3D Cluster-Based Ray Tracing Technique for Massive MIMO Channel Modeling

In this paper, a novel 3 dimensional (3D) approach is proposed for precise modeling of massive multiple input multiple output (M-MIMO) channels in millimeter wave (mmW) frequencies. This model is based on both deterministic and statistic computations to extract characteristics of the propagation channel. In order to increase algorithm execution speed, the physical channel is divided into two regions. The first region refers to those parts of the channel which can be mapped with simple planes such as walls, ramps and etc. The second region is usually complex which is defined by considering the channel with physical clusters. These physical clusters yield multipath components (MPCs) with similar angles of arrival (AoA) and time delay. The ray-tracing algorithm is utilized to find ray paths from transmitter (Tx) to receiver (Rx). Some characteristics of MPCs in each cluster are defined according to some appropriate statistical distribution. The non-stationary property of M-MIMO along the antenna array axis is considered in the algorithm. Due to the correspondence between the propagation environment and scatters, the accuracy of the model is highly increased. To evaluate the proposed channel model, simulation results are compared with some measurements reported in the literature.

Hunting for open clusters in Gaia DR2: the Galactic anticentre

Context. The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the search for unknown stellar structures. Particularly, supervised and unsupervised learning methods combined together significantly improves the detection rate of open clusters. Aims. We systematically scan Gaia DR2 in a region covering the Galactic anticentre and the Perseus arm (120° ≤ l ≤ 205° and −10° ≤ b ≤ 10°), with the goal of finding any open clusters that may exist in this region, and fine tuning a previously proposed methodology and successfully applied to TGAS data, adapting it to different density regions. Methods. Our methodology uses an unsupervised, density-based, clustering algorithm, DBSCAN, that identifies overdensities in the five-dimensional astrometric parameter space (l, b, ϖ, μα*, μδ) that may correspond to physical clusters. The overdensities are separated into physical clusters (open clusters) or random statistical clusters using an artificial neural network to recognise the isochrone pattern that open clusters show in a colour magnitude diagram. Results. The method is able to recover more than 75% of the open clusters confirmed in the search area. Moreover, we detected 53 open clusters unknown previous to Gaia DR2, which represents an increase of more than 22% with respect to the already catalogued clusters in this region. Conclusions. We find that the census of nearby open clusters is not complete. Different machine learning methodologies for a blind search of open clusters are complementary to each other; no single method is able to detect 100% of the existing groups. Our methodology has shown to be a reliable tool for the automatic detection of open clusters, designed to be applied to the full Gaia DR2 catalogue.

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.