Common Codebook Millimeter Wave Beam Design: Designing Beams for Both Sounding and Communication with Uniform Planar Arrays

Fifth generation (5G) wireless networks are expected to utilize wide bandwidths available at millimeter wave (mmWave) frequencies for enhancing system throughput. However, the unfavorable channel conditions of mmWave links, e.g., higher path loss and attenuation due to atmospheric gases or water vapor, hinder reliable communications. To compensate for these severe losses, it is essential to have a multitude of antennas to generate sharp and strong beams for directional transmission. In this paper, we consider mmWave systems using uniform planar array (UPA) antennas, which effectively place more antennas on a two-dimensional grid. A hybrid beamforming setup is also considered to generate beams by combining a multitude of antennas using only a few radio frequency chains. We focus on designing a set of transmit beamformers generating beams adapted to the directional characteristics of mmWave links assuming a UPA and hybrid beamforming. We first define ideal beam patterns for UPA structures. Each beamformer is constructed to minimize the mean squared error from the corresponding ideal beam pattern. Simulation results verify that the proposed codebooks enhance beamforming reliability and data rate in mmWave systems.Comment: 14 pages, 10 figure

Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays

Massive multiple-input multiple-output (MIMO) systems, which utilize a large number of antennas at the base station, are expected to enhance network throughput by enabling improved multiuser MIMO techniques. To deploy many antennas in reasonable form factors, base stations are expected to employ antenna arrays in both horizontal and vertical dimensions, which is known as full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform planar array (UPA), where antennas are placed in a grid pattern. To exploit the full benefit of massive MIMO in frequency division duplexing (FDD), the downlink channel state information (CSI) should be estimated, quantized, and fed back from the receiver to the transmitter. However, it is difficult to accurately quantize the channel in a computationally efficient manner due to the high dimensionality of the massive MIMO channel. In this paper, we develop both narrowband and wideband CSI quantizers for FD-MIMO taking the properties of realistic channels and the UPA into consideration. To improve quantization quality, we focus on not only quantizing dominant radio paths in the channel, but also combining the quantized beams. We also develop a hierarchical beam search approach, which scans both vertical and horizontal domains jointly with moderate computational complexity. Numerical simulations verify that the performance of the proposed quantizers is better than that of previous CSI quantization techniques.Comment: 15 pages, 6 figure

Beam Design for Millimeter-Wave Backhaul with Dual-Polarized Uniform Planar Arrays

This paper proposes a beamforming design for millimeter-wave (mmWave) backhaul systems with dual-polarization antennas in uniform planar arrays (UPAs). The proposed design method optimizes a beamformer to mimic an ideal beam pattern, which has flat gain across its coverage, under the dominance of the line-of-sight (LOS) component in mmWave systems. The dual-polarization antenna structure is considered as constraints of the optimization. Simulation results verify that the resulting beamformer has uniform beam pattern and high minimum gain in the covering region.Comment: To appear in IEEE ICC 202

HW-SW co-design techniques for modern programming languages

Modern programming languages raise the level of abstraction, hide the details of computer systems from programmers, and provide many convenient features. Such strong abstraction from the details of computer systems with runtime support of many convenient features increases the productivity of programmers. Such benefits, however, come with performance overheads. First, many of modern programming languages use a dynamic type system which incurs overheads of profiling program execution and generating specialized codes in the middle of execution. Second, such specialized codes constantly add overheads of dynamic type checks. Third, most of modern programming languages use automatic memory management which incurs memory overheads due to metadata and delayed reclamation as well as execution time overheads due to garbage collection operations. This thesis makes three contributions to address the overheads of modern programming languages. First, it describes the enhancements to the compiler of dynamic scripting languages necessary to enable sharing of compilation results across executions. These compilers have been developed with little consideration for reusing optimization efforts across executions since it is considered difficult due to dynamic nature of the languages. As a first step toward enabling the reuse of compilation results of dynamic scripting languages, it focuses on inline caching (IC) which is one of the fundamental optimization techniques for dynamic type systems. Second, it describes a HW-SW co-design technique to further improve IC operations. While the first proposal focuses on expensive IC miss handling during JavaScript initialization, the second proposal accelerates IC hit operations to improve the overall performance. Lastly, it describes how to exploit common sharing patterns of programs to reduce overheads of reference counting for garbage collection. It minimizes atomic operations in reference counting by biasing each object to a specific thread

Is Signed Message Essential for Graph Neural Networks?

Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes, achieve satisfying results on homophilic graphs. However, their performances are dismal in heterophilous graphs, and many researchers have proposed a plethora of schemes to solve this problem. Especially, flipping the sign of edges is rooted in a strong theoretical foundation, and attains significant performance enhancements. Nonetheless, previous analyses assume a binary class scenario and they may suffer from confined applicability. This paper extends the prior understandings to multi-class scenarios and points out two drawbacks: (1) the sign of multi-hop neighbors depends on the message propagation paths and may incur inconsistency, (2) it also increases the prediction uncertainty (e.g., conflict evidence) which can impede the stability of the algorithm. Based on the theoretical understanding, we introduce a novel strategy that is applicable to multi-class graphs. The proposed scheme combines confidence calibration to secure robustness while reducing uncertainty. We show the efficacy of our theorem through extensive experiments on six benchmark graph datasets

Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overfitting in certain dimensions in the first projection matrix, as training samples may not cover the entire range of graph filters (hyperplanes). To address this, we propose a novel data augmentation strategy. Specifically, by flipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the first attempt to mitigate the overfitting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classification accuracy by up to 46.5% relatively

Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods

The Impact of CRM on Firm- andRelationship-Level Performance in Distributed Networks

This paper develops and empirically tests a model to evaluate a manufacturer\u27s strategy which provides customer relationship management (CRM) technology to its exclusive retailers. The impact of the strategy on manufacturer-retailer relationship quality is also examined. The research objectives are (1) to identify and test factors that promote active implementation of CRM technology among small retail organizations; (2) to determine whether our expanded concept of CRM implementation that integrates customer information management activities and relationship marketing activities explains CRM performance better; and (3) to investigate whether a manufacturer\u27s support contributes to manufacturer-retailer relationship quality. Statistical analysis shows that the model provides an adequate fit to the data. The retailer\u27s perception of the importance of customer information, manufacturer support, and trade area competitiveness significantly impacts the intensity of CRM implementation by small retailers. CRM implementation intensity positively influences the performance outcomes of CRM, which in turn greatly improves the quality of the manufacturer-retailer relationship. Different from our expectation, supporting retailers with CRM technology did not directly impact the manufacturer-retailer relationship quality. The ease of use of the CRM system also did not influence CRM implementation intensity significantly. The implications of these results and their importance for successful CRM implementation are discussed