In the context of Long-Term Evolution (LTE), the next generation mobile telecommunication network, femtocells are lowpower base stations that efficiently provide coverage and capacity indoors. This paper presents a computationally efficient dynamic system-level LTE simulator for enterprise femtocell scenarios. The simulator includes specific mobility and traffic and propagation models for indoor environments. A physical layer abstraction is performed to predict link-layer performance with low computational cost. At link layer, two important functions are included to increase network capacity: Link Adaptation and Dynamic Scheduling. At network layer, other Radio Resource Management functionalities, such as Admission Control and Mobility Management, are also included. The resulting tool can be used to test and validate optimization algorithms in the context of Self-Organizing Networks (SON).
Mobility Load Balancing (MLB) is a common technique to deal with the uneven traffic distribution in mobile networks. The aim of MLB is to alleviate congestion problems by sharing traffic demand among neighbor cells through the modification of handover parameters. MLB has been successfully used in legacy radio access technologies. However, in Long Term Evolution (LTE), MLB may lead to severe network performance degradation due to the tight frequency reuse used in this technology.
In this paper, a comprehensive analysis of the limitations of MLB in LTE is done based on the results of a classical MLB algorithm in a live LTE network. Field trial results confirm that MLB reduces network congestion at the expense of degrading cell-edge user performance in the uplink of congested cells and the downlink of adjacent cells receiving traffic.Index Terms-LTE , Experimental and prototype results, Mobile network , Mobility Load Balance , Handover.
A computationally efficient self-planning algorithm for adjusting base station transmit power in a LTE system on a cell-by-cell basis is presented. The aim of the algorithm is to improve the overall network spectral efficiency in the downlink by reducing the transmit power of specific cells to eliminate interference problems. The main driver of the algorithm is a new indicator that predicts the impact of changes in the transmit power of individual cells on the overall network Signal to Interference plus Noise Ratio (SINR) for the downlink. Algorithm assessment is carried out over a static system-level simulator implementing a live LTE network scenario. During assessment, the proposed algorithm is compared with a state-of-the-art self-planning algorithm based on the modification of antenna tilt angles. Results show that the proposed algorithm can improve both network coverage and capacity significantly compared to other automatic planning methods.
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