Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO) optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO) approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI) is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

Appropriate and correct indoor positioning in wireless networks could provide interesting services and applications in many domains. There are Time of Arrival (TOA), Time Difference ofArrival (TDOA), Angle of Arrival (AOA), and location fingerprinting schemes that can be used for positioning. We locus on location fingerprinting in this paper since it is more applicable to complex indoor environments than other schemes. Location fingerprinting uises received signal strength to estimate locations of mobile nodes or uisers. Probabilistic method, k-nearest-neighbor, and neural networks are pi-eviously proposed positioning techniques based on location fingerprintitng. However, most ofthese previouis works only concentrate on accuracy, which means the average distance error. Actlually, it is not enough to measure the performance ofa positioning techniquie by the accutracy only. A comprehensive performance comparison is also critical and helpfidl in order to choose the most fitting algorithm in real environments. In this paper, we compare comprehensively various performance metrics including accuracy, precision, complexitv, robuistness, and scalability. Through our analysis and experiment results, k-nearestneighbor reports the best overall performance for the indoor positioning purpose.

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In vehicular communications, roadside units are the key components to collect/disseminate information from/to vehicles. In this paper we investigate the roadside unit deployment problem in vehicle-to-infrastructure communications. This problem is formulated as a constrained optimization problem with the objective to minimize the deployment cost subject to the constraints that all service areas should be covered. The roadside unit deployment problem under consideration is a binary integer programming problem. We solve it by the branch and bound method which could effectively reduce the complexity.Index Terms-Vehicular communications, roadside units, integer programming, V2I.