References of "Lei, Lei 50025811"
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See detailNOMA-Enabled Multi-Beam Satellite Systems: Joint Optimization to Overcome Offered-Requested Data Mismatches
Wang, Anyue UL; Lei, Lei UL; Lagunas, Eva UL et al

in IEEE Transactions on Vehicle Technology (2020)

Detailed reference viewed: 105 (16 UL)
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See detailImpact of Varying Radio Power Density on Wireless Communications of RF Energy Harvesting Systems
Luo, Yu; Pu, Lina; Lei, Lei UL

in IEEETransactions on Communications (2020)

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See detailActor-Critic Deep Reinforcement Learning for Energy Minimization in UAV-Aided Networks
Yuan, Yaxiong UL; Lei, Lei UL; Vu, Thang Xuan UL et al

in 2020 European Conference on Networks and Communications (EuCNC) (2020, September 21)

In this paper, we investigate a user-timeslot scheduling problem for downlink unmanned aerial vehicle (UAV)-aided networks, where the UAV serves as an aerial base station. We formulate an optimization ... [more ▼]

In this paper, we investigate a user-timeslot scheduling problem for downlink unmanned aerial vehicle (UAV)-aided networks, where the UAV serves as an aerial base station. We formulate an optimization problem by jointly determining user scheduling and hovering time to minimize UAV’s transmission and hovering energy. An offline algorithm is proposed to solve the problem based on the branch and bound method and the golden section search. However, executing the offline algorithm suffers from the exponential growth of computational time. Therefore, we apply a deep reinforcement learning (DRL) method to design an online algorithm with less computational time. To this end, we first reformulate the original user scheduling problem to a Markov decision process (MDP). Then, an actor-critic-based RL algorithm is developed to determine the scheduling policy under the guidance of two deep neural networks. Numerical results show the proposed online algorithm obtains a good tradeoff between performance gain and computational time. [less ▲]

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See detailBeam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping
Lei, Lei UL; Lagunas, Eva UL; Yuan, Yaxiong UL et al

in IEEE Access (2020)

Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular and time-varying traffic requests in future multi-beam satellite systems. In BH optimization, adopting ... [more ▼]

Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular and time-varying traffic requests in future multi-beam satellite systems. In BH optimization, adopting conventional iterative heuristics may have their own limitations in providing timely solutions, and directly using data-driven technique to approximate optimization variables may lead to constraint violation and degraded performance. In this paper, we explore a combined learning-and-optimization (L&O) approach to provide an efficient, feasible, and near-optimal solution. The investigations are from the following aspects: 1) Integration ofBH optimization and learning techniques; 2) Features to be learned in BH design; 3) How to address the feasibility issue incurred by machine learning. We provide numerical results and analysis to show that the learning component in L&O significantly accelerates the procedure of identifying promising BH patterns, resulting in reduced computing time from seconds/minutes to milliseconds level. The identified learning feature enables high accuracy in predictions. In addition, the optimization component in L&O guarantees the solution’s feasibility and improves the overall performance with around 5% gap to the optimum. [less ▲]

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See detailDeep Learning for Beam Hopping in Multibeam Satellite Systems
Lei, Lei UL; Lagunas, Eva UL; Yuan, Yaxiong UL et al

in IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (2020, May)

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See detailTowards Power-Efficient Aerial Communicationsvia Dynamic Multi-UAV Cooperation
Xiang, Lin; Lei, Lei UL; Chatzinotas, Symeon UL et al

in IEEE Wireless Communications and Networking Conference (WCNC) 2020 (2020, May)

Detailed reference viewed: 18 (0 UL)
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See detailAn Overview of Generic Tools for Information-Theoretic Secrecy Performance Analysis over Wiretap Fading Channels
Kong, Long UL; Ai, Yun; Lei, Lei UL et al

E-print/Working paper (2020)

An alternative or supplementary approach named as physical layer security has been proposed to afford an extra security layer on top of the conventional cryptography technique. In this paper, an overview ... [more ▼]

An alternative or supplementary approach named as physical layer security has been proposed to afford an extra security layer on top of the conventional cryptography technique. In this paper, an overview of secrecy performance investigations over the classic Alice-Bob-Eve wiretap fading channels is conducted. On the basis of the classic wiretap channel model, we have comprehensively listed and thereafter compared the existing works on physical layer secrecy analysis considering the small-scale, large-scale, composite, and cascaded fading channel models. Exact secrecy metrics expressions, including secrecy outage probability (SOP), the probability of non-zero secrecy capacity (PNZ), average secrecy capacity (ASC), and secrecy bounds, including the lower bound of SOP and ergodic secrecy capacity, are presented. In order to encompass the aforementioned four kinds of fading channel models with a more \textit{generic} and \textit{flexible} distribution, the mixture gamma (MG), mixture of Gaussian (MoG), and Fox's $H$-function distributions are three useful candidates to largely include the above-mentioned four kinds of fading channel models. It is shown that they are flexible and general when assisting the secrecy analysis to obtain closed-form expressions. Their advantages and limitations are also highlighted. Conclusively, these three approaches are proven to provide a unified secrecy analysis framework and can cover all types of independent wiretap fading channel models. Apart from those, revisiting the existing secrecy enhancement techniques based on our system configuration, the on-off transmission scheme, jamming approach (including artificial noise (AN) & artificial fast fading (AFF)), antenna selection, and security region are presented. [less ▲]

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See detailProxSGD: Training Structured Neural Networks under Regularization and Constraints
Yang, Yang; Yuan, Yaxiong UL; Chatzimichailidis, Avraam et al

in International Conference on Learning Representations (ICLR) 2020 (2020)

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See detailLearning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems
Lei, Lei UL; You, Lei; He, Qing et al

in IEEE Transactions on Green Communications and Networking (2019)

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See detailOn Fairness Optimization for NOMA-Enabled Multi-Beam Satellite Systems
Wang, Anyue UL; Lei, Lei UL; Lagunas, Eva UL et al

in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2019 (2019, September)

Detailed reference viewed: 109 (22 UL)
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See detailJoint User Grouping and Power Allocation for MISO Systems: Learning to Schedule
Yuan, Yaxiong; Vu, Thang Xuan UL; Lei, Lei UL et al

in IEEE European Signal Processing Conference 2019 (2019, September)

Detailed reference viewed: 66 (19 UL)
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See detailLoad Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks
Lei, Lei UL; You, Lei; Yang, Yang et al

in IEEE Transactions on Vehicular Technology (2019)

Detailed reference viewed: 137 (21 UL)
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See detailOptimal Resource Allocation for NOMA-Enabled Cache Replacement and Content Delivery
Lei, Lei UL; Vu, Thang Xuan UL; Xiang, Lin UL et al

in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2019) (2019, September)

Detailed reference viewed: 76 (11 UL)
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See detailLearning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network
Lei, Lei UL; Yaxiong, Yuan UL; Vu, Thang Xuan UL et al

in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 (2019, July)

Detailed reference viewed: 69 (25 UL)
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See detailMachine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

Detailed reference viewed: 69 (15 UL)
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See detailOn the Successful Delivery Probability of Full-Duplex-Enabled Mobile Edge Caching
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2019)

Detailed reference viewed: 58 (6 UL)
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See detailSatellite Links Integrated in 5G SDN-enabled Backhaul Networks: An Iterative Joint Power and Flow Assignment
Lagunas, Eva UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in European Signal Processing Conference (EUSIPCO), A Coruna, Spain, Sept. 2019 (2019)

Detailed reference viewed: 104 (13 UL)
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See detailEnergy Efficient Design for Coded Caching Delivery Phase
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) (2019)

Detailed reference viewed: 38 (6 UL)
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See detailLinear Precoding Design for Cache-aided Full-duplex Networks
Vu, Thang Xuan UL; Vu, Trinh Anh; Lei, Lei UL et al

in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019)

Detailed reference viewed: 42 (4 UL)
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See detailPower and Flow Assignment for 5G Integrated Terrestrial-Satellite Backhaul Networks
Lagunas, Eva UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in IEEE Wireless Communications and Networking Conference, Marrakech, Morocco, April 2019 (2019)

Detailed reference viewed: 127 (25 UL)