantum sensor. With nitrogen-vacancy center in diamond, the intracellular protein ferritin has been imaged with a spatial resolution of ~ 10 nanometers, and ferritin-containing organelles were co-localized by correlative MRI and electron microscopy. Comparing to the current micrometer resolution in current state-of-art conventional MRI, our approach represents a 100-fold enhancement, and paves the way for MRI of intracellular proteins.

physics.bio-ph↗
arXiv2020

Intelligent Reflecting Surface Aided Multiple Access: Capacity Region and Deployment Strategy

Intelligent reflecting surface (IRS) is a new promising technology that is able to manipulate the wireless propagation channel via smart and controllable signal reflection. In this paper, we investigate the capacity region of a multiple access channel (MAC) with two users sending independent messages to an access point (AP), aided by $M$ IRS reflecting elements. We consider two practical IRS deployment strategies that lead to different user-AP effective channels, namely, the distributed deployment where the $M$ reflecting elements form two IRSs, each deployed in the vicinity of one user, versus the centralized deployment where all the $M$ reflecting elements are deployed in the vicinity of the AP. For the distributed deployment, we derive the capacity region in closed-form; while for the centralized deployment, we derive a capacity region outer bound and propose an efficient rate-profile based method to characterize an achievable rate region (or capacity region inner bound). Furthermore, we compare the capacity regions of the two cases and draw useful insights into the optimal deployment of IRS in practical systems.

cs.IT↗
arXiv2020

Cooperative NOMA for Downlink Asymmetric Interference Cancellation

This letter advances the non-orthogonal multiple access (NOMA) technique for cellular downlink co-channel interference mitigation, via exploiting the (limited) cooperation among base stations (BSs). Specifically, we consider a simplified but practically relevant scenario of two co-channel cells with asymmetric interference, i.e., only the user in one cell receives the strong interference from the BS in the other cell. To mitigate such interference, we propose a new cooperative NOMA scheme, where the interfered user's serving BS sends a superposed signal comprising both the desired message and the co-channel user's message (shared by the interfering BS). The co-channel user's signal is aimed to add constructively with the interfering BS's signal at the interfered user's receiver so that the combined interference with enhanced power can be effectively decoded and cancelled. This thus leads to a new problem on how to optimally allocate the transmit power for the two superposed messages. We provide the closed-form solution to this problem and investigate the conditions under which the performance of the proposed scheme is superior over the existing schemes.

cs.IT↗
arXiv2020

Differentiating Population Spatial Behavior using Representative Features of Geospatial Mobility (ReFGeM)

Understanding how humans use and consume space by comparing stratified groups, either through observation or controlled study, is key to designing better spaces, cities, and policies. GPS data traces provide detailed movement patterns of individuals but can be difficult to interpret due to the scale and scope of the data collected. For actionable insights, GPS traces are usually reduced to one or more features which express the spatial phenomenon of interest. However, it is not always clear which spatial features should be employed, and substantial effort can be invested into designing features which may or may not provide insight. In this paper we present an alternative approach: a standardized feature set with actionable interpretations that can be efficiently run against many datasets. We show that these features can distinguish between disparate human mobility patterns, although no single feature can distinguish them alone.

physics.soc-ph↗
arXiv2020

Towards Reliable UAV Swarm Communication in D2D-Enhanced Cellular Network

In the existing cellular networks, it remains a challenging problem to communicate with and control an unmanned aerial vehicle (UAV) swarm with both high reliability and low latency. Due to the UAV swarm's high working altitude and strong ground-to-air channels, it is generally exposed to multiple ground base stations (GBSs), while the GBSs that are serving ground users (occupied GBSs) can generate strong interference to the UAV swarm. To tackle this issue, we propose a novel two-phase transmission protocol by exploiting cellular plus device-to-device (D2D) communication for the UAV swarm. In Phase I, one swarm head is chosen for ground-to-air channel estimation, and all the GBSs that are not serving ground users (available GBSs) transmit a common control message to the UAV swarm simultaneously, using the same cellular frequency band, to combat the strong interference from occupied GBSs. In Phase II, all the UAVs that have decoded the common control message in Phase I further relay it to the other UAVs in the swarm via D2D communication, by exploiting the less interfered D2D frequency band and the proximity among UAVs. In this paper, we aim to characterize the reliability performance of the above two-phase protocol, i.e., the expected percentage of UAVs in the swarm that can decode the common control message, which is a non-trivial problem due to the complex system setup and the intricate coupling between the two phases. Nevertheless, we manage to obtain an approximated expression of the reliability performance of interest, under reasonable assumptions and with the aid of the Pearson distributions. Numerical results validate the accuracy of our analytical results and show the effectiveness of our protocol over other benchmark protocols. We also study the effect of key system parameters on the reliability performance, to reveal useful insights on the practical system design.

cs.IT↗
arXiv2020

Data User-Based Attribute-Based Encryption

Attribute-Based Encryption (ABE) has emerged as an information-centric public-key cryptographic system which allows a data owner to share data, according to access policy, with multiple data users based on the attributes they possess, without knowing their identities. In the original ABE schemes, a central authority administrates the system and issues secret keys to data users based on their attributes and both the owner and users need to trust a specific CA. However, in certain real-world applications, the data users would not trust anyone but themselves. For such situations, we introduce a new decentralization model of ABE, termed Data User-based ABE (DU-ABE), which is managed jointly by the data users. DU-ABE is the first decentralized ABE scheme that replaces the authorities with the data users without employing any other extra entities.

cs.CR↗
arXiv2020

DWM: A Decomposable Winograd Method for Convolution Acceleration

Winograd's minimal filtering algorithm has been widely used in Convolutional Neural Networks (CNNs) to reduce the number of multiplications for faster processing. However, it is only effective on convolutions with kernel size as 3x3 and stride as 1, because it suffers from significantly increased FLOPs and numerical accuracy problem for kernel size larger than 3x3 and fails on convolution with stride larger than 1. In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions. DWM decomposes kernels with large size or large stride to several small kernels with stride as 1 for further applying Winograd method, so that DWM can reduce the number of multiplications while keeping the numerical accuracy. It enables the fast exploring of larger kernel size and larger stride value in CNNs for high performance and accuracy and even the potential for new CNNs. Comparing against the original Winograd, the proposed DWM is able to support all kinds of convolutions with a speedup of ~2, without affecting the numerical accuracy.

cs.LG↗
arXiv2020

Optimal l-one Rank One Matrix Decompositions

In this paper we consider the decomposition of positive semidefinite matrices as a sum of rank one matrices. We introduce and investigate the properties of various measures of optimality of such decompositions. For some classes of positive semidefinite matrices we give explicitly these optimal decompositions. These classes include diagonally dominant matrices and certain of their generalizations, $2\times 2$, and a class of $3\times 3$ matrices.

math.FA↗
arXiv2020

UAV-Sensing-Assisted Cellular Interference Coordination: A Cognitive Radio Approach

Aerial-ground interference mitigation has been deemed as the main challenge in realizing cellular-connected unmanned aerial vehicle (UAV) communications. Due to the line-of-sight (LoS)-dominant air-ground channels, the UAV generates/suffers much stronger interference to/from cellular base stations (BSs) over a much larger region in its uplink/downlink communication, as compared to the terrestrial users. As a result, conventional inter-cell interference coordination (ICIC) techniques catered for terrestrial networks become ineffective in mitigating the more severe UAV-induced interference. To deal with this new challenge, this letter introduces a cognitive radio based solution by treating the UAV and terrestrial users as secondary and primary users in the network, respectively. In particular, the LoS channels with terrestrial BSs/users endow the UAV with a powerful spectrum sensing capability for detecting the terrestrial signals over a much larger region than its serving BS. By exploiting this unique feature, we propose a new UAV-sensing-assisted ICIC design for both the UAV downlink and uplink communications. Specifically, the UAV senses its received interference and the transmissions of terrestrial users in the downlink and uplink, respectively, over the resource blocks (RBs) available at its serving BS to assist its RB allocation to the UAV for avoiding the interference with co-channel terrestrial communications. Numerical results demonstrate that the proposed UAV-assisted ICIC outperforms the conventional terrestrial ICIC by engaging the neighboring BSs for cooperation only.

cs.IT↗
arXiv2020

Intelligent Reflecting Surface-Assisted Multiple Access with User Pairing: NOMA or OMA?

The integration of intelligent reflecting surface (IRS) to multiple access networks is a cost-effective solution for boosting spectrum/energy efficiency and enlarging network coverage/connections. However, due to the new capability of IRS in reconfiguring the wireless propagation channels, it is fundamentally unknown which multiple access scheme is superior in the IRS-assisted wireless network. In this letter, we pursue a theoretical performance comparison between non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in the IRS-assisted downlink communication, for which the transmit power minimization problems are formulated under the discrete unit-modulus reflection constraint on each IRS element. We analyze the minimum transmit powers required by different multiple access schemes and compare them numerically, which turn out to not fully comply with the stereotyped superiority of NOMA over OMA in conventional systems without IRS. Moreover, to avoid the exponential complexity of the brute-force search for the optimal discrete IRS phase shifts, we propose a low-complexity solution to achieve near-optimal performance.

cs.IT↗
arXiv2020

Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization

In the intelligent reflecting surface (IRS)-enhanced wireless communication system, channel state information (CSI) is of paramount importance for achieving the passive beamforming gain of IRS, which, however, is a practically challenging task due to its massive number of passive elements without transmitting/receiving capabilities. In this letter, we propose a practical transmission protocol to execute channel estimation and reflection optimization successively for an IRS-enhanced orthogonal frequency division multiplexing (OFDM) system. Under the unit-modulus constraint, a novel reflection pattern at the IRS is designed to aid the channel estimation at the access point (AP) based on the received pilot signals from the user, for which the channel estimation error is derived in closed-form. With the estimated CSI, the reflection coefficients are then optimized by a low-complexity algorithm based on the resolved strongest signal path in the time domain. Simulation results corroborate the effectiveness of the proposed channel estimation and reflection optimization methods.

cs.IT↗