scholarly journals Broad Coverage Precoding for 3D Massive MIMO with Huge Uniform Planar Arrays

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 887
Author(s):  
An-An Lu ◽  
Yan Chen ◽  
Xiqi Gao
Keyword(s):  
Massive Mimo ◽  
Three Dimensional ◽  
Optimization Methods ◽  
Base Station ◽  
High Dimensional ◽  
Power Constraint ◽  
Planar Arrays ◽  
Precoder Design ◽  
Simulation Results ◽  
Low Dimensional

In this paper, we propose a novel broad coverage precoder design for three-dimensional (3D) massive multi-input multi-output (MIMO) equipped with huge uniform planar arrays (UPAs). The desired two-dimensional (2D) angle power spectrum is assumed to be separable. We use the per-antenna constant power constraint and the semi-unitary constraint which are widely used in the literature. For normal broad coverage precoder design, the dimension of the optimization space is the product of the number of antennas at the base station (BS) and the number of transmit streams. With the proposed method, the design of the high-dimensional precoding matrices is reduced to that of a set of low-dimensional orthonormal vectors, and of a pair of low-dimensional vectors. The dimensions of the vectors in the set and the pair are the number of antennas per column and per row of the UPA, respectively. We then use optimization methods to generate the set of orthonormal vectors and the pair of vectors, respectively. Finally, simulation results show that the proposed broad coverage precoding matrices achieve nearly the same performance as the normal broad coverage precoder with much lower computational complexity.

scholarly journals An Order-Independent Algorithm for Learning Chain Graphs

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Mohammad Ali Javidian ◽  
Marco Valtorta ◽  
Pooyan Jamshidi
Keyword(s):  
Directed Acyclic Graphs ◽  
High Dimensional ◽  
P Values ◽  
Order Dependence ◽  
Error Measures ◽  
Acyclic Graphs ◽  
Simulation Results ◽  
Improved Performance ◽  
Low Dimensional ◽  
Chain Graphs

LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that PC4LWF is order dependent, in the sense that the output can depend on the order in which the variables are given. This order dependence can be very pronounced in high-dimensional settings. We propose two modifications of the PC4LWF algorithm that remove part or all of this order dependence. Simulation results with different sample sizes, network sizes, and p-values demonstrate the competitive performance of the PC4LWF algorithms in comparison with the LCD algorithm proposed by Ma et al. (2008) in low-dimensional settings and improved performance (with regard to error measures) in high-dimensional settings.

Study on dual peg-in-hole insertion using of constraints formed in the environment

2017 ◽  
Vol 44 (6) ◽  
pp. 730-740 ◽  
Author(s):  
Jianhua Su ◽  
Rui Li ◽  
Hong Qiao ◽  
Jing Xu ◽  
Qinglin Ai ◽  
...  
Keyword(s):  
Configuration Space ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Force Sensor ◽  
High Dimensional ◽  
Content Type ◽  
Attractive Region ◽  
Low Dimensional ◽  
The Cost ◽  
Yaw Angle

Purpose The purpose of this paper is to develop a dual peg-in-hole insertion strategy. Dual peg-in-hole insertion is the most common task in manufacturing. Most of the previous work develop the insertion strategy in a two- or three-dimensional space, in which they suppose the initial yaw angle is zero and only concern the roll and pitch angles. However, in some case, the yaw angle could not be ignored due to the pose uncertainty of the peg on the gripper. Therefore, there is a need to design the insertion strategy in a higher-dimensional configuration space. Design/methodology/approach In this paper, the authors handle the insertion problem by converting it into several sub-problems based on the attractive region formed by the constraints. The existence of the attractive region in the high-dimensional configuration space is first discussed. Then, the construction of the high-dimensional attractive region with its sub-attractive region in the low-dimensional space is proposed. Therefore, the robotic insertion strategy can be designed in the subspace to eliminate some uncertainties between the dual pegs and dual holes. Findings Dual peg-in-hole insertion is realized without using of force sensors. The proposed strategy is also used to demonstrate the precision dual peg-in-hole insertion, where the clearance between the dual-peg and dual-hole is about 0.02 mm. Practical implications The sensor-less insertion strategy will not increase the cost of the assembly system and also can be used in the dual peg-in-hole insertion. Originality/value The theoretical and experimental analyses for dual peg-in-hole insertion are proposed without using of force sensor.

A New SLNR-Based Precoding for Multiuser MIMO Downlinks

2014 ◽  
Vol 644-650 ◽  
pp. 4066-4071
Author(s):  
Xin Min Li
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Base Station ◽  
Semidefinite Relaxation ◽  
Constraint Condition ◽  
Multiuser Mimo ◽  
Power Constraint ◽  
Transmit Power ◽  
Simulation Results

A new SLNR-based precoding is proposed for multiuser MIMO downlinks, which pursues the goal that minimizes total transmit power under each user’s SLNR constraint. The goal problems can be successfully solved by using semidefinite relaxation (SDR) techniques, and power constraint condition added in goal problems can efficiently reduce total transmit power of the base station. Simulation results show that our proposed scheme is almost feasible for users with one antenna, and it has better bit error rate (BER) and lower total transmit power than the maximal-SLNR based precoding method, when it satisfies large SLNR thretholds.

Finding the Macroworld

2021 ◽  
pp. 225-250
Author(s):  
Alyssa Ney
Keyword(s):  
Wave Function ◽  
Dimensional Space ◽  
Three Dimensional ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Macroscopic Objects ◽  
Low Dimensional ◽  
Object Problem ◽  
Wave Function Realism

This chapter proposes a solution to the macro-object problem for wave function realism. This is the problem of how a wave function in a high-dimensional space may come to constitute the low-dimensional, macroscopic objects of our experience. The solution takes place in several stages. First, it is argued that how the wave function’s being invariant under certain transformations may give us reason to regard three-dimensional configurations corresponding symmetries with ontological seriousness. Second it is shown how the wave function may decompose into low-dimensional microscopic parts. Interestingly, this reveals mereological relationships in which parts and wholes inhabit distinct spatial frameworks. Third, it is shown how these parts may come to compose macroscopic objects.

scholarly journals Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 107 ◽  
Author(s):  
Mujtaba Husnain ◽  
Malik Missen ◽  
Shahzad Mumtaz ◽  
Muhammad Luqman ◽  
Mickaël Coustaty ◽  
...  
Keyword(s):  
Local Structure ◽  
High Dimensional Data ◽  
Three Dimensional ◽  
Principal Component ◽  
Large Data ◽  
High Dimensional ◽  
Data Set ◽  
Novel Approach ◽  
Critical Issues ◽  
Low Dimensional

We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.

scholarly journals Human Motion Capture Based on Incremental Dimension Reduction and Projection Position Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wanyi Li ◽  
Yuqi Zeng ◽  
Qian Zhang ◽  
Yilin Wu ◽  
Guoming Chen
Keyword(s):  
Dimension Reduction ◽  
Motion Capture ◽  
Three Dimensional ◽  
Human Motion ◽  
The Other ◽  
High Dimensional ◽  
Key Technology ◽  
Human Motion Capture ◽  
Low Dimensional ◽  
Multimedia Product

Three-dimensional (3D) human motion capture is a hot researching topic at present. The network becomes advanced nowadays, the appearance of 3D human motion is indispensable in the multimedia works, such as image, video, and game. 3D human motion plays an important role in the publication and expression of all kinds of medium. How to capture the 3D human motion is the key technology of multimedia product. Therefore, a new algorithm called incremental dimension reduction and projection position optimization (IDRPPO) is proposed in this paper. This algorithm can help to learn sparse 3D human motion samples and generate the new ones. Thus, it can provide the technique for making 3D character animation. By taking advantage of the Gaussian incremental dimension reduction model (GIDRM) and projection position optimization, the proposed algorithm can learn the existing samples and establish the relevant mapping between the low dimensional (LD) data and the high dimensional (HD) data. Finally, the missing frames of input 3D human motion and the other type of 3D human motion can be generated by the IDRPPO.

scholarly journals MMSE based beamformer in massive MIMO-IDMA downlink systems

2020 ◽  
Vol 71 (1) ◽  
pp. 65-68
Author(s):  
Aasheesh Shukla ◽  
Vishal Goyal ◽  
Manish Kumar ◽  
Munesh Chandra Trivedi ◽  
Vinay Kumar Deolia
Keyword(s):  
Massive Mimo ◽  
Multiple Access ◽  
Minimum Mean Square Error ◽  
Multiple Access Interference ◽  
Base Station ◽  
Propagation Loss ◽  
Rate Performance ◽  
Multiple Input ◽  
Code Domain ◽  
Simulation Results

AbstractNow-a-days Massive MIMO (mMIMO) become an attractive technology due to its spectral and energy efficiency by the means of simple signal processing. However, in overloaded scenario, wherein the number of users equipments (UEs) are larger than number of antennas, the spectral effciency (SE) suffers and hence error rate performance, it has been shown recently that use of code domain NOMA in mMIMO can improve the SE performance. Further, interleave division multiple access (IDMA) has been drawn much attention as a suitable code domain non-orthogonal multiple access (NOMA) for future communication standards. This paper proposes the work in two folds, first a massive multiple input and multiple output (MIMO) and IDMA communication system is jointly proposed in which antennas on the base station serves users simultaneously in the same frequency band. Both and are large in numbers. Secondly, the minimum mean square error (MMSE) based beamformer is suggested to combat the propagation loss and effect of multiple access interference (MAI), for massive MIMO-IDMA system under downlink communication constraints. With the help of simulation results, the performance of the proposed system with MMSE beamforming have been investigated in terms of BER vs SNR curve plot.

scholarly journals Manifold Discriminative Learning Inspired Hybrid Beamforming for Millimeter-Wave Massive MIMO Systems

2021 ◽  
Vol 09 (11) ◽  
Author(s):  
Xiaoping Zhou ◽  
Keyword(s):  
Spatial Correlation ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
High Dimensional ◽  
Discriminative Learning ◽  
Hybrid Precoding ◽  
User Clustering ◽  
Sum Rate ◽  
Low Dimensional

Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propose user clustering hybrid precoding to enable efficient and low-complexity operation in high-dimensional mmWave massive MIMO, where a large number of antennas are used in low-dimensional manifolds. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. The manifold discriminative learning seek to learn the embedding low-dimensional manifolds, where manifolds with different user cluster labels are better separated, and the local spatial correlation of the high-dimensional channels within each manifold is enhanced. Most of the high-dimensional channels are embedded in the low-dimensional manifolds by manifold discriminative learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into global and local nonlinearity to achieve dimensionality reduction. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions

scholarly journals Projections as visual aids for classification system design

2017 ◽  
Vol 17 (4) ◽  
pp. 282-305 ◽  
Author(s):  
Paulo E Rauber ◽  
Alexandre X Falcão ◽  
Alexandru C Telea
Keyword(s):  
Dimensionality Reduction ◽  
Supervised Learning ◽  
Visual Analytics ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Classification Systems ◽  
High Dimensional ◽  
Visual Aids ◽  
Low Dimensional ◽  
Effective Development

Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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