A Practical Tessellation-Based Approach for Optimizing Cell-Specific Bias Values in LTE-A Heterogeneous Cellular Networks

In order to implement an optimized solution for cell range expansion (CRE) and enhanced intercell interference coordination (eICIC) schemes in long-term evolution-advanced (LTE-A) heterogeneous cellular networks (HCNs) and to realize good load-balancing performance in existing LTE-A systems, a practical tessellation-based algorithm is proposed. In this algorithm, a globalized cell-specific bias optimization and a localized almost blank subframe (ABS) ratio update are proposed. The proposed scheme does not require major changes to existing protocols. Thus, it can be implemented in existing LTE-A systems with any legacy user equipment (UE) with only a partial update to the BSs and core networks. From simulation results, it is shown that the tessellation formed by the proposed approach is quite consistent with the optimal one for various realistic scenarios. Thus, the proposed scheme can provide a much better load-balancing capability compared with the conventional common bias scheme. Owing to the improved load-balancing capability, the user rate distribution of the proposed scheme is much better than that obtained from the conventional scheme and is even indistinguishable from that of the ideal joint user association scheme

Hot Spot Analysis over Big Trajectory Data

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years. Document type: Conference objec

Hot Spot Analysis over Big Trajectory Data

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years. Document type: Conference objec