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认知中继三跳网络联合优化 Article

澄 赵,万良 王,信威 姚,双华 杨

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 253-261 doi: 10.1631/FITEE.1601414

Abstract: 认知中继网络中，传输的吞吐量和传输距离一直是衡量性能的重要指标。现有的研究多数都集中在两跳网络的优化，但其也存在着传输距离不长，只能进行单项传输等缺点。本文提出了一种新的使用认知中继的三跳网络传输方案，通过三阶段的传输过程，实现了次级用户之间的双向传输。同时，引入了叠加编码技术来处理网络中双接收节点的情况。本文将这个三阶段的联合优化问题转化为一个非线性的优化问题，并进行了求解。仿真结果表明，本文提出的优化方法可以在不增加中继跳数的情况下，延长主用户传输距离，并同时提高次级用户的传输吞吐量。

Keywords： 解码转发；三跳；认知中继网络；时间功率分配；叠加编码    

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Secrecy outage performance for wireless-powered relaying systems with nonlinear energy harvesters Article

继亮 张,高峰 潘,宜原 解

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 246-252 doi: 10.1631/FITEE.1601352

Abstract: 本文考虑了一个包含单一信源、单一信宿、N (N>1)个无线充电中继和单一窃听者的协作系统。本文假设每个中继都拥有一个非线性的能量收集器，且该能量收集器存在一个饱和阈值以限制收集能量的大小。在考虑解码转发和功率分配接收器的场景中，本文选择第K个最优中继来协助信源−中继−信宿链路的传输。同时，本文还推导了保密中断概率的解析表达式，并用通过仿真验证了分析结果。

Keywords： 解码转发     能量收集     非线性     保密中断概率    

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Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output None

Shang LIU, Ishtiaq AHMAD, Ping ZHANG, Zhi ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 674-684 doi: 10.1631/FITEE.1700081

Abstract: This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio (CR) network that contains relay and massive multi-input multi-output (MIMO). The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the kth user’s asymptotic signal to interference and noise ratio (SINR) is independent of fast fading. When the ratio between the base station (BS) antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users (PUs) and secondary users (SUs) in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information (CSI), including the end-to-end SINR and achievable sum rate. When the numbers of primary base station (PBS) antennas, secondary base station (SBS) antennas, and relay antennas become infinite, the asymptotic SINR of the PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled asymptotically. Transmission performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K-user system without fast fading. Next, the PUs’ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna number, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underlay CR network. The primary and secondary networks can operate independently.

Keywords： Massive multi-input multi-output     Cognitive radio     Relay network     Transmission rate     Power analysis    

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Anovel resource optimization scheme for multi-cellOFDMArelay network Article

Ning DU,Fa-sheng LIU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 825-833 doi: 10.1631/FITEE.1500294

Abstract: In cellular networks, users communicate with each other through their respective base stations (BSs). Conventionally, users are assumed to be in different cells. BSs serve as decode-and-forward (DF) relay nodes to users. In addition to this type of conventional user, we recognize that there are scenarios users who want to communicate with each other are located in the same cell. This gives rise to the scenario of intra-cell communication. In this case, a BS can behave as a two-way relay to achieve information exchange instead of using conventional DF relay. We consider a multi-cell orthogonal frequency division multiple access (OFDMA) network that comprises these two types of users. We are interested in resource allocation between them. Specifically, we jointly optimize subcarrier assignment, subcarrier pairing, and power allocation to maximize the weighted sum rate. We consider the resource allocation problem at BSs when the end users’ power is fixed. We solve the problem approximately through Lagrange dual decomposition. Simulation results show that the proposed schemes outperform other existing schemes.

Keywords： Intra-cell communication     Two-way relay     Subcarrier assignment     Subcarrier pairing    

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5,   Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract:

Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss
the potential advantages of a "bidirectional" modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

Keywords： Brain encoding and decoding     Functional magnetic resonance imaging     Deep neural networks     Deep generative models     Dual learning    

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Cross-layer QoS routing algorithm of metric-based cooperative relay in WSNs

Xu Nan,Sun Yamin,Yu Jiming,Wang Hua

Strategic Study of CAE 2011, Volume 13, Issue 3,   Pages 45-49

Abstract:

We proposed a cross-layer QoS routing algorithm which is metric-based cooperative relay initiative forwarding(MCRICQR). Nodes form a metric according to energy, channel, congestion and distance to sink. The node with maximal metric forwards or relays or leapfrogs the data in term of it's own states. Simulation results show that MCRICQR can prolong the lifetime of network and guarantee the required QoS. It also can transmit data in time and load balance which improve the reliability and energy efficiency and throughput of WSN(wireless sensor network).

Keywords： quality of service     cross-layer design     cooperative relay     initiative forwarding     wireless sensor network    

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A new anti-jamming communication technical system：pre-encoded code hopping spread spectrum

Yao Fuqiang,Zhang Yi

Strategic Study of CAE 2011, Volume 13, Issue 10,   Pages 69-75

Abstract:

A kind of pre-encoded code hopping spread spectrum (PCHSS) communication technical system for anti-jamming communication is brought forward and researched after analyzing its necessity based on the deficiency of conventional direct-sequence spread spectrum(DSSS). The main discussions include the basic principle of PCHSS, the differences between PCHSS and self-coded spread spectrum(SCHSS), and some key techniques. The PCHSS basic performance is analyzed finally. The technical system and its basic performance have been proved in practice.

Keywords： anti-jamming communication     direct-sequence spread spectrum     pre-encoded code hopping spread spectrum     self-coded spread spectrum    

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United Algorithm for Dynamic Subcarrier, Bit and Power Allocation in OFDM System

Gao Huanqin,Feng Guangzeng,Zhuqi

Strategic Study of CAE 2006, Volume 8, Issue 3,   Pages 62-65

Abstract:

A realtime united algorithm for dynamic subbcarrier, bit and power allocation according to the change of channel (UA) is presented in this paper, which can be used into the down-link of multi-user orthogonal frequency division multiplexing (OFDM) system. With the algorithm the total transmission power is the minimum while the data rate of each user and the required BER performance can be achieved. Comparing to the subcarrier allocation algorithm (WSA) , the simulation results show that the algorithm presented in this paper has better performance while both have equal calculating complexity.

Keywords： OFDM     Wong's subcarrier allocation (WSA)     UA    

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Aprojected gradient based game theoretic approach for multi-user power control in cognitive radio network None

Yun-zheng TAO, Chun-yan WU, Yu-zhen HUANG, Ping ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 3,   Pages 367-378 doi: 10.1631/FITEE.1700067

Abstract: The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.

Keywords： Cognitive radio networks     Multi-user power control     Non-cooperative game     Nash equilibrium     Projected gradient    

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Suboptimal network coding subgraph algorithms for 5G minimum-cost multicast networks None

Feng WEI, Wei-xia ZOU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 5,   Pages 662-673 doi: 10.1631/FITEE.1700020

Abstract: To reduce the transmission cost in 5G multicast networks that have separate control and data planes, we focus on the minimum-power-cost network-coding subgraph problem for the coexistence of two multicasts in wireless networks. We propose two suboptimal algorithms as extensions of the Steiner tree multicast. The critical 1-cut path eliminating (C1CPE) algorithm attempts to find the minimum-cost solution for the coexistence of two multicast trees with the same throughput by reusing the links in the topology, and keeps the solution decodable by a coloring process. For the special case in which the two multicast trees share the same source and destinations, we propose the extended selective closest terminal first (E-SCTF) algorithm out of the C1CPE algorithm. Theoretically the complexity of the E-SCTF algorithm is lower than that of the C1CPE algorithm. Simulation results show that both algorithms have superior performance in terms of power cost and that the advantage is more evident in networks with ultra-densification.

Keywords： Network coding subgraph     Minimum power cost     5G     Separation architecture    

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Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network

Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu,lengcangche@bupt.edu.cn,renchao_xie@bupt.edu.cn,htao@bupt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1573-1590 doi: 10.1631/FITEE.1601585

Abstract: As a promising future network architecture, named data networking (NDN) has been widely considered as a very appropriate network protocol for the (MWN). In named-data MWNs, is a critical issue. Independent optimization for may cause severe performance degradation if it can not cooperate well with protocols in other layers. Cross-layer is a potential method to enhance performance. There have been many cross-layer mechanisms for MWN with Internet Protocol (IP). However, these cross-layer mechanisms for MWNs with IP are not applicable to named-data MWNs because the communication characteristics of NDN are different from those of IP. In this paper, we study the joint , forwarding strategy, and link scheduling problem for named-data MWNs. The problem is modeled as a network utility maximization (NUM) problem. Based on the approximate subgradient algorithm, we propose an algorithm called ‘jointly optimized , forwarding strategy, and link scheduling (JOCFS)’ to solve the NUM problem distributively and iteratively. To the best of our knowledge, our proposal is the first cross-layer mechanism for named-data MWNs. By comparison with the existing mechanism, JOCFS can achieve a better performance in terms of network throughput, fairness, and the pending interest table (PIT) size.

Keywords： Information-centric networking     Congestion control     Cross-layer design     Multihop wireless network    

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Deep 3D reconstruction: methods, data, and challenges Review Article

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5,   Pages 615-766 doi: 10.1631/FITEE.2000068

Abstract: Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states which can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types, , , , and based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.

Keywords： 深度学习模型；三维重建；循环神经网络；深度自编码器；生成对抗网络；卷积神经网络    

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Efficient decoding self-attention for end-to-end speech synthesis Research Article

Wei ZHAO, Li XU

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1127-1138 doi: 10.1631/FITEE.2100501

Abstract: has been innovatively applied to text-to-speech (TTS) because of its parallel structure and superior strength in modeling sequential data. However, when used in with an autoregressive decoding scheme, its inference speed becomes relatively low due to the quadratic complexity in sequence length. This problem becomes particularly severe on devices without graphics processing units (GPUs). To alleviate the dilemma, we propose an (EDSA) module as an alternative. Combined with a dynamic programming decoding procedure, TTS model inference can be effectively accelerated to have a linear computation complexity. We conduct studies on Mandarin and English datasets and find that our proposed model with EDSA can achieve 720% and 50% higher inference speed on the central processing unit (CPU) and GPU respectively, with almost the same performance. Thus, this method may make the deployment of such models easier when there are limited GPU resources. In addition, our model may perform better than the baseline Transformer TTS on out-of-domain utterances.

Keywords： Efficient decoding     End-to-end     Self-attention     Speech synthesis    

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A forwarding graph embedding algorithm exploiting regional topology information Article

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1854-1866 doi: 10.1631/FITEE.1601404

Abstract: Network function virtualization (NFV) is a newly proposed technique designed to construct and manage network functions dynamically and efficiently. Allocating physical resources to the virtual network function forwarding graph is a critical issue in NFV. We formulate the forwarding graph embedding (FGE) problem as a binary integer programming problem, which aims to increase the revenue and decrease the cost to a service provider (SP) while considering limited network resources and the requirements of virtual functions. We then design a novel regional resource clustering metric to quantify the embedding potential of each substrate node and propose a topology-aware FGE algorithm called ‘regional resource clustering FGE’ (RRC-FGE). After implementing our algorithms in C++, simulation results showed that the total revenue was increased by more than 50 units and the acceptance ratio by more than 15%, and the cost of the service provider was decreased by more than 60 units.

Keywords： Network function virtualization     Virtual network function     Forwarding graph embedding    

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Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-based efficient network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AE-GDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2×10 parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradethttps://github.com/robvincen/robot_gradet.

Keywords： Robot grasp detection     Attention mechanism     Encoder–     decoder     Neural network    

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Title Author Date Type Operation

认知中继三跳网络联合优化

澄 赵,万良 王,信威 姚,双华 杨

Journal Article

Secrecy outage performance for wireless-powered relaying systems with nonlinear energy harvesters

继亮 张,高峰 潘,宜原 解

Journal Article

Multi-user rate and power analysis in a cognitive radio network with massive multi-input multi-output

Shang LIU, Ishtiaq AHMAD, Ping ZHANG, Zhi ZHANG

Journal Article

Anovel resource optimization scheme for multi-cellOFDMArelay network

Ning DU,Fa-sheng LIU

Journal Article

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Journal Article

Cross-layer QoS routing algorithm of metric-based cooperative relay in WSNs

Xu Nan,Sun Yamin,Yu Jiming,Wang Hua

Journal Article

A new anti-jamming communication technical system：pre-encoded code hopping spread spectrum

Yao Fuqiang,Zhang Yi

Journal Article

United Algorithm for Dynamic Subcarrier, Bit and Power Allocation in OFDM System

Gao Huanqin,Feng Guangzeng,Zhuqi

Journal Article

Aprojected gradient based game theoretic approach for multi-user power control in cognitive radio network

Yun-zheng TAO, Chun-yan WU, Yu-zhen HUANG, Ping ZHANG

Journal Article

Suboptimal network coding subgraph algorithms for 5G minimum-cost multicast networks

Feng WEI, Wei-xia ZOU

Journal Article

Jointly optimized congestion control, forwarding strategy, and link scheduling in a named-data multihop wireless network

Cheng-cheng Li, Ren-chao Xie, Tao Huang, Yun-jie Liu,lengcangche@bupt.edu.cn,renchao_xie@bupt.edu.cn,htao@bupt.edu.cn

Journal Article

Deep 3D reconstruction: methods, data, and challenges

Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn

Journal Article

Efficient decoding self-attention for end-to-end speech synthesis

Wei ZHAO, Li XU

Journal Article

A forwarding graph embedding algorithm exploiting regional topology information

Hong-chao HU, Fan ZHANG, Yu-xing MAO, Zhen-peng WANG

Journal Article

Attention-based efficient robot grasp detection network

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Journal Article