The mobile network optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic networkoptimization (DNO) concept emerged years ago, aimed to continuously optimize the network i...
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ISBN:
(纸本)9789532900439
The mobile network optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic networkoptimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.
In mobilenetworks, the assignment of base stations to controllers when planning the network has a strong impact on network performance. In a previous paper, the authors formulated the assignment of base stations to p...
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In mobilenetworks, the assignment of base stations to controllers when planning the network has a strong impact on network performance. In a previous paper, the authors formulated the assignment of base stations to packet controllers in GSM-EDGE Radio Access network (GERAN) as a graph partitioning problem, which was solved by a heuristic method. In this paper, an exact method is presented to find optimal solutions that can be used as a benchmark. The proposed method is based on an effective re-formulation of the classical integer linear programming model of the graph partitioning problem, which is solved by the branch-and-cut algorithm in a commercial optimization package. Performance assessment is based on an extensive set of problem instances built from data of a live network. Preliminary analysis shows some properties of the graphs in this problem justifying the limitations of heuristic approaches and the need for more sophisticated methods. Results show that the proposed method outperforms classical heuristic algorithms used for benchmarking, even under runtime constraints. Likewise, it improves the efficiency of exact methods previously applied to similar problems in the cellular field. (C) 2011 Elsevier B.V. All rights reserved.
In a mobile communications network, the uneven spatial distribution of traffic demand can be dealt with by redefining cell service areas. When resizing cells, network operators often aim at equalizing call blocking ra...
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In a mobile communications network, the uneven spatial distribution of traffic demand can be dealt with by redefining cell service areas. When resizing cells, network operators often aim at equalizing call blocking rates throughout the network in the hope that this will maximize the total carried traffic in the network. In this paper, an analytical teletraffic model for the traffic sharing problem in GSM-EDGE radio access network (GERAN) is presented. From this model, a closed-form expression is derived for the optimal traffic sharing criterion between cells. Using this expression, it can be shown that balancing call blocking rate between adjacent cells is not the optimal strategy. Results show that using the optimal traffic sharing criterion instead of balancing blocking rates across the network can increase network capacity, even under restrictions in the cell resizing process.
In this paper, we propose a fine-grained grid-based multi-objective model which aims at optimizing base station antennas' configurations, such as transmit power, antenna tilt and antenna azimuth, in order to upgra...
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In this paper, we propose a fine-grained grid-based multi-objective model which aims at optimizing base station antennas' configurations, such as transmit power, antenna tilt and antenna azimuth, in order to upgrading network performance in cellular networks. As the model is non-convex, non-smooth and discrete and computationally expensive, we use decomposition method to solve the MOP problem. We mainly focus on addressing the scalarized sub-problem after decomposition. For the scalarized sub-problem, we propose an enhanced difference method. First, difference of each component is calculated, which provides the guidance of optimization. Then an OPSO is applied to search the optimal step length. The method is applied to GSM networkoptimization on an area in Beijing. The effect of the application shows that proposed method has a good performance, and is effective/efficient to solve mobile network optimization problems.
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