arXiv subjects

Zhaohui Yang

Publications and source records attributed to Zhaohui Yang.

At least 253 records · Page 14Linked to original sources

arXiv2020

Deep Learning for Optimal Deployment of UAVs with Visible Light Communications

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association.

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arXiv2020

Delay Minimization for Federated Learning Over Wireless Communication Networks

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.

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arXiv2020

Reflections in the Sky: Joint Trajectory and Passive Beamforming Design for Secure UAV Networks with Reconfigurable Intelligent Surface

This paper investigates the problem of secure energy efficiency maximization for a reconfigurable intelligent surface (RIS) assisted uplink wireless communication system, where an unmanned aerial vehicle (UAV) equipped with an RIS works as a mobile relay between the base station (BS) and a group of users. We focus on maximizing the secure energy efficiency of the system via jointly optimizing the UAV's trajectory, the RIS's phase shift, users' association and transmit power. To tackle this problem, we divide the original problem into three sub-problems, and propose an efficient iterative algorithm. In particular, the successive convex approximation method (SCA) is applied to solve the nonconvex UAV trajectory, the RIS's phase shift, and transmit power optimization sub-problems. We further provide two schemes to simplify the solution of phase and trajectory sub-problem. Simulation results demonstrate that the proposed algorithm converges fast, and the proposed design can enhance the secure energy efficiency by up to 38\% gains, as compared to the traditional schemes without any RIS.

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arXiv2020

Cooperative Rate-Splitting for Secrecy Sum-Rate Enhancement in Multi-antenna Broadcast Channels

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes. For CRS based on the three-node relay channel, the transmitter splits and encodes the messages of legitimate users into common and private streams based on Rate-Splitting (RS). With the goal of maximizing SSR, the proposed CRS strategy opportunistically asks the relaying legitimate user to forward its decoded common message. During the transmission, the eavesdropper keeps wiretapping silently. To ensure secure transmission, the common message is used for the dual purpose, serving both as a desired message and Artificial Noise (AN) without consuming extra transmit power comparing to the conventional AN design. Taking into account the total power constraint and the Physical Layer (PHY) security, the precoders and time-slot allocation are jointly optimized by solving the non-convex SSR maximization problem based on Sequential Convex Approximation (SCA) algorithm. Numerical results show that the proposed CRS secure transmission scheme outperforms existing Multi-User Linear Precoding (MU-LP) and Cooperative Non-Orthogonal Multiple Access (C-NOMA) strategies. Therefore, CRS is a promising strategy to enhance the PHY security in multi-antenna BC systems.

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arXiv2020

Multi-cell Edge Coverage Enhancement Using Mobile UAV-Relay

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs), but also improve the quality of service of cell-edge users (CEUs). In this paper, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multi-cell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm was proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with existing benchmark schemes.

cs.NI↗

arXiv2020

Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33\% and 68\% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

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arXiv2020

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4\% mAP on COCO minival set with 27M parameters. Our implementation is available at https://github.com/ggjy/HitDet.pytorch.

cs.CV↗

arXiv2020

CARS: Continuous Evolution for Efficient Neural Architecture Search

Searching techniques in most of existing neural architecture search (NAS) algorithms are mainly dominated by differentiable methods for the efficiency reason. In contrast, we develop an efficient continuous evolutionary approach for searching neural networks. Architectures in the population that share parameters within one SuperNet in the latest generation will be tuned over the training dataset with a few epochs. The searching in the next evolution generation will directly inherit both the SuperNet and the population, which accelerates the optimal network generation. The non-dominated sorting strategy is further applied to preserve only results on the Pareto front for accurately updating the SuperNet. Several neural networks with different model sizes and performances will be produced after the continuous search with only 0.4 GPU days. As a result, our framework provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings. These networks surpass those produced by the state-of-the-art methods on the benchmark ImageNet dataset.

cs.CV↗

arXiv2020

Resource Allocation for UAV Assisted Wireless Networks with QoS Constraints

For crowded and hotspot area, unmanned aerial vehicles (UAVs) are usually deployed to increase the coverage rate. In the considered model, there are three types of services for UAV assisted communication: control message, non-realtime communication, and real-time communication, which can cover most of the actual demands of users in a UAV assisted communication system. A bandwidth allocation problem is considered to minimize the total energy consumption of this system while satisfying the requirements. Two techniques are introduced to enhance the performance of the system. The first method is to categorize the ground users into multiple user groups and offer each group a unique RF channel with different bandwidth. The second method is to deploy more than one UAVs in the system. Bandwidth optimization in each scheme is proved to be a convex problem. Simulation results show the superiority of the proposed schemes in terms of energy consumption.

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arXiv2020

Channel Assignment in Uplink Wireless Communication using Machine Learning Approach

This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.

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arXiv2019

Data-Free Learning of Student Networks

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors. Most existing deep neural network compression and speed-up methods are very effective for training compact deep models, when we can directly access the training dataset. However, training data for the given deep network are often unavailable due to some practice problems (e.g. privacy, legal issue, and transmission), and the architecture of the given network are also unknown except some interfaces. To this end, we propose a novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs). To be specific, the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator. Then, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. Efficient student networks learned using the proposed Data-Free Learning (DAFL) method achieve 92.22% and 74.47% accuracies using ResNet-18 without any training data on the CIFAR-10 and CIFAR-100 datasets, respectively. Meanwhile, our student network obtains an 80.56% accuracy on the CelebA benchmark.

cs.LG↗

arXiv2019

Energy Efficient UAV Communication with Energy Harvesting

This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.

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arXiv2019

Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.

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arXiv2019

Efficient Resource Allocation for Mobile-Edge Computing Networks with NOMA: Completion Time and Energy Minimization

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users' tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closed-form solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.

cs.IT↗

arXiv2019

A Caching Strategy Towards Maximal D2D Assisted Offloading Gain

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is non-convex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

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arXiv2019

Cache Placement in Two-Tier HetNets with Limited Storage Capacity: Cache or Buffer?

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared to the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

cs.IT↗

arXiv2019

Sum-Rate Maximization of Uplink Rate Splitting Multiple Access (RSMA) Communication

In this paper, the problem of maximizing the wireless users' sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, each user transmits a superposition of two messages to a base station (BS) with separate transmit power and the BS uses a successive decoding technique to decode the received messages. To maximize each user's transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users' transmit power and the BS's decoding order. However, since the decoding order variable in the optimization problem is discrete, the original maximization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is calculated. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. For comparisons, the optimal sum-rate maximizing solutions with proportional rate constraints are obtained in closed form for non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). Simulation results show that RSMA can achieve up to 10.0\%, 22.2\%, and 83.7\% gains in terms of sum-rate compared to NOMA, FDMA, and TDMA.

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arXiv2019

Revisiting Perspective Information for Efficient Crowd Counting

Crowd counting is the task of estimating people numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions. A major challenge of this task lies in the perspective distortion, which results in drastic person scale change in an image. Density regression on the small person area is in general very hard. In this work, we propose a perspective-aware convolutional neural network (PACNN) for efficient crowd counting, which integrates the perspective information into density regression to provide additional knowledge of the person scale change in an image. Ground truth perspective maps are firstly generated for training; PACNN is then specifically designed to predict multi-scale perspective maps, and encode them as perspective-aware weighting layers in the network to adaptively combine the outputs of multi-scale density maps. The weights are learned at every pixel of the maps such that the final density combination is robust to the perspective distortion. We conduct extensive experiments on the ShanghaiTech, WorldExpo'10, UCF_CC_50, and UCSD datasets, and demonstrate the effectiveness and efficiency of PACNN over the state-of-the-art.

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