MIRAI Architecture for Heterogeneous Network

One of the keywords that describe next-generation wireless communications is "seamless." As part of the e-Japan Plan promoted by the Japanese Government, the Multimedia Integrated Network by Radio Access Innovation project has as its goal the development of new technologies to enable seamless integration of various wireless access systems for practical use by 2005. This article describes a heterogeneous network architecture including a common tool, a common platform, and a common access. In particular, software-defined radio technologies are used to develop a multiservice user terminal to access different wireless networks. The common platform for various wireless networks is based on a wireless-supporting IPv6 network. A basic access network, separated from other wireless access networks, is used as a means for wireless system discovery, signaling, and paging. A proof-of-concept experimental demonstration system is available

Device-to-device communications in LTE-unlicensed heterogeneous Network

In this article, we look into how the LTE network can efficiently evolve to cater for new data services by utilizing direct communications between mobile devices and extending the direct transmissions to the unlicensed bands, that is, D2D communications in conjunction with LTE-Unlicensed. In doing so, it provides an opportunity to solve the main challenge of mutual interference between D2D and CC transmissions. In this context, we review three interconnected major technical areas of multihop D2D: transmission band selection, routing path selection, and resource management. Traditionally, D2D transmissions are limited to specific regions of a cell's coverage area in order to limit the interference to CC primary links. We show that by allowing D2D to operate in the unlicensed bands with protective fairness measures for WiFi transmissions, D2D is able to operate across the whole coverage area and, in doing so, efficiently scale the overall network capacity while minimizing cross-tier and cross-technology interference

Ambient networks: Bridging heterogeneous network domains

Providing end-to-end communication in heterogeneous internetworking environments is a challenge. Two fundamental problems are bridging between different internetworking technologies and hiding of network complexity and differences from both applications and application developers. This paper presents abstraction and naming mechanisms that address these challenges in the Ambient Networks project. Connectivity abstractions hide the differences of heterogeneous internetworking technologies and enable applications to operate across them. A common naming framework enables end-to-end communication across otherwise independent internetworks and supports advanced networking capabilities, such as indirection or delegation, through dynamic bindings between named entities

GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste

Multidimensional integration in a heterogeneous network environment

We consider several issues related to the multidimensional integration using a network of heterogeneous computers. Based on these considerations, we develop a new general purpose scheme which can significantly reduce the time needed for evaluation of integrals with CPU intensive integrands. This scheme is a parallel version of the well-known adaptive Monte Carlo method (the VEGAS algorithm), and is incorporated into a new integration package which uses the standard set of message-passing routines in the PVM software system.Comment: 19 pages, latex, 5 postscript figures include

Representation Learning for Attributed Multiplex Heterogeneous Network

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn