arXiv subjects

Zhaoyang Zhang

Publications and source records attributed to Zhaoyang Zhang.

At least 271 records · Page 15Linked to original sources

arXiv2020

Convolutional Polar Coded Modulation

$2^m$-ary modulation creates $m$ bit channels which are neither independent nor identical, and this causes problems when applying polar coding because polar codes are designed for independent identical channels. Different from the existing multi-level coding (MLC) and bit-interleaved coded modulation (BICM) schemes, this paper provides a convolutional polar coded modulation (CPCM) method that preserves the low-complexity nature of BICM while offering improved spectral efficiency. Numerical results are given to show the good performance of the proposed method.

cs.IT↗

arXiv2020

Deep Reinforcement Learning for Joint Beamwidth and Power Optimization in mmWave Systems

This paper studies the joint beamwidth and transmit power optimization problem in millimeter wave communication systems. A deep reinforcement learning based approach is proposed. Specifically, a customized deep Q network is trained offline, which is able to make real-time decisions when deployed online. Simulation results show that the proposed approach significantly outperforms conventional approaches in terms of both performance and complexity. Besides, strong generalization ability to different system parameters is also demonstrated, which further enhances the practicality of the proposed approach.

cs.IT↗

arXiv2020

Robust Design for Intelligent Reflecting Surfaces Assisted MISO Systems

In this work, we study the statistically robust beamforming design for an intelligent reflecting surfaces (IRS) assisted multiple-input single-output (MISO) wireless system under imperfect channel state information (CSI), where the channel estimation errors are assumed to be additive Gaussian. We aim at jointly optimizing the transmit/receive beamformers and IRS phase shifts to minimize the average mean squared error (MSE) at the user. In particular, to tackle the non-convex optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization and majorization-minimization techniques. Simulation results show that the proposed scheme achieves robust MSE performance in the presence of CSI error, and substantially outperforms conventional non-robust methods.

cs.IT↗

arXiv2020

AdaX: Adaptive Gradient Descent with Exponential Long Term Memory

Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in a simple non-convex synthetic problem, showing that Adam's fast convergence would possibly lead the algorithm to local minimums. To address this problem, we improve Adam by proposing a novel adaptive gradient descent algorithm named AdaX. Unlike Adam that ignores the past gradients, AdaX exponentially accumulates the long-term gradient information in the past during training, to adaptively tune the learning rate. We thoroughly prove the convergence of AdaX in both the convex and non-convex settings. Extensive experiments show that AdaX outperforms Adam in various tasks of computer vision and natural language processing and can catch up with Stochastic Gradient Descent.

cs.LG↗

arXiv2020

Sum Rate Optimization for Two Way Communications with Intelligent Reflecting Surface

In this letter, an intelligent reflecting surface (IRS) enhanced full-duplex MIMO two-way communication system is studied. The system sum rate is maximized through jointly optimizing the source precoders and the IRS phase shift matrix. Adopting the idea of Arimoto-Blahut algorithm, the non-convex optimization problem is decoupled into three sub-problems, which are solved alternatingly. All the sub-problems can be solved efficiently with closed-form solutions. In addition, practical IRS assumptions, e.g., discrete phase shift levels, are also considered. Numerical results verify the convergence and performance of the proposed scheme.

eess.SP↗

arXiv2020

Programmable Metasurface Based Multicast Systems: Design and Analysis

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

cs.IT↗

arXiv2020

Joint Activity Detection and Channel Estimation for mmW/THz Wideband Massive Access

Millimeter-wave/Terahertz (mmW/THz) communications have shown great potential for wideband massive access in next-generation cellular internet of things (IoT) networks. To decrease the length of pilot sequences and the computational complexity in wideband massive access, this paper proposes a novel joint activity detection and channel estimation (JADCE) algorithm. Specifically, after formulating JADCE as a problem of recovering a simultaneously sparse-group and low rank matrix according to the characteristics of mmW/THz channel, we prove that jointly imposing $l_1$ norm and low rank on such a matrix can achieve a robust recovery under sufficient conditions, and verify that the number of measurements derived for the mmW/THz wideband massive access system is significantly smaller than currently known measurements bound derived for the conventional simultaneously sparse and low-rank recovery. Furthermore, we propose a multi-rank aware method by exploiting the quotient geometry of product of complex rank-$L$ matrices with the number of scattering clusters $L$. Theoretical analysis and simulation results confirm the superiority of the proposed algorithm in terms of computational complexity, detection error rate, and channel estimation accuracy.

cs.IT↗

arXiv2020

Spin-orbit coupling in photonic graphene

We generate experimentally a honeycomb refractive index pattern in an atomic vapor cell using electromagnetically-induced transparency. We study experimentally and theoretically the propagation of polarized light beams in such "photonic graphene". We demonstrate that an effective spin-orbit coupling appears as a correction to the paraxial beam equations because of the strong spatial gradients of the permittivity. It leads to the coupling of spin and angular momentum at the Dirac points of the graphene lattice. Our results suggest that the polarization degree plays an important role in many configurations where it has been previously neglected.

cond-mat.mes-hall↗

arXiv2020

Unsupervised Learning for Passive Beamforming

Reconfigurable intelligent surface (RIS) has recently emerged as a promising candidate to improve the energy and spectral efficiency of wireless communication systems. However, the unit modulus constraint on the phase shift of reflecting elements makes the design of optimal passive beamforming solution a challenging issue. The conventional approach is to find a suboptimal solution using the semi-definite relaxation (SDR) technique, yet the resultant suboptimal iterative algorithm usually incurs high complexity, hence is not amenable for real-time implementation. Motivated by this, we propose a deep learning approach for passive beamforming design in RIS-assisted systems. In particular, a customized deep neural network is trained offline using the unsupervised learning mechanism, which is able to make real-time prediction when deployed online. Simulation results show that the proposed approach maintains most of the performance while significantly reduces computation complexity when compared with SDR-based approach.

cs.IT↗

arXiv2019

Observation of Edge Solitons in Photonic Graphene

Edge states emerge in diverse areas of science, offering new opportunities for the development of novel electronic or optoelectronic devices, sound and light propagation controls in acoustics and photonics. Previous experiments on edge states and exploration of topological phases in photonics were carried out mostly in linear regimes, but the current belief is that nonlinearity introduces new striking features into physics of edge states, lead-ing to the formation of edge solitons, optical isolation, and topological lasing, to name a few. Here we experimentally demonstrate edge solitons at the zigzag edge of a reconfigurable photonic graphene lattice created via the effect of electromagneti-cally induced transparency in an atomic vapor cell with controllable nonlinearity . To obtain edge solitons, Raman gain was introduced to compensate strong absorption experienced by the edge state during propagation. Our observations pave the way to ex-perimental exploration of topological photonics on nonlinear, reconfigurable platform.

physics.optics↗

arXiv2019

Deep Learning for Spectrum Sensing

In cognitive radio systems, the ability to accurately detect primary user's signal is essential to secondary user in order to utilize idle licensed spectrum. Conventional energy detector is a good choice for blind signal detection, while it suffers from the well-known SNR-wall due to noise uncertainty. In this letter, we firstly propose a deep learning based signal detector which exploits the underlying structural information of the modulated signals, and is shown to achieve the state of the art detection performance, requiring no prior knowledge about channel state information or background noise. In addition, the impacts of modulation scheme and sample length on performance are investigated. Finally, a deep learning based cooperative detection system is proposed, which is shown to provide substantial performance gain over conventional cooperative sensing methods.

cs.IT↗

arXiv2019

Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks

Group convolution, which divides the channels of ConvNets into groups, has achieved impressive improvement over the regular convolution operation. However, existing models, eg. ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers. Toward addressing this issue, we present Groupable ConvNet (GroupNet) built by using a novel dynamic grouping convolution (DGConv) operation, which is able to learn the number of groups in an end-to-end manner. The proposed approach has several appealing benefits. (1) DGConv provides a unified convolution representation and covers many existing convolution operations such as regular dense convolution, group convolution, and depthwise convolution. (2) DGConv is a differentiable and flexible operation which learns to perform various convolutions from training data. (3) GroupNet trained with DGConv learns different number of groups for different convolution layers. Extensive experiments demonstrate that GroupNet outperforms its counterparts such as ResNet and ResNeXt in terms of accuracy and computational complexity. We also present introspection and reproducibility study, for the first time, showing the learning dynamics of training group numbers.

cs.CV↗

arXiv2019

Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP), and approximate it to a virtual continuous time system (VCTS) with reflections. Different to most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). A extension is provided from single MT single MEC server scenarios to multiple MTs multiple MEC servers scenarios and several insights are derived. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

eess.SP↗

arXiv2019

Learn to Sense: a Meta-learning Based Sensing and Fusion Framework for Wireless Sensor Networks

Wireless sensor networks (WSN) acts as the backbone of Internet of Things (IoT) technology. In WSN, field sensing and fusion are the most commonly seen problems, which involve collecting and processing of a huge volume of spatial samples in an unknown field to reconstruct the field or extract its features. One of the major concerns is how to reduce the communication overhead and data redundancy with prescribed fusion accuracy. In this paper, an integrated communication and computation framework based on meta-learning is proposed to enable adaptive field sensing and reconstruction. It consists of a stochastic-gradient-descent (SGD) based base-learner used for the field model prediction aiming to minimize the average prediction error, and a reinforcement meta-learner aiming to optimize the sensing decision by simultaneously rewarding the error reduction with samples obtained so far and penalizing the corresponding communication cost. An adaptive sensing algorithm based on the above two-layer meta-learning framework is presented. It actively determines the next most informative sensing location, and thus considerably reduces the spatial samples and yields superior performance and robustness compared with conventional schemes. The convergence behavior of the proposed algorithm is also comprehensively analyzed and simulated. The results reveal that the proposed field sensing algorithm significantly improves the convergence rate.

eess.SP↗

arXiv2019

MmWave Communication With Active Ambient Perception

In existing communication systems, the channel state information of each UE (user equipment) should be repeatedly estimated when it moves to a new position or another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mmWave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mmWave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signalling overhead and online computational complexity. Based on the reconstructed mmWave channel, single-beam mmWave communication is designed and evaluated which shows excellent performance. Such an approach in fact integrates radar with communication, which may possibly open a new paradigm for future communication system design.

eess.SP↗

arXiv2019

Joint Active User Detection and Channel Estimation in Massive Access Systems Exploiting Reed-Muller Sequences

The requirements to support massive connectivity and low latency in massive Machine Type Communications (mMTC) bring a huge challenge in the design of its random access (RA) procedure, which usually calls for efficient joint active user detection and channel estimation. In this paper, we exploit the vast sequence space and the beneficial nested structure of the length-$2^m$ second-order Reed-Muller (RM) sequences for designing an efficient RA scheme, which is capable of reliably detecting multiple active users from the set of unknown potential users with a size as large as $2^{m(m-1)/2}$, whilst simultaneously estimating their channel state information as well. Explicitly, at the transmitter each user is mapped to a specially designed RM sequence, which facilitates reliable joint sequence detection and channel estimation based on a single transmission event. To elaborate, as a first step, at the receiver we exploit the elegant nested structure of the RM sequences using a layer-by-layer RM detection algorithm for the single-user (single-sequence) scenario. Then an iterative RM detection and channel estimation algorithm is conceived for the multi-user (multi-sequence) scenario. As a benefit of the information exchange between the RM sequence detector and channel estimator, a compelling performance vs. complexity trade-off is struck, as evidenced both by our analytical and numerical results.

cs.IT↗

arXiv2019

Design of Non-Orthogonal Beamspace Multiple Access for Cellular Internet-of-Things

In this paper, we study the problem of massive connections over limited radio spectrum for the cellular Internet-of-Things (IoT) in the fifth-generation (5G) wireless network. To address the challenging issues associated with channel state information (CSI) acquisition and beam design in the context of massive connections, we propose a new non-orthogonal beamspace multiple access framework. In particular, the user equipments (UEs) are non-orthogonal not only in the temporal-frequency domain, but also in the beam domain. We analyze the performance of the proposed non-orthogonal beamspace multiple access scheme, and derive an upper bound on the weighted sum rate in terms of channel conditions and system parameters. For further improving the performance, we propose three non-orthogonal transmit beam construction algorithms with different beamspace resolutions. Finally, extensive simulation results show that substantial performance gain can be obtained by the proposed non-orthogonal beamspace multiple access scheme over the baseline ones.

cs.IT↗

arXiv2018

Particle-like behavior of topological defects in linear wave packets in photonic graphene

Topological defects, such as quantum vortices, determine the properties of quantum fluids. The study of their properties has been at the center of activity in solid state and BEC communities. On the other hand, the non-trivial behavior of wavepackets, such as the self-accelerating Airy beams, has also been intriguing physicists. Here, we study the formation, evolution, and interaction of optical vortices in wavepackets at the Dirac point in photonic graphene. We show that even in a non-interacting two-component system the topological excitations appearing in a wavepacket can still be considered as "particles", capable of mutual interaction, with their trajectory obeying the laws of dynamics.

physics.optics↗