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codeword selection for Concurrent Transmissions in UAV Networks: A Machine Learning Approach

IEEE ACCESS 2020年 8卷 26583-26590页

作者： Yang, Yang Gao, Zhen Zhang, Yue Yan, Qing He, Dazhong Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Ctr Data Sci Beijing 100876 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China HAOHAN Data Technol Co Ltd Beijing 100142 Peoples R China

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

关键词： Machine learning UAV networks concurrent transmissions codeword selection support vector machine

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A statistical design approach to unsupervised codeword selection in image retrieval

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NEUROCOMPUTING 2015年 157卷 323-334页

作者： Ji, Ming Zhao, Wei Liu, Zheng Univ Illinois Urbana IL USA Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China

Recently, the bag-of-features (BoF) representation has attracted substantial interest in large scale image retrieval. In BoF representation, images are treated as collections of local invariant descriptors. A visual codebook is constructed by quantizing the local descriptors into clusters, whose centers are called codewords. Then each image could be represented by the frequency histogram over the codebook, and existing text retrieval techniques such as inverted indexing could be applied to image retrieval. It has been recognized that the quality of the codebook is critical to the performance of BoF-based image retrieval systems, and codeword selection is therefore a fundamental problem. Many of the existing approaches for codeword selection make use of the label information of the images. However, collecting the labels of a large amount of images is very expensive. In this paper, we investigate the problem of codeword selection in the absence of labels. Inspired from the techniques of statistical design, we propose two novel unsupervised learning algorithms to select the codewords for an image retrieval system. Specifically, we assume that the relationship between the relevance score and the BoF representation of an image could be expressed by a linear regression model, and select the codewords which can improve the regression model the most. In other words, if the selected codewords are used to train the regression model, the expected prediction error can be minimized. Extensive experimental results have demonstrated the effectiveness of our proposed methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Statistical design codeword selection Image retrieval

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Machine Learning Inspired codeword selection For Dual Connectivity in 5G User-Centric Ultra-Dense Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第8期68卷 8284-8288页

作者： Yang, Yang Deng, Xinyi He, Dazhong You, Yanan Song, Ruoning Beijing Univ Posts & Telecommun Ctr Data Sci Beijing 100876 Peoples R China

In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS serves the MU are further derived to get the average sum rate (ASR) in UUDN. Third, inspired by ML, we utilize an iterative support vector machine (SVM) classifier to select the codewords of BSs, with sequential minimal optimization (SMO) algorithm used for training all link samples in UUDN. Then, an iterative SVM-SMO classification algorithm is proposed to achieve a highly efficient performance of DC, where the convergence and complexity are also discussed. The sample training and simulation at last are evaluated by Google Tensor Flow. The simulation verified that our proposed algorithm gets a higher ASR than the traditional channel estimation based algorithm. In addition, the results also show a lower computational complexity can be achieved by the proposed algorithm as well.

关键词： User-centric ultra-dense networks dual connectivity codeword selection machine learning support vector machine

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Location and Attitude Information Aided codeword selection in Millimeter Wave MIMO System 2

Location and Attitude Information Aided Codeword Selection i...

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2nd IEEE International Symposium on Joint Communications and Sensing (JC and S)

作者： Yang, Zhibo Chen, Li Wang, Weidong Univ Sci & Technol China CAS Key Lab Wireless Opt Commun Hefei Peoples R China

ISBN: (纸本)9781665405799

Recently, the location and attitude information (LAI) from sensors have been utilized to assist beamforming in millimeter wave (mmWave) system for the potential of reducing training overhead. In this paper, we propose a machine learning based LAI assisted codeword selection algorithm. Specifically, based on the spatial consistency of mmWave channel, we transform the codeword selection problem into a nonlinear classification problem with the LAI of the user equipment (UE). Furthermore, we derive the relationship between the LAI of UE and the arrival angles of the line-of-sight (LOS) path. To solve this nonlinear classification problem, a custom kernel function based on the definition of linearly separable sample space is proposed for the support vectors machine (SVM) method. Finally, simulation results are presented to show the effectiveness of the proposed algorithms.

关键词： Location and attitude information codeword selection spatial consistency kernel function

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One-pass Keypoint selection to Construct Codebook for Patch-based Object Classification 9

One-pass Keypoint Selection to Construct Codebook for Patch-...

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9th IEEE International Conference on Information and Automation for Sustainability (ICIAfS)

作者： Vinoharan, Veerapathirapillai Ramanan, Amirthalingam Univ Jaffna Comp Unit Jaffna Sri Lanka Univ Jaffna Dept Comp Sci Jaffna Sri Lanka

ISBN: (纸本)9781538694183

In a patch-based object classification system, one of the most popular image representation approach is the bag-of-features (BoF) representation. However, the number of patchbased features extracted from images to create the BoF vector is usually very large, which causes two problems: (i) Increased amount of computational needs during the vector quantisation step, and (ii) Ambiguous descriptors from training images increase false positive rate in the classification. To overcome these issues we introduce a one-pass feature selection approach followed by an entropy-based filtering technique to eliminate the ambiguous features from initial large feature set. In this work, a discriminative BoF representation for object recognition is constructed using patch-based descriptors that are informative in distinguishing object categories. Following the construction of a codebook a subset of codewords which is not activated enough in images is eliminated based on statistical measures and visual-bit representation of codewords. The proposed technique is evaluated on (i) Xerox7, (ii) UIUCTex, (iii) PASCAL VOC 2007, and (iv) Caltech101 image datasets. The proposed feature selection step increases the discriminant power of a codebook, while the codeword selection method maintains the codebook to be more compact. The proposed framework would help to optimise the BoF representation to be effective with stable performance.

关键词： Bag-of-features Keypoint selection codeword selection Image representation

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Selected Crosstalk Avoidance Code for Reliable Network-on-Chip

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Journal of Computer Science & Technology 2009年第6期24卷 1074-1085页

作者：张颖李华伟李晓维 Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Graduate School of the Chinese Academy of Sciences

With the shrink of the technology into nanometer scale, network-on-chip （NOC） has become a reasonable solution for connecting plenty of IP blocks on a single chip. But it suffers from both crosstalk effects and single event upset （SEU）, especially crosstalk-induced delay, which may constrain the overall performance of NOC. In this paper, we introduce a reliable NOC design using a code with the capability of both crosstalk avoidance and single error correction. Such a code, named selected crosstalk avoidance code （SCAC） in our previous work, joins crosstalk avoidance code （CAC） and error correction code （ECC） together through codeword selection from an original CAC codeword set. It can handle possible error caused by either crosstalk effects or SEU. When designing a reliable NOC, data are encoded to SCAC codewords and can be transmitted rapidly and reliably across NOC. Experimental results show that the NOC design with SCAC achieves higher performance and is reliable to tolerate single errors. Compared with previous crosstalk avoidance methods, SCAC reduces wire overhead, power dissipation and the total delay. When SCAC is used in NOC, it can save 20% area overhead and reduce 49% power dissipation.

关键词： crosstalk avoidance codeword selection reliable network-on-chip single event upset

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On channel quantization for multi-cell cooperative systems with limited feedback

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Science China(Information Sciences) 2013年第2期56卷 74-89页

作者： HOU XueYing YANG ChenYang LAU Buon Kiong School of Electronics and Information Engineering Beihang University Department of Electrical and Information Technology Lund University

Coherent multi-cell cooperative transmission, also referred to as coordinated multi-point transmission (CoMP), is a promising strategy to provide high spectral efficiency for universal frequency reuse cellular systems. To report the required channel information to the transmitter in frequency division duplexing systems, limited feedback techniques are often applied. Considering that the average channel gains from multiple base stations (BSs) to one mobile station are different and the number of cooperative BSs may be dynamic, it is neither flexible nor compatible to employ a large codebook to directly quantize the CoMP channel. In this paper, we employ per-cell codebooks for quantizing local and cross channels. We first propose a codeword selection criterion, aiming at maximizing an estimated data rate for each user. The proposed criterion can be applied for an arbitrary number of receive antennas at each user and also for an arbitrary number of data streams transmitted to each user. Considering that the resulting optimal per-cell codeword selection for CoMP channel is of high complexity, we propose a serial codeword selection method that has low complexity but yields comparable performance to that of the optimal codeword selection. We evaluate the proposed codeword selection criterion and method using measured CoMP channels from an urban environment as well as simulations. The results demonstrate significant performance gain as compared to an existing low-complexity method.

关键词： base station cooperative transmission channel quantization limited feedback codeword selection

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Simple evaluation of squared weight

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JOURNAL OF ENGINEERING-JOE 2015年第7期2015卷 226-228页

作者： Fair, I. J. Carle, E. R. Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Squared weight is an excellent metric on which to base codeword selection in DC-free multimode coding. It has, however, been considered too complex to implement in practice. In this Letter, the authors present a simpl... 详细信息

关键词： encoding squared weight simple evaluation codeword selection DC-free multimode coding digital logic

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Research on Per-cell Codebook based Channel Quantization for CoMP Transmission

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2014年第6期8卷 1828-1847页

作者： Hu, Zhirui Feng, Chunyan Zhang, Tiankui Gao, Qiubin Sun, Shaohui Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing Peoples R China State Key Lab Wireless Mobile Commun CATT Beijing Peoples R China

Coordinated multi-point (CoMP) transmission has been regarded as a potential technology for LTE-Advanced. In frequency division duplexing systems, channel quantization is applied for reporting channel state information (CSI). Considering the dynamic number of cooperation base stations (BSs), asymmetry feature of CoMP channels and high searching complexity, simply increasing the size of the codebook used in traditional multiple antenna systems to quantize the global CSI of CoMP systems directly is infeasible. Per-cell codebook based channel quantization to quantize local CSI for each BS separately is an effective method. In this paper, the theoretical upper bounds of system throughput are derived for two codeword selection schemes, independent codeword selection (ICS) and joint codeword selection (JCS), respectively. The feedback overhead and selection complexity of these two schemes are analyzed. In the simulation, the system throughput of ICS and JCS is compared. Both analysis and simulation results show that JCS has a better tradeoff between system throughput and feedback overhead. The ICS has obvious advantage in complexity, but it needs additional phase information (PI) feedback for obtaining the approximate system throughput with JCS. Under the same number of feedback bits constraint, allocating the number of bits for channel direction information (CDI) and PI quantization can increase the system throughput, but ICS is still inferior to JCS. Based on theoretical analysis and simulation results, some recommendations are given with regard to the application of each scheme respectively.

关键词： CoMP system channel quantization limited feedback per-cell codebook codeword selection bits allocation

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