285 results on '"Li-ping Qian"'
Search Results
102. Power controlled system revenue maximization in large-scale heterogeneous cellular networks.
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Li Ping Qian 0001, Cheng Qian, Yuan Wu 0001, and Qingzhang Chen
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- 2014
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103. Interference-aware system utility maximization for cognitive radio networks.
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Li Ping Qian 0001, Shengli Zhang, Wei Zhang 0001, and Ying Jun (Angela) Zhang
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- 2014
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104. SWIPT Cooperative Spectrum Sharing for 6G-Enabled Cognitive IoT Network
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Yi Gong, Nan Zhao, Weidang Lu, Guoxing Huang, Huimei Han, Li Ping Qian, and Peiyuan Si
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Computer Networks and Communications ,business.industry ,Computer science ,Orthogonal frequency-division multiplexing ,020209 energy ,Transmitter ,020206 networking & telecommunications ,02 engineering and technology ,Spectral efficiency ,Spectrum management ,Multiplexing ,Computer Science Applications ,Hardware and Architecture ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,business ,Wireless sensor network ,Information Systems ,Computer network ,Efficient energy use - Abstract
Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.
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- 2021
105. System Utility Maximization With Interference Processing for Cognitive Radio Networks.
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Li Ping Qian 0001, Shengli Zhang, Wei Zhang 0001, and Ying Jun Zhang
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- 2015
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106. Optimal Power Control for Energy Efficient D2D Communication and Its Distributed Implementation.
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Yuan Wu 0001, Jiaheng Wang 0001, Li Ping Qian 0001, and Robert Schober
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- 2015
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107. Learning Driven Resource Allocation and SIC Ordering in EH Relay Aided NB-IoT Networks
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Yuan Wu, Li Ping Qian, Chao Yang, Huimei Han, and Limin Meng
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Optimization problem ,Computer science ,Distributed computing ,Throughput ,Spectral efficiency ,Computer Science Applications ,law.invention ,Single antenna interference cancellation ,Relay ,law ,Modeling and Simulation ,Resource allocation ,Reinforcement learning ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering - Abstract
Integrating the energy-harvesting (EH) relay and non-orthogonal multiple access (NOMA) technologies into narrow band internet of things (NB-IoT) networks can efficiently improve the energy and spectrum efficiency of the network and the quality-of-service of edge users. Therefore, we consider an EH relay aided NOMA NB-IoT network in this letter. To reduce the rate variance among NB-IoT devices, we aim to maximize the proportional fairness of data rate across all NB-IoT devices through jointly optimizing the communication resource allocation and successive interference cancellation (SIC) ordering subject to the minimum data rate requirements. Considering the non-convexity of this optimization problem, we propose a deep reinforcement learning based online optimization algorithm to obtain the sub-optimal solution. Simulation results demonstrate that the proposed algorithm can efficiently improve the proportional fairness and the total throughput among NB-IoT devices, in comparison with orthogonal multiple access techniques.
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- 2021
108. NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things
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Ningning Yu, Yuan Wu, Bin Lin, Li Ping Qian, Weidang Lu, and Fuli Jiang
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Mobile edge computing ,Computer science ,Distributed computing ,020208 electrical & electronic engineering ,02 engineering and technology ,Computer Science Applications ,Control and Systems Engineering ,Distributed algorithm ,Server ,0202 electrical engineering, electronic engineering, information engineering ,Computation offloading ,Reinforcement learning ,Electrical and Electronic Engineering ,Edge computing ,Information Systems ,Communication channel - Abstract
Multiaccess mobile edge computing (MA-MEC) has been envisioned as one of the key approaches for enabling computation-intensive yet delay-sensitive services in future industrial Internet of Things (IoT). In this article, we exploit nonorthogonal multiple access (NOMA) for computation offloading in MA-MEC and propose a joint optimization of the multiaccess multitask computation offloading, NOMA transmission, and computation-resource allocation, with the objective of minimizing the total energy consumption of IoT device to complete its tasks subject to the required latency limit. We first focus on a static channel scenario and propose a distributed algorithm to solve the joint optimization problem by identifying the layered structure of the formulated nonconvex problem. Furthermore, we consider a dynamic channel scenario in which the channel power gains from the IoT device to the edge-computing servers are time varying. To tackle with the difficulty due to the huge number of different channel realizations in the dynamic scenario, we propose an online algorithm, which is based on deep reinforcement learning (DRL), to efficiently learn the near-optimal offloading solutions for the time-varying channel realizations. Numerical results are provided to validate our distributed algorithm for the static channel scenario and the DRL-based online algorithm for the dynamic channel scenario. We also demonstrate the advantage of the NOMA assisted multitask MA-MEC against conventional orthogonal multiple access scheme under both static and dynamic channels.
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- 2021
109. Secrecy-Based Energy-Efficient Mobile Edge Computing via Cooperative Non-Orthogonal Multiple Access Transmission
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Weidang Lu, Bin Lin, Yuan Wu, Tony Q. S. Quek, Li Ping Qian, and Wu Weicong
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Base station ,Mobile edge computing ,Utility maximization problem ,business.industry ,Computer science ,Wireless network ,Wireless ,Computation offloading ,Throughput ,Electrical and Electronic Engineering ,business ,Efficient energy use ,Computer network - Abstract
Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED’s offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED’s offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED’s latency-requirement and the selected WDs’ required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs’ delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD’s optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.
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- 2021
110. Distributed power control with limited message passing for nonconcave utility maximization.
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Li Ping Qian 0001 and Ying Jun (Angela) Zhang
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- 2011
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111. Globally Optimal Distributed Power Control for Nonconcave Utility Maximization.
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Li Ping Qian 0001, Ying Jun Zhang, and Mung Chiang
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- 2010
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112. On Optimization of Joint Base Station Association and Power Control via Benders' Decomposition.
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Jieying Chen, Li Ping Qian 0001, and Ying Jun Zhang
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- 2009
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113. Monotonic Optimization for Non-Concave Power Control in Multiuser Multicarrier Network Systems.
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Li Ping Qian 0001 and Ying Jun Zhang
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- 2009
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114. Optimal Energy Scheduling for Residential Smart Grid With Centralized Renewable Energy Source.
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Yuan Wu 0001, Vincent K. N. Lau, Danny H. K. Tsang, Li Ping Qian 0001, and Limin Meng
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- 2014
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115. Transmit Power Minimization for Outage-Constrained Relay Selection over Rayleigh-Fading Channels.
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Li Ping Qian 0001, Yuan Wu 0001, and Qingzhang Chen
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- 2014
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116. Optimal Throughput-Oriented Power Control by Linear Multiplicative Fractional Programming.
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Li Ping Qian 0001 and Ying Jun Zhang
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- 2008
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117. Meta-Learning Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks
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Shicheng Yang, Liang Huang, Li Ping Qian, Yuan Wu, and Luxin Zhang
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Mobile edge computing ,Meta learning (computer science) ,Computer science ,business.industry ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,Computer Science Applications ,Task (project management) ,Modeling and Simulation ,Server ,0202 electrical engineering, electronic engineering, information engineering ,Task analysis ,Wireless ,Computation offloading ,Electrical and Electronic Engineering ,business - Abstract
Deep learning-based algorithms provide a promising solution to efficiently generate offloading decisions in mobile edge computing (MEC) networks. However, considering dynamic MEC devices or offloading tasks, most of them require large-scale training data and long training time to retrain the deep neural networks (DNNs). In this letter, we propose a MEta-Learning-based computation Offloading (MELO) algorithm for dynamic computation tasks in MEC networks. Specifically, it learns from historical MEC task scenarios and adapts to a new MEC task scenario with a few training samples. Numerical results show that the proposed algorithm can adapt to a new MEC task scenario and achieve 99% accuracy via 1-step fine-tuning using only 10 training samples.
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- 2021
118. Distributed Charging-Record Management for Electric Vehicle Networks via Blockchain
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Weijia Jia, Li Ping Qian, Zhiguo Shi, Bo Ji, Yuan Wu, and Xu Xu
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Mathematical optimization ,Blockchain ,business.product_category ,Matching (graph theory) ,Computer Networks and Communications ,Computer science ,020208 electrical & electronic engineering ,020206 networking & telecommunications ,02 engineering and technology ,Computer Science Applications ,Hardware and Architecture ,Server ,Signal Processing ,Electric vehicle ,0202 electrical engineering, electronic engineering, information engineering ,Resource management ,business ,Byzantine fault tolerance ,Protocol (object-oriented programming) ,Integer programming ,Information Systems - Abstract
The deep penetration of electric vehicles (EVs) into the transportation section and the associated charging management has yielded a critical issue, namely, how to efficiently store the generated charging records. In this article, we investigate the cost-efficient charging-record storage scheme by exploiting blockchain (BC). Accounting for the operational cost due to the consensus process via the practical Byzantine fault tolerance (PBFT) protocol, we model the associated cost for storing the charging records via an ideal multiblockchain system and formulate a joint optimization of the storage selection (i.e., either storing the charging record locally or selecting one of the BCs for storing the charging record) and server-node allocation for each BC, with the objective of minimizing a systemwise cost. Despite the nature of the complicated mixed binary and integer programming problem, we exploit the decomposition structure and propose a layered algorithm (i.e., the bottom subproblem for determining the optimal storage selection and the top problem for finding the server-node allocation) to solve it. For the bottom subproblem, we exploit the nature of minimum weighted matching of the problem and propose a distributed auction-based algorithm for computing the optimal storage selection. With the optimal solution from the subproblem, we further propose an annealing-based algorithm to determine the server-node allocation for each BC. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our cost-efficient charging-record storage scheme via BC.
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- 2021
119. Optimal ADMM-Based Spectrum and Power Allocation for Heterogeneous Small-Cell Networks with Hybrid Energy Supplies
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Xuemin Sherman Shen, Li Ping Qian, Yuan Wu, and Bo Ji
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Mathematical optimization ,Optimization problem ,Computer Networks and Communications ,Computer science ,business.industry ,020206 networking & telecommunications ,02 engineering and technology ,Maximization ,Energy consumption ,Frequency reuse ,Scheduling (computing) ,Frequency allocation ,Renewable energy ,Convex optimization ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Resource management ,Small cell ,Electrical and Electronic Engineering ,business ,Time complexity ,Software ,Power control - Abstract
Powering cellular networks with hybrid energy supplies is not only environment-friendly but can also reduce the on-grid energy consumption, thus being emerging as a promising solution for green networking. Intelligent management of spectrum and power can increase the network utility in cellular networks with hybrid energy supplies, usually at the cost of higher energy consumption. Unlike prior studies on either the network utility maximization or on-grid energy cost minimization, this paper studies the joint spectrum and power allocation problem that maximizes the system revenue in a heterogeneous small-cell network with hybrid energy supplies. Specifically, the system revenue is considered as the difference between the network utility and on-grid energy cost. By developing the convexity of the optimization problem through transformation and reparameterization, we propose a joint spectrum and power allocation algorithm based on the primal-dual arguments to obtain the optimal solution by iteratively solving the primal and dual sub-problems of the convex optimization problem. To solve the primal sub-problem, we further propose the Lagrangian maximization based on the alternating direction method of multipliers (ADMM), and derive the optimal solution in the closed-form expression at each iteration. It is shown that the proposed joint spectrum and power allocation algorithm approaches the global optimality at the rate of $1/n$ 1 / n with $n$ n being the number of iterations. Also, the proposed ADMM-based Lagrangian maximization algorithm approaches the primal optimal solution with the time complexity of $O(1/\epsilon _r)$ O ( 1 / e r ) iterations with $\epsilon _r$ e r being the termination parameter. Simulation results show that in comparison with the power control with equal frequency allocation algorithm and frequency allocation with equal power allocation algorithms the proposed algorithm increases the system revenue by over 20 and 60 percent without consuming more on-grid energy when the proportional fairness utility and the weighted sum rate utility are considered with the approximate system parameter settings, respectively. Meanwhile, in comparison with the full frequency reuse case, the proposed algorithm increases the system revenue by 20 percent at least in terms of the weighted sum rate utility, although it achieves the similar system revenue when considering the proportional fairness utility. Simulation results also show that our proposed algorithm can perform well under the realistic fast fading channel conditions.
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- 2021
120. Learning Driven NOMA Assisted Vehicular Edge Computing via Underlay Spectrum Sharing
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Li Ping Qian, Yuan Wu, Fuli Jiang, Tony Q. S. Quek, Ningning Yu, and Haibo Zhou
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Optimization problem ,Frequency-division multiple access ,Computer Networks and Communications ,Computer science ,Distributed computing ,Aerospace Engineering ,020302 automobile design & engineering ,02 engineering and technology ,0203 mechanical engineering ,Server ,Automotive Engineering ,Computation offloading ,Resource management ,Electrical and Electronic Engineering ,Underlay ,5G ,Edge computing ,Communication channel - Abstract
Edge computing has been considered as one of the key paradigms in the fifth-generation (5G) networks for enabling computation-intensive yet latency-sensitive vehicular Internet services. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted vehicular edge computing via underlay spectrum sharing, in which vehicular computing-users (VUs) form a NOMA-group and reuse conventional cellular user's (CU's) channel for computation offloading. In spite of the benefit of spectrum sharing, the resulting co-channel interference degrades the CU's transmission. We thus firstly focus on a single-cell scenario of two VUs reusing one CU's channel, and analyze the CU's increased delay due to sharing channel with the VUs. We then jointly optimize the VUs’ partial offloading and the allocation of the communication and computing resources to minimize the VUs’ delay while limiting the CU's suffered increased delay. An efficient layered-algorithm is proposed to tackle with the non-convexity of the joint optimization problem. Based on our study on the single-cell scenario, we further investigate the multi-cell scenario in which a group of VUs flexibly form pairs to reuse the channels of different CUs for offloading, and formulate an optimal pairing problem to minimize the VUs’ overall-delay. To address the difficulty due to the combinatorial nature of the pairing problem, we propose a cross-entropy (CE) based probabilistic learning algorithm to find the optimal VU-pairings. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms for both the single-cell scenario and multi-cell scenario. The results also demonstrate that our NOMA-assisted MEC via spectrum sharing can outperform the conventional frequency division multiple access assisted offloading scheme.
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- 2021
121. Demand Response Management via Real-Time Electricity Price Control in Smart Grids.
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Li Ping Qian 0001, Ying Jun (Angela) Zhang, Jianwei Huang 0001, and Yuan Wu 0001
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- 2013
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122. Joint Base Station Association and Power Control via Benders' Decomposition.
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Li Ping Qian 0001, Ying Jun (Angela) Zhang, Yuan Wu 0001, and Jieying Chen
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- 2013
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123. Pareto Optimal Power Control via Bisection Searching in Wireless Networks.
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Li Ping Qian 0001, Yuan Wu 0001, Shengli Zhang, and Qingzhang Chen
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- 2013
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124. Distributed Nonconvex Power Control using Gibbs Sampling.
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Li Ping Qian 0001, Ying-Jun Angela Zhang, and Mung Chiang
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- 2012
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125. Energy-Efficient Delay-Constrained Transmission and Sensing for Cognitive Radio Systems.
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Yuan Wu 0001, Vincent K. N. Lau, Danny H. K. Tsang, and Li Ping Qian 0001
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- 2012
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126. S-MAPEL: monotonic optimization for non-convex joint power control and scheduling problems.
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Li Ping Qian 0001 and Ying Jun Zhang
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- 2010
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127. MAPEL: Achieving global optimality for a non-convex wireless power control problem.
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Li Ping Qian 0001, Ying Jun Zhang, and Jianwei Huang 0001
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- 2009
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128. Vehicular Networking-Enabled Vehicle State Prediction via Two-Level Quantized Adaptive Kalman Filtering
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Yuan Wu, Wenchao Xu, Li Ping Qian, Feng Anqi, and Ningning Yu
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Vehicular ad hoc network ,Computer Networks and Communications ,Computer science ,Real-time computing ,020302 automobile design & engineering ,020206 networking & telecommunications ,02 engineering and technology ,Kalman filter ,Computer Science Applications ,Moment (mathematics) ,Acceleration ,0203 mechanical engineering ,Autoregressive model ,Hardware and Architecture ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Trajectory ,Vehicle acceleration ,Autoregressive–moving-average model ,Enhanced Data Rates for GSM Evolution ,Information Systems - Abstract
The accurate prediction of vehicle state based on the data acquired by the vehicular networking system plays an important role in improving traffic safety in the transportation section. However, it is difficult to accurately predict the vehicle state due to the highly dynamic road environment and various drivers’ behaviors. To this end, in this article, we propose a two-level quantized adaptive Kalman filter (KF) algorithm based on the autoregressive moving average (MA) model to predict the vehicle state (including the moving direction, driving lane, vehicle speed, and acceleration). First, we propose a vehicular networking system to acquire the vehicle data by exchanging traffic data between the onboard unit and the roadside unit (RSU). Then, we predict the vehicle state at the edge cloud server (ECS) equipped at the RSU. Specifically, we utilize the autoregressive MA model to predict vehicle acceleration at the next moment. Then, the predicted vehicle acceleration is used as an input variable of the adaptive KF model to predict the vehicle location and speed at the next moment, in which we quantify the predicted vehicle location to the moving direction and the driving lane. Finally, the ECS broadcasts the predicted state to other RSUs. Through the communication with the road unit, all vehicles moving at the intersection can share vehicles states each other. In this doing, we can efficiently improve traffic safety in the intersection. We provide numerical simulations to validate the effectiveness of the autoregressive MA model used for predicting acceleration. Then, we evaluate the efficiency of the proposed two-level quantized adaptive KF algorithm. Compared with five conventional prediction algorithms, our proposed algorithm can improve the speed prediction accuracy by 90.62%, 89.81%, 88.91%, 82.76%, and 70.77%, respectively, which implies that our algorithm is a promising scheme for predicting the vehicle state in vehicular networks.
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- 2020
129. Energy-Efficient Multi-task Multi-access Computation Offloading Via NOMA Transmission for IoTs
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Cai Jiali, Fen Hou, Binghua Shi, Yuan Wu, Li Ping Qian, and Xuemin Sherman Shen
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Mobile edge computing ,Computer science ,Distributed computing ,020208 electrical & electronic engineering ,02 engineering and technology ,Energy consumption ,Computer Science Applications ,Task (computing) ,Control and Systems Engineering ,Server ,0202 electrical engineering, electronic engineering, information engineering ,Computation offloading ,Resource management ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering ,Edge computing ,Information Systems ,Efficient energy use - Abstract
Driven by the explosive growth in computation-intensive applications in future 5G networks and industries, mobile edge computing (MEC), which enables smart terminals (STs) to offload their computation workloads to nearby edge servers (ESs) in radio access networks, has attracted increasing attention. In this article, we investigate the energy-efficient multitask multiaccess MEC via nonorthogonal multiple access (NOMA). Exploiting NOMA, an ST with multiple tasks can offload the respective computation workloads of different tasks to different ESs simultaneously. To study this problem, we adopt a two-step approach. Specifically, we first consider a given task-ES assignment and formulate a joint optimization of the tasks’ computation offloading, local computation-resource allocation, and the NOMA-transmission duration, with the objective of minimizing the ST's total energy consumption for completing all tasks. Next, based on the optimal offloading solution for the given task-ES assignment, we further investigate how to properly assign different tasks to the ESs for further minimizing the ST's total energy consumption. For both the formulated problems, we propose efficient algorithms to compute the respective solutions. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results also show that our proposed NOMA-enabled multitask multiaccess computation offloading can outperform conventional orthogonal multiple access based offloading scheme, especially when the tasks have heavy computation-workload requirements and stringent delay limits.
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- 2020
130. Latency Optimization for Cellular Assisted Mobile Edge Computing via Non-Orthogonal Multiple Access
- Author
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Zhiguo Shi, Bin Lin, Yuan Wu, Weijia Jia, Jinyuan Ouyang, and Li Ping Qian
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Mobile edge computing ,Optimization problem ,Computer Networks and Communications ,Computer science ,Distributed computing ,Aerospace Engineering ,020302 automobile design & engineering ,02 engineering and technology ,0203 mechanical engineering ,Automotive Engineering ,Computation offloading ,Electrical and Electronic Engineering ,Latency (engineering) ,Communication channel - Abstract
In this article, we investigate the cellular assisted mobile edge computing (MEC) via non-orthogonal multiple access (NOMA), where a group of edge-computing users (EUs) exploit NOMA to simultaneously offload their computation-workloads to an edge-server (ES), and conventional cellular-user (CU) allows the EUs to reuse its authorized frequency channel for the NOMA-transmission. We firstly characterize the transmit-powers of the CU and EUs and then formulate a joint optimization of the EUs’ offloaded computation-workloads, offloading-duration (i.e., how long for reusing the CU's channel), as well as the ES's computation-resource allocations (for processing different EUs’ offloaded workloads), with the objective of minimizing the overall-latency in completing all EUs’ computation-requirements, subject to the CU‘s and EUs’ limited power and energy capacities as well as the ES's limited computation-resource capacity. Despite the strict non-convexity of the formulated joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. With the optimal offloading solution for the scenario of one CU, we further investigate the scenario of multiple CUs and investigate the optimal pairing of the EUs for reusing different CUs’ channels for computation-offloading. Taking into account the coupling effect due to the ES's limited computation-resource, we formulate a joint optimization of the EU-pairing and the ES's capacity allocation of the computation-resource for accommodating different EU-pairs. Despite the difficulty due to the mixed binary and non-linear non-convex programming of the formulated problem, we propose an efficient layered algorithm for solving the problem. Numerical results are provided to validate the accuracy of our proposed algorithms. We also show the performance advantage of our NOMA-assisted offloading in comparison with conventional orthogonal multiple access (OMA) based computation offloading.
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- 2020
131. A Grant-Free Random Access Scheme for M2M Communication in Massive MIMO Systems
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Wenchao Zhai, Ying Li, Li Ping Qian, and Huimei Han
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Computer Networks and Communications ,business.industry ,Computer science ,0211 other engineering and technologies ,Word error rate ,020206 networking & telecommunications ,Throughput ,02 engineering and technology ,Independent component analysis ,Computer Science Applications ,Hardware and Architecture ,Signal Processing ,Telecommunications link ,0202 electrical engineering, electronic engineering, information engineering ,Overhead (computing) ,business ,Throughput (business) ,Decoding methods ,Random access ,Computer Science::Information Theory ,021101 geological & geomatics engineering ,Information Systems ,Communication channel ,Computer network - Abstract
A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input–multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time–frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.
- Published
- 2020
132. NOMA-Enabled Mobile Edge Computing for Internet of Things via Joint Communication and Computation Resource Allocations
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Yuan Wu, Li Ping Qian, Bo Sun, Danny H. K. Tsang, and Binghua Shi
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Optimization problem ,Mobile edge computing ,Computer Networks and Communications ,Computer science ,Computation ,Distributed computing ,020206 networking & telecommunications ,020302 automobile design & engineering ,Workload ,02 engineering and technology ,Computer Science Applications ,Base station ,0203 mechanical engineering ,Hardware and Architecture ,Server ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Resource allocation ,Computation offloading ,Quality of experience ,Enhanced Data Rates for GSM Evolution ,Information Systems - Abstract
The past decades have witnessed an explosive growth of the Internet of Things (IoT) services requiring intensive computation resources. The conventional IoT devices, however, are usually equipped with very limited computation resources, which results in degraded quality of experience when executing the resource-hungry applications. Mobile edge computing (MEC), which enables smart terminals (STs) to offload parts of their computation workloads to the edge servers located at cellular base stations (BSs), has provided a promising approach to address this issue. In this article, we investigate the nonorthogonal multiple access (NOMA)-enabled multiaccess MEC. Specifically, by exploiting the advanced NOMA, an ST can simultaneously offload its computation workloads to different edge servers (ESs), which thus reduces the overall delay in completing the ST’s computation workloads. To study this problem, we formulate a joint optimization of the computation resource allocations at the ESs, the ST’s offloaded workloads and its radio resource allocations for NOMA transmission, with the objective of minimizing a system wise cost that accounts for the overall delay in finishing the ST’s total computation workload and the total computation resource usage cost at the ESs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient layered algorithm to find the optimal solution. By exploiting the optimal offloading solution of a single ST, we further investigate the scenario of multiple STs and propose two algorithms to determine the optimal grouping among different ESs for serving the STs, with one algorithm aiming at minimizing the total cost of all STs and the other algorithm aiming at determining the Nash stable grouping for the ESs. Numerical results are presented to validate the effectiveness of our proposed algorithms and show the performance gain of our proposed NOMA-enabled multiaccess computation offloading.
- Published
- 2020
133. Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning
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Ningning Yu, Li Ping Qian, and Chengxiao Shen
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reinforcement learning ,Technology ,Computational complexity theory ,Computer science ,QH301-705.5 ,QC1-999 ,adaptive clustering ,Convolutional neural network ,Reinforcement learning ,General Materials Science ,Biology (General) ,Cluster analysis ,Instrumentation ,QD1-999 ,Fluid Flow and Transfer Processes ,business.industry ,Process Chemistry and Technology ,Physics ,General Engineering ,Pattern recognition ,Engineering (General). Civil engineering (General) ,Spectral clustering ,face feature extraction ,Computer Science Applications ,Range (mathematics) ,Chemistry ,ComputingMethodologies_PATTERNRECOGNITION ,face clustering ,Face (geometry) ,Artificial intelligence ,TA1-2040 ,business ,Focus (optics) - Abstract
In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters, our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.
- Published
- 2021
134. Energy Optimization for NOMA assisted Federated Learning with Secrecy Provisioning
- Author
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Tianshun Wang, Li Ping Qian, Yuxiao Song, Bin Lin, Yuan Wu, and Xumin Huang
- Subjects
Base station ,Optimization problem ,business.industry ,Computer science ,Heuristic (computer science) ,Node (networking) ,Telecommunications link ,Physical layer ,Wireless ,Throughput ,business ,Computer network - Abstract
Federated learning (FL) has been considered as an efficient yet privacy-preserving approach for enabling the distributed learning. There have been many studies investigating the applications of FL in different scenarios, e.g., Internet of Things, Internet of Vehicles, and UAV systems. However, due to delivering the trained model via wireless links, FL may suffer from a potential issue, i.e., some malicious users may intentionally overhear the trained model delivered through the wireless links. In this paper, we investigate the energy optimization for nonorthogonal multiple access (NOMA) assisted with secrecy provisioning. Specifically, we consider that the wireless devices (WDs) adopt NOMA to deliver their respectively trained local models to a base station (BS) which serves a parameter-server, and there exists a malicious node that overhears the parameter-server when delivering the aggregated global model to all WDs. We adopt the physical layer security to quantify the secrecy throughput under the eavesdropping attack and formulate an optimization problem to minimize the overall energy consumption of all the WDs in FL, by jointly optimizing the uplink time, the downlink time, the local model accuracy, and the uplink decoding order of NOMA. In spite of the non-convexity of this joint optimization problem, we propose an efficient algorithm, which is based on the theory of monotonic optimization, for finding the solution. Numerical results show that our proposed algorithm can achieve the almost same solutions as the LINGO's global-solver while reducing more than 90% computation-time than LINGO. Moreover, the results also show that our proposed NOMA decoding scheme can outperform some heuristic decoding schemes.
- Published
- 2021
135. Non-orthogonal Multiple Access assisted Federated Learning for UAV Swarms: An Approach of Latency Minimization
- Author
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Tianshun Wang, Yuan Wu, Li Ping Qian, Yuxiao Song, and Zhiguo Shi
- Subjects
business.industry ,Computer science ,Real-time computing ,Bandwidth (signal processing) ,Swarm behaviour ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,ComputingMethodologies_ARTIFICIALINTELLIGENCE ,Broadcasting (networking) ,Telecommunications link ,Wireless ,Minification ,Latency (engineering) ,Duration (project management) ,business - Abstract
Equipped with machine learning (ML) models, unmanned aerial vehicle (UAV) swarms can execute various applications like surveillance and target detection. However, the connections between UAVs and cloud servers cannot be guaranteed, especially when executing massive data. Thus, traditional cloud-centric approach will not be suitable, since it may cause high latency and significant bandwidth consumption. In this work, we propose a federated learning (FL) framework via non-orthogonal multiple access (NOMA) for a UAV swarm which is composed of a leader-UAV and a group of follower-UAVs. Specifically, each follower-UAV updates its local model by using its collected data, and then all follower-UAVs form a NOMA-group to send their respectively trained FL parameters (i.e., the local FL models) to the leader-UAV simultaneously. We formulate a joint optimization of the uplink NOMA-transmission durations, downlink broadcasting duration, as well as the computation-rates of the leader-UAV and all follower-UAVs, aiming at minimizing the latency in executing the FL iterations until reaching a specified accuracy. Numerical results are presented to verify the effectiveness of our proposed algorithm, and demonstrate that the proposed algorithm can outperform some baseline strategies.
- Published
- 2021
136. Optimal Channel Sharing assisted Multi-user Computation Offloading via NOMA
- Author
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Tianshun Wang, Weijia Jia, Yuan Wu, Yang Li, Lin Bin, and Li Ping Qian
- Subjects
Mobile edge computing ,Transmission (telecommunications) ,Computer science ,business.industry ,Server ,Computation offloading ,Wireless ,Multi-user ,business ,Edge computing ,Communication channel ,Computer network - Abstract
Computation offloading via mobile edge computing (MEC) has been considered as one of the promising paradigms for enabling computation-intensive yet latency-sensitive services on resource-limited mobile terminals in future wireless systems. In this paper, we propose a channel sharing assisted multi-user computation offloading in MEC via non-orthogonal multiple access (NOMA), in which a group of edge-computing users (EUs) form a NOMA-group and further reuse the cellular user’s (CU’s) channel for computation-offloading transmission. Specifically, we formulate a joint optimization of the EUs’ offloaded workloads, NOMA-transmission duration, as well as the processing-rate allocations (for different EUs) at the edge computing server (ECS), with the objective of minimizing the overall latency for all EUs to complete their tasks. In spite of the non-convexity of the joint optimization, we identify the structural feature of the optimal offloading solution and propose a layered-algorithm for finding the solution efficiently. Numerical results are provided to validate the effectiveness of our proposed algorithm as well as the advantage of our multi-user offloading scheme via NOMA.
- Published
- 2021
137. Delay-Minimization Nonorthogonal Multiple Access Enabled Multi-User Mobile Edge Computation Offloading
- Author
-
Li Ping Qian, Kejie Ni, Xuemin Shen, Yuan Wu, and Cheng Zhang
- Subjects
Mobile edge computing ,Optimization problem ,Computer science ,Computation ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,Multi-user ,Base station ,Server ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Computation offloading ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering - Abstract
The significant advances of cellular systems and mobile Internet services have yielded a variety of computation intensive applications, resulting in great challenge to mobile terminals (MTs) with limited computation resources. Mobile edge computing, which enables MTs to offload their computation tasks to edge servers located at cellular base stations (BSs), has provided a promising approach to address this challenging issue. Considering the advantage of improving transmission efficiency provided by nonorthogonal multiple access (NOMA), we propose an NOMA-enabled computation offloading scheme, in which a group of MTs offload partial of their computation workloads to an edge server based on the NOMA transmission. After finishing all MTs’ offloaded computation workloads, the edge server sends the computation results back to the MTs based on NOMA. We aim at minimizing the overall delay for completing all MTs’ computation requirements, which is achieved by jointly optimizing the MTs’ offloaded computation workloads, and the uploading duration for the MTs to send their computation workloads to the BS, and the downloading-duration for the BS to send the computation results back to the MTs. Despite the nonconvexity of the joint optimization problem, we exploit its layered structure and propose an efficient algorithm to compute the optimal offloading solution. Numerical results are provided to validate the accuracy and efficiency of our proposed algorithm and show the performance advantage of our NOMA-enabled computation-offloading scheme.
- Published
- 2019
138. Optimal SIC Ordering and Computation Resource Allocation in MEC-Aware NOMA NB-IoT Networks
- Author
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Zhiguo Shi, Feng Anqi, Bo Ji, Yuan Wu, Huang Yupin, and Li Ping Qian
- Subjects
Optimization problem ,Mobile edge computing ,Computer Networks and Communications ,Computer science ,Computation ,Distributed computing ,020302 automobile design & engineering ,020206 networking & telecommunications ,02 engineering and technology ,Bottleneck ,Computer Science Applications ,0203 mechanical engineering ,Single antenna interference cancellation ,Hardware and Architecture ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Task analysis ,Resource allocation ,Resource management ,Performance improvement ,Information Systems - Abstract
Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) have been emerging as promising techniques in narrowband Internet of Things (NB-IoT) systems to provide ubiquitously connected IoT devices with efficient transmission and computation. However, the successive interference cancellation (SIC) ordering of NOMA has become the bottleneck limiting the performance improvement for the uplink transmission, which is the dominant traffic flow of NB-IoT communications. Also, in order to guarantee the fairness of task execution latency across NB-IoT devices, the computation resource of MEC units has to be fairly allocated to tasks from IoT devices according to the task size. For these reasons, we investigate the joint optimization of SIC ordering and computation resource allocation in this paper. Specifically, we formulate a combinatorial optimization problem with the objective to minimize the maximum task execution latency required per task bit across NB-IoT devices under the limitation of computation resource. We prove the NP-hardness of this joint optimization problem. To tackle this challenging problem, we first propose an optimal algorithm to obtain the optimal SIC ordering and computation resource allocation in two stages: the convex computation resource allocation optimization followed by the combinatorial SIC ordering optimization. To reduce the computational complexity, we design an efficient heuristic algorithm for the SIC ordering optimization. As a good feature, the proposed low-complexity algorithm suffers a negligible performance degradation in comparison with the optimal algorithm. Simulation results demonstrate the benefits of NOMA in reducing the task execution latency.
- Published
- 2019
139. Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing
- Author
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Xu Feng, Liang Huang, Yuan Wu, Li Ping Qian, and Cheng Zhang
- Subjects
Mobile edge computing ,Optimization problem ,lcsh:T58.5-58.64 ,lcsh:Information technology ,Computer Networks and Communications ,Computer science ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,020210 optoelectronics & photonics ,Bandwidth allocation ,Hardware and Architecture ,Server ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Reinforcement learning ,Computation offloading ,Enhanced Data Rates for GSM Evolution - Abstract
The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance. Keywords: Mobile edge computing, Joint computation offloading and resource allocation, Deep-Q network
- Published
- 2019
140. Resource-Efficient NOMA Transmission via Joint Bandwidth and Rate Allocations
- Author
-
Li Ping Qian, Wang Xiaoding, Yuan Wu, and Kejie Ni
- Subjects
Mathematical optimization ,Optimization problem ,Computer science ,020206 networking & telecommunications ,Throughput ,02 engineering and technology ,Computer Science Applications ,Channel capacity ,Base station ,Transmission (telecommunications) ,Modeling and Simulation ,Telecommunications link ,0202 electrical engineering, electronic engineering, information engineering ,Bandwidth (computing) ,Resource management ,Electrical and Electronic Engineering - Abstract
In this letter, we investigate the joint channel bandwidth and rate allocations for downlink non-orthogonal multiple access with the objective of maximizing the resource utilization efficiency (RUE). In particular, we take into account the utility of base station (BS) in serving the mobile terminals’ traffic and the cost in consuming channel bandwidth and measure the RUE as the ratio between the BS’s utility and the total power consumption. Despite the non-convexity of the joint optimization problem, we propose a three-layered vertical decomposition and design an efficient algorithm to compute the optimal solution. Extensive numerical results are provided to validate the performance of our proposed algorithms.
- Published
- 2019
141. Ensemble Learning for Facial Age Estimation Within Non-Ideal Facial Imagery
- Author
-
Huang Yupin, Yuan Wu, Li Ping Qian, and Ningning Yu
- Subjects
image preprocessing ,General Computer Science ,Biometrics ,Computer science ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,0211 other engineering and technologies ,02 engineering and technology ,transfer learning ,Convolutional neural network ,deep convolutional neural network ,Facial age estimation ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,021110 strategic, defence & security studies ,Training set ,business.industry ,General Engineering ,Pattern recognition ,Ensemble learning ,Support vector machine ,ensemble learning ,RGB color model ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,Transfer of learning ,lcsh:TK1-9971 - Abstract
Human facial age estimation has been widely used in many computer vision applications, including security surveillance, forensics, biometrics, human-computer interaction (HCI), and so on. We propose a facial age estimation method oriented to non-ideal facial imagery. The method consists of image preprocessing, feature extraction, and age predication. First, we preprocess non-ideal input images in RGB stream, luminance modified (LM) stream, and YIQ stream. Then, we leverage the deep convolutional neural networks (DCNNs) to extract the feature of images preprocessed in each stream. To reduce the training data volume and training complexity, we adopt the transfer learning to build the DCNN structure. With the extracted feature, the weak classifier equipped at every stream is designed to obtain a weak classification prediction of the age range. Moreover, in order to generate estimation, we use the ensemble learning to fuse the three weak classifiers. We design an integrated strategy algorithm based on the combination of voting method and weighted average method. The simulation results show that our proposed algorithm can improve the an exact match (AEM) and an error of one age category (AEO) by 4.75% and 6.75% compared with the best AEM and AEO of the three weak classifiers. Furthermore, in comparison with the unweighted average method, our proposed algorithm can improve the AEM and AEO by 8.68% and 12.79%, respectively.
- Published
- 2019
142. Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
- Author
-
Huang Yupin, Yuan Wu, Ningning Yu, Feng Anqi, and Li Ping Qian
- Subjects
0209 industrial biotechnology ,deep belief network ,General Computer Science ,Computer science ,Real-time computing ,General Engineering ,02 engineering and technology ,Short-term traffic prediction ,Term (time) ,Deep belief network ,020901 industrial engineering & automation ,edge computing ,ComputerSystemsOrganization_MISCELLANEOUS ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Hidden Markov model ,hidden Markov model ,lcsh:TK1-9971 ,Edge computing ,5G - Abstract
Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.
- Published
- 2019
143. Optimal Dual-Connectivity Traffic Offloading in Energy-Harvesting Small-Cell Networks
- Author
-
Haibo Zhou, Mohamad Khattar Awad, Yuan Wu, Li Ping Qian, Xuemin Shen, and Xiaowei Yang
- Subjects
021103 operations research ,Optimization problem ,Exploit ,Computer Networks and Communications ,Renewable Energy, Sustainability and the Environment ,Computer science ,Quality of service ,Distributed computing ,0211 other engineering and technologies ,020206 networking & telecommunications ,02 engineering and technology ,Scheduling (computing) ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Small cell ,Macro ,Energy harvesting - Abstract
Traffic offloading through heterogenous small-cell networks (HSCNs) has been envisioned as a cost-efficient approach to accommodate the tremendous traffic growth in cellular networks. In this paper, we investigate an energy-efficient dual-connectivity (DC) enabled traffic offloading through HSCNs, in which small cells are powered in a hybrid manner including both the conventional on-grid power-supply and renewable energy harvested from environment. To achieve a flexible traffic offloading, the emerging DC-enabled traffic offloading in 3GPP specification allows each mobile user (MU) to simultaneously communicate with a macro cell and offload data through a small cell. In spite of saving the on-grid power consumption, powering traffic offloading by energy harvesting (EH) might lead to quality of service degradation, e.g., when the EH power-supply fails to support the required offloading rate. Thus, to reap the benefits of the DC-capability and the EH power-supply, we propose a joint optimization of traffic scheduling and power allocation that aims at minimizing the total on-grid power consumption of macro and small cells, while guaranteeing each served MU’s traffic requirement. We start by studying a representative case of one small cell serving a group of MUs. In spite of the non-convexity of the formulated joint optimization problem, we exploit its layered structure and propose an algorithm that efficiently computes the optimal offloading solution. We further study the scenario of multiple small cells, and investigate how the small cells select different MUs for maximizing the system-wise reward that accounts for the revenue for offloading the MUs’ traffic and the cost of total on-grid power consumption of all cells. We also propose an efficient algorithm to find the optimal MU-selection solution. Numerical results are provided to validate our proposed algorithms and show the advantage of our proposed DC-enabled traffic offloading through the EH-powered small cells.
- Published
- 2018
144. NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation
- Author
-
Li Ping Qian, Yuan Wu, Danny H. K. Tsang, Kejie Ni, and Cheng Zhang
- Subjects
Mobile edge computing ,Optimization problem ,Computer Networks and Communications ,Computer science ,Distributed computing ,Aerospace Engineering ,020206 networking & telecommunications ,020302 automobile design & engineering ,02 engineering and technology ,Energy consumption ,0203 mechanical engineering ,Distributed algorithm ,Automotive Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Computation offloading ,Resource management ,Enhanced Data Rates for GSM Evolution ,Electrical and Electronic Engineering ,Edge computing - Abstract
Multi-access mobile edge computing (MEC), which enables mobile users (MUs) to offload their computation-workloads to the computation-servers located at the edge of cellular networks via multi-access radio access, has been considered as a promising technique to address the explosively growing computation-intensive applications in mobile Internet services. In this paper, by exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, we study the NOMA-enabled multi-access MEC. We aim at minimizing the overall delay of the MUs for finishing their computation requirements, by jointly optimizing the MUs’ offloaded workloads and the NOMA transmission-time. Despite the non-convexity of the formulated joint optimization problem, we propose efficient algorithms to find the optimal offloading solution. For the single-MU case, we exploit the layered structure of the problem and propose an efficient layered algorithm to find the MU's optimal offloading solution that minimizes its overall delay. For the multi-MU case, we propose a distributed algorithm (in which the MUs individually optimize their respective offloaded workloads) to determine the optimal offloading solution for minimizing the sum of all MUs’ overall delay. Extensive numerical results have been provided to validate the effectiveness of our proposed algorithms and the performance advantage of our NOMA-enabled multi-access MEC in comparison with conventional orthogonal multiple access enabled multi-access MEC.
- Published
- 2018
145. Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
- Author
-
Liang Huang, Huang Yupin, Li Ping Qian, Xu Feng, and Feng Anqi
- Subjects
Mobile edge computing ,Computer Networks and Communications ,Computer science ,business.industry ,Computation ,Distributed computing ,Quality of service ,Deep learning ,020206 networking & telecommunications ,020302 automobile design & engineering ,02 engineering and technology ,Bandwidth allocation ,0203 mechanical engineering ,Hardware and Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,Artificial intelligence ,business ,Integer programming ,Software ,Information Systems ,Curse of dimensionality - Abstract
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.
- Published
- 2018
146. Optimal Power Allocation and Scheduling for Non-Orthogonal Multiple Access Relay-Assisted Networks
- Author
-
Haowei Mao, Xiaowei Yang, Li Ping Qian, Yuan Wu, Xuemin Shen, and Haibo Zhou
- Subjects
Job shop scheduling ,Computer Networks and Communications ,Computer science ,business.industry ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Time division multiple access ,020206 networking & telecommunications ,020302 automobile design & engineering ,Throughput ,Data_CODINGANDINFORMATIONTHEORY ,02 engineering and technology ,Non orthogonal ,Scheduling (computing) ,law.invention ,0203 mechanical engineering ,Relay ,law ,Telecommunications link ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Electrical and Electronic Engineering ,business ,Software ,Computer network ,Communication channel - Abstract
The emerging non-orthogonal multiple access (NOMA), which enables mobile users (MUs) to share same frequency channel simultaneously, has been considered as a spectrum-efficient multiple access scheme to accommodate tremendous traffic growth in future cellular networks. In this paper, we investigate the NOMA downlink relay-transmission, in which the macro base station (BS) first uses NOMA to transmit to a group of relays, and all relays then use NOMA to transmit their respectively received data to an MU. In specific, we propose an optimal power allocation problem for the BS and relays to maximize the overall throughput delivered to the MU. Despite the non-convexity of the problem, we adopt the vertical decomposition and propose a layered-algorithm to efficiently compute the optimal power allocation solution. Numerical results show that the proposed NOMA relay-transmission can increase the throughput up to 30 percent compared with the conventional time division multiple access (TDMA) scheme, and we find that increasing the relays’ power capacity can increase the throughput gain of the NOMA relay against the TDMA relay. Furthermore, to improve the throughput under weak channel power gains, we propose a hybrid NOMA (HB-NOMA) relay that adaptively exploits the benefit of NOMA relay and that of the interference-free TDMA relay. By using the throughput provided by the HB-NOMA relay for each individual MU, we study the multi-MUs scenario and investigate the multi-MUs scheduling problem over a long-term period to maximize the overall utility of all MUs. Numerical results demonstrate the performance advantage of the proposed multi-MUs scheduling that adopts the HB-NOMA relay-transmission.
- Published
- 2018
147. MoMAC: Mobility-Aware and Collision-Avoidance MAC for Safety Applications in VANETs
- Author
-
Hongzi Zhu, Li Ping Qian, Haibo Zhou, Minglu Li, Wenchao Xu, Feng Lyu, and Xuemin Shen
- Subjects
Computer Networks and Communications ,Wireless ad hoc network ,Computer science ,business.industry ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Time division multiple access ,Aerospace Engineering ,020206 networking & telecommunications ,020302 automobile design & engineering ,Topology (electrical circuits) ,Access control ,02 engineering and technology ,0203 mechanical engineering ,Transmission (telecommunications) ,Automotive Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,business ,Protocol (object-oriented programming) ,Collision avoidance ,Computer network - Abstract
Time-division multiple access (TDMA) based medium access control (MAC) protocol provides a promising solution to well support delay-sensitive safety applications in vehicular ad hoc networks, since a time-slotted access scheme ensures the transmission within the ultra-low delays. However, due to the varying vehicle mobility, existing TDMA-based MAC protocols may result in collisions of slot assignment when multiple sets of vehicles move together. To avoid slot-assignment collisions, in this paper, we propose a mobility-aware TDMA MAC, named as MoMAC, which can assign every vehicle a time slot according to the underlying road topology and lane distribution on roads with the consideration of vehicles’ mobilities. In MoMAC, different lanes on the same road segment and different road segments at intersections are associated with disjoint time slot sets. In addition, each vehicle broadcasts safety messages together with the time slot occupying information of neighboring vehicles; by updating time slot occupying information of two-hop neighbors (obtained indirectly from one-hop neighbors), vehicles can detect time slot collisions and access a vacant time slot in a fully distributed way. We demonstrate the efficiency of MoMAC through theoretical analysis and extensive simulations; compared with state-of-the-art TDMA MACs, the transmission collisions can be reduced by $59.2\%$ , and the rate of safety message transmissions/receptions can be greatly enhanced.
- Published
- 2018
148. Resource optimisation for downlink non‐orthogonal multiple access systems: a joint channel bandwidth and power allocations approach
- Author
-
Feng Xu, Mao Haowei, Li Ping Qian, Kejie Ni, Huang Liang, Wu Yuan, and Zhiguo Shi
- Subjects
Computer science ,020302 automobile design & engineering ,020206 networking & telecommunications ,02 engineering and technology ,Spectral efficiency ,Computer Science Applications ,Channel capacity ,Bandwidth allocation ,0203 mechanical engineering ,Single antenna interference cancellation ,Telecommunications link ,0202 electrical engineering, electronic engineering, information engineering ,Bandwidth (computing) ,Electronic engineering ,Resource allocation ,Electrical and Electronic Engineering ,Computer Science::Information Theory ,Communication channel - Abstract
The emerging non-orthogonal multiple access (NOMA) has been considered as a promising scheme to reach the goals of 5G cellular systems. By enabling a group of mobile users (MUs) to share a same frequency channel and adopting the successive interference cancellation to mitigate the co-channel interference, NOMA can improve the spectrum efficiency compared with the orthogonal multiple access (OMA). This study proposes a joint optimisation scheme of the channel bandwidth and the transmit-power allocations for the NOMA downlink transmission, which aims at minimising the overall resource consumption cost including both the spectrum consumption and the power consumption, while satisfying the MUs' traffic requirements. In spite of the non-convexity nature of the joint optimisation problem, this study characterises the connection between the channel bandwidth and the associated transmit powers for the MUs. Based on this connection, this study transforms the joint optimisation problem into an equivalent bandwidth optimisation problem, and further proposes an efficient algorithm to compute the optimal bandwidth allocation (which enables us to derive the corresponding transmit powers for the MUs). Extensive numerical results are provided to validate the proposed algorithm and the advantage of the proposed joint channel bandwidth and power allocations for the NOMA transmission.
- Published
- 2018
149. Optimal Resource Allocations for Mobile Data Offloading via Dual-Connectivity
- Author
-
Xuemin Shen, Yuan Wu, Yanfei He, Jianwei Huang, and Li Ping Qian
- Subjects
Optimization problem ,Channel allocation schemes ,Computer Networks and Communications ,business.industry ,Computer science ,05 social sciences ,050801 communication & media studies ,020206 networking & telecommunications ,02 engineering and technology ,Scheduling (computing) ,Base station ,0508 media and communications ,Bandwidth allocation ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Resource allocation ,Resource management ,Small cell ,Electrical and Electronic Engineering ,business ,Mobile data offloading ,Software ,Computer network - Abstract
The rapid growth of mobile traffic has heavily overloaded the cellular networks, making it increasingly desirable to offload mobile users’ (MUs’) traffic to small-cell networks. In this paper, we study the MUs’ optimal uplink traffic offloading scheme based on the new paradigm of small-cell dual-connectivity (DC). Through DC, an MU can flexibly schedule its traffic between a macro-cell base station (BS) and a small-cell access point (AP) via two different radio interfaces. To optimize the overall network radio resource usage, we jointly optimize the BS’ bandwidth allocation as well as the MUs’ traffic scheduling and power allocation. Specifically, for reducing the bandwidth usage, the BS prefers to allocate the MUs small amount of bandwidth to encourage the MUs to utilize the small-cell networks. However, excessive traffic offloading can lead to severe interferences among MUs, which increase the MUs’ power consumption. Hence, our joint optimization strikes a proper balance between these two aspects. Despite the non-convexity of the proposed joint optimization problem, we propose an efficient algorithm to compute the optimal offloading solution. The key idea is to exploit the layered-structure of the joint optimization problem, and decompose it into the BS’ bandwidth allocation problem (on the top-level) and the MUs’ traffic scheduling and power allocation problem (as a subproblem). Such a decomposition enables us to exploit the hidden convexity of the MUs’ problem and the monotonic structure of the BS’ problem for an effective algorithm design. Numerical results show that our proposed algorithm can achieve the global optimum solution with significantly reduced computational time. Moreover, the proposed traffic offloading scheme can significantly reduce the overall system cost, in comparison with using the fixed bandwidth allocation or traffic scheduling schemes.
- Published
- 2018
150. Optimal Power Allocation for Secure Non-orthogonal Multiple Access Transmission
- Author
-
Weidang Lu, Li Ping Qian, Liang Huang, Yuan Wu, Wu Weicong, and Ningning Yu
- Subjects
Optimization problem ,Computer science ,business.industry ,Distributed computing ,020302 automobile design & engineering ,020206 networking & telecommunications ,Throughput ,Jamming ,Eavesdropping ,02 engineering and technology ,0203 mechanical engineering ,Transmission (telecommunications) ,Search algorithm ,0202 electrical engineering, electronic engineering, information engineering ,Wireless ,business ,Throughput (business) - Abstract
Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users’ power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1
- Published
- 2020
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