arXiv subjects

Yanjie Dong

Publications and source records attributed to Yanjie Dong.

29 records · Page 2Linked to original sources

arXiv2019

Cross-Layer Scheduling and Beamforming in Smart Grid Powered Small-Cell Networks

In the small-cell networks (SCNs) with multiple small-cell base stations (ScBSs), the joint design of beamforming vectors, user scheduling and ScBS sleeping is investigated with the constraints on proportional rate. A long-term grid-energy expenditure minimization problem is formulated for the considered SCNs, which are powered by the smart grid and natural renewable energy. Since the scheduled user indicators are coupled with the beamforming vectors, the formulated problem is challenging to handle. In order to decouple the beamforming vectors from the scheduled user indicators, the Lyapunov optimization technique is used. As a result, a practical two-scale algorithm is proposed to allocate the user scheduling indicators and ScBS sleeping variables at the coarse-grained granularity (frame) as well as obtain the beamforming vectors at the fine-grained granularity (slot). Numerical results are used to verify the performance of the proposed two-scale algorithm.

cs.IT↗

arXiv2019

Secure Distributed On-Device Learning Networks With Byzantine Adversaries

The privacy concern exists when the central server has the copies of datasets. Hence, there is a paradigm shift for the learning networks to change from centralized in-cloud learning to distributed \mbox{on-device} learning. Benefit from the parallel computing, the on-device learning networks have a lower bandwidth requirement than the in-cloud learning networks. Moreover, the on-device learning networks also have several desirable characteristics such as privacy preserving and flexibility. However, the \mbox{on-device} learning networks are vulnerable to the malfunctioning terminals across the networks. The worst-case malfunctioning terminals are the Byzantine adversaries, that can perform arbitrary harmful operations to compromise the learned model based on the full knowledge of the networks. Hence, the design of secure learning algorithms becomes an emerging topic in the on-device learning networks with Byzantine adversaries. In this article, we present a comprehensive overview of the prevalent secure learning algorithms for the two promising on-device learning networks: Federated-Learning networks and decentralized-learning networks. We also review several future research directions in the \mbox{Federated-Learning} and decentralized-learning networks.

cs.LG↗

arXiv2018

Robust Secrecy Energy Efficient Beamforming in MISOME-SWIPT Systems With Proportional Fairness

The joint design of beamforming vector and artificial noise covariance matrix is investigated for multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. A secrecy energy efficiency (SEE) maximization problem is formulated in the MISOME-SWIPT system with imperfect channel state information and proportional secrecy rate constraints. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Numerical results are used to verify the performance of the proposed algorithm and to reveal practical insights.

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arXiv2018

Robust Energy Efficient Beamforming in MISOME-SWIPT Systems With Proportional Secrecy Rate

The joint design of beamforming vector and artificial noise covariance matrix is investigated for the multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring \mbox{(MISOME-SWIPT)} systems. In the MISOME-SWIPT system, the base station delivers information signals to the legitimate user equipments and broadcasts jamming signals to the eavesdroppers. A secrecy energy efficiency (SEE) maximization problem is formulated for the considered \mbox{MISOME-SWIPT} system with imperfect channel state information, where the SEE is defined as the ratio of sum secrecy rate over total power consumption. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Two suboptimal solutions are also proposed based on the heuristic beamforming techniques that trade performance for computational complexity. In addition, the analysis of computational complexity is performed for the optimal and suboptimal solutions. Numerical results are used to verify the performance of proposed algorithms and to reveal practical insights.

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arXiv2018

Extracting the Most Weighted Throughput in UAV Empowered Wireless Systems With Nonlinear Energy Harvester

With the maturity of unmanned aerial vehicle (UAV) technology, this work investigates the integration of UAV into wireless communication systems. Since the UAV is powered by a capacity-limited battery, this work proposes to use the radio energy harvesting technology at the UAV in order to extend the lifetime of UAV empowered base station. To extract the most weighted throughput of UAV empowered wireless systems, the dirty paper coding scheme and information-theoretic uplink-downlink channel duality are exploited to propose an extracting the most weighted throughput algorithm. Numerical results are used to verify the proposed algorithm.

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arXiv2018

An Edge Computing Empowered Radio Access Network With UAV-Mounted FSO Fronthaul and Backhaul: Key Challenges and Approaches

One promising approach to address the supply-demand mismatch between the terrestrial infrastructure and the temporary and/or unexpected traffic demands is to leverage the unmanned aerial vehicle (UAV) technologies. Motivated by the recent advancement of UAV technologies and retromodulator based free space optical communication, we propose a novel edge-computing empowered radio access network architecture where the fronthaul and backhaul links are mounted on the UAVs for rapid event response and flexible deployment. The implementation of hardware and networking technologies for the proposed architecture are investigated. Due to the limited payload and endurance as well as the high mobility of UAVs, research challenges related to the communication resource management and recent research progress are reported.

cs.NI↗

arXiv2018

Secure Beamforming in Full-Duplex SWIPT Systems With Loopback Self-Interference Cancellation

Security is a critical issue in full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems with loopback self-interference cancellation. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a secrecy rate maximization problem under energy transfer rate constraints. Although the secrecy rate maximization problem is non-convex, we solve it via semidefinite relaxation and a two-dimensional search. We prove the optimality of our proposed algorithm and demonstrate its performance via simulations.

cs.NI↗

arXiv2017

Fronthaul-Aware Group Sparse Precoding and Signal Splitting in SWIPT C-RAN

We investigate the precoding, remote radio head (RRH) selection and signal splitting in the simultaneous wireless information and power transferring (SWIPT) cloud radio access networks \mbox{(C-RANs)}. The objective is to minimize the power consumption of the SWIPT C-RAN. Different from the existing literature, we consider the nonlinear fronthaul power consumption and the multiple antenna RRHs. By switching off the unnecessary RRHs, the group sparsity of the precoding coefficients is introduced, which indicates that the precoding process and the RRH selection are coupled. In order to overcome these issues, a group sparse precoding and signal splitting algorithm is proposed based on the majorization-minimization framework, and the convergence behavior is established. Numerical results are used to verify our proposed studies.

cs.IT↗

arXiv2017

Secure Beamforming in Full-Duplex SWIPT Systems

Physical layer security is a key issue in the full duplex (FD) communication systems due to the broadcast nature of wireless channels. In this paper, the joint design of information and artificial noise beamforming vectors is proposed for the FD simultaneous wireless information and power transferring (FD-SWIPT) systems. To guarantee high security and energy harvesting performance of the FD-SWIPT system, the proposed design is formulated as a sum information transmission rate (SITR) maximization problem under information-leakage and energy constraints. In addition, we consider the fairness issue between the uplink and downlink information transmission rates by formulating a \mbox{fairness-aware} SITR-maximization problem. Although the formulated \mbox{SITR-maximization} and \mbox{fairness-aware} \mbox{SITR-maximization} problems are non-convex, we solve them via semidefinite relaxation and one-dimensional search. The optimality of our proposed algorithms is theoretically proved, and the computation complexities are established. Moreover, we propose two suboptimal solutions to the formulated optimization problems. In terms of the SITR-maximization problem, numerical results show that the performance achieved by one of the two suboptimal algorithms is close to the performance of the optimal algorithm with increasing maximum transmission power of the FD-BST.

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arXiv2017

Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy

The application of renewable energy is a promising solution to realize the Green Communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a \mbox{per-frame} grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the \mbox{zero-forcing} beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.

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arXiv2016

Fronthauling for 5G LTE-U Ultra Dense Cloud Small Cell Networks

Ultra dense cloud small cell network (UDCSNet), which combines cloud computing and massive deployment of small cells, is a promising technology for the fifth-generation (5G) LTE-U mobile communications because it can accommodate the anticipated explosive growth of mobile users' data traffic. As a result, fronthauling becomes a challenging problem in 5G LTE-U UDCSNet. In this article, we present an overview of the challenges and requirements of the fronthaul technology in 5G \mbox{LTE-U} UDCSNets. We survey the advantages and challenges for various candidate fronthaul technologies such as optical fiber, millimeter-wave based unlicensed spectrum, Wi-Fi based unlicensed spectrum, sub 6GHz based licensed spectrum, and free-space optical based unlicensed spectrum.

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