User Assignment with Distributed Large Intelligent Surface (LIS) Systems

In this paper, we consider a wireless communication system where a large intelligent surface (LIS) is deployed comprising a number of small and distributed LIS-Units. Each LIS-Unit has a separate signal process unit (SPU) and is connected to a central process unit (CPU) that coordinates the behaviors of all the LIS-Units. With such a LIS system, we consider the user assignments both for sum-rate and minimal user-rate maximizations. That is, assuming $M$ LIS-Units deployed in the LIS system, the objective is to select $K$ ($K\!\leq\!M$) best LIS-Units to serve $K$ autonomous users simultaneously. Based on the nice property of effective inter-user interference suppression of the LIS-Units, the optimal user assignments can be effectively found through classical linear assignment problems (LAPs) defined on a bipartite graph. To be specific, the optimal user assignment for sum-rate and user-rate maximizations can be solved by linear sum assignment problem (LSAP) and linear bottleneck assignment problem (LBAP), respectively. The elements of the cost matrix are constructed based on the received signal strength (RSS) measured at each of the $M$ LIS-Units for all the $K$ users. Numerical results show that, the proposed user assignments are close to optimal user assignments both under line-of-sight (LoS) and scattering environments.Comment: submitted to IEEE conference; 6 pages;10 figure

Performance of Sensor Fusion for Vehicular Applications

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The perfor-mance of the sensor fusion depends on many factors such as the sensors used, the kinematicmodel used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type ofroad, the density of vehicles, and the gating methods. The interactions between parametersand the extent to which individual parameters contribute to the overall accuracy of a sensorfusion system can be difficult to assess.In this study, a full-factorial experimental evaluation of a sensor fusion system basedon a real vehicle was performed. The experimental results for different driving scenariosand parameters are discussed and the factors that make the most impact are identified.The performance of sensor fusion depends on many factors such as the sensors used, thekinematic model used in the Extended Kalman Filter (EKF) motion of the vehicles, type ofroad, density of vehicles, and gating methods.This study identified that the distance between the vehicles has the largest impact on theestimation error because the vision sensor performs poorly with increased distance. In addi-tion, it was identified that the kinematic models had no significant impact on the estimation.Last but not least, the ellipsoid gates performed better than rectangular gates.In addition, we propose a new gating algorithm called an angular gate. This algorithmis based on the observation that the data for each target lies in the direction of that target.Therefore, the angle and the range can be used for setting up a two-level gating approachthat is both more intuitive and computationally faster than ellipsoid gates. The angulargates can achieve a speedup factor of up to 2.27 compared to ellipsoid gates.Furthermore, we provide time complexity analysis of angular gates, ellipsoid gates, andrectangular gates demonstrating the theoretical reasons why angular gates perform better.Last, we evaluated the performance of the Munkres algorithm using a full factorial designand identified that narrower gates can speedup the running time of the Munkres algorithmand, surprisingly, even improve the RMSE in some cases.The low target maneuvering index of vehicular systems was identified as the reason whythe kinematic models do not have an impact on the estimation. This finding supports the useof simpler and computationally inexpensive filters instead of complex Interacting MultipleModel filters. The angular gates also improve the computational efficiency of the overallsensor fusion system making them suitable for vehicular application as well as for embeddedsystems and robotics

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework implements a coarse-grained parallelism by computing persistence diagrams and finding optimal matchings in parallel for every couple of consecutive timesteps. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is an order of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our method provides competitive runtimes while yielding exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various simulated time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated

A Formal Definition for Configuration

There exists a wide set of techniques to perform keyword-based search over relational databases but all of them match the keywords in the users' queries to elements of the databases to be queried as first step. The matching process is a time-consuming and complex task. So, improving the performance of this task is a key issue to improve the keyword based search on relational data sources.In this work, we show how to model the matching process on keyword-based search on relational databases by means of the symmetric group. Besides, how this approach reduces the search space is explained in detail

Statistical CSIT Aided User Scheduling for Broadcast MU-MISO System

Abstract: Recent studies show that the statistical channel state information (SCSI) helps to largely increase the capacity of communication systems when the instantaneous perfect CSI (IPCSI) is unavailable. In this paper, we consider multi-user multipleinput- single-output (MU-MISO) broadcast channels where the transmitter has the knowledge of SCSI. The major issue concerned in our work is to improve the average group-rate of the whole system by scheduling users over different time slots. With SCSI at the transmitter side, we are able to precode signals and hence compute the theoretical achievable group-rate of arbitrary user groups. Based on the group-rates, we propose tier-2 Munkres user scheduling algorithm (T2-MUSA) which leads to higher average group-rate than existing algorithms with generally better fairness. The optimality of the proposed algorithm in energy-fair user scheduling space is proved and we derive a lower bound of a special case to verify the validity of our simulations. In addition, many conventional user scheduling algorithms maintain queue stability by solving a weighted sum-rate (WSR) problem, using queue lengths to represent weight coefficients. Inspired by T2-MUSA we propose a QoS-based Munkres user scheduling algorithm (QB-MUSA) aimed at stabilizing queue lengths and maximizing throughput. In results, we show that QB-MUSA exhibits higher throughput than the conventional weighted sumrate (WSR) based algorithm