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10th IEEE International Conference on Electronics Information and Emergency Communication (IEEE ICEIEC)

作者： Wei An Liu, Yaxi Wei Huangfu Univ Sci & Technol Beijing USTB Beijing Engn & Technol Ctr Convergence Networks & Beijing Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan Peoples R China

ISBN: (数字)9781728163130

ISBN: (纸本)9781728163130;9781728163123

It is an essential task for mobile operators to extend the coverage and improve the capacity by adjusting the radio parameters in cellular networks, especially in self-organizing networks. In the existing algorithm, the decoration of target local performance is neglected, which severely deteriorates the algorithm efficiency. In this paper, we propose a hierarchy induced coordinate descent algorithm, which focuses on the local decoration of the target function to solve the coverage and capacity optimization problems by obtaining the optimal antenna tilt and azimuth settings in cellular networks. Such algorithm alternatively executes the optimization step decision where the original complex constrained optimization problem is converted into a non-constrained simple one with the penalty term and the optimization variable selection that balances the influences of the hierarchy and the distance according to different system parameter settings. Besides, in the optimization step decision mentioned above, we adopt the non-linear interpolation to obtain the optimal optimization direction and its corresponding length.

关键词： Cellular networks coordinate descent algorithm coverage and capacity antenna tilt and azimuth hierarchy-induced

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Self-optimization of coverage and capacity based on a fuzzy neural network with cooperative reinforcement learning

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2014年第1期2014卷 1页

作者： Fan, Shaoshuai Tian, Hui Sengul, Cigdem Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Oxford Brookes Univ Dept Comp & Commun Technol Oxford OX33 1HX England

Self-organization is a key concept in long-term evolution (LTE) systems to reduce capital and operational expenditures (CAPEX and OPEX). Self-optimization of coverage and capacity, which allows the system to periodically and automatically adjust the key radio frequency (RF) parameters through intelligent algorithms, is one of the most important tasks in the context of self-organizing networks (SON). In this paper, we propose self-optimization of antenna tilt and power using a fuzzy neural network optimization based on reinforcement learning (RL-FNN). In our approach, a central control mechanism enables cooperation-based learning by allowing distributed SON entities to share their optimization experience, represented as the parameters of learning method. Specifically, SON entities use cooperative Q-learning and reinforced back-propagation method to acquire and adjust their optimization experience. To evaluate the coverage and capacity performance of RL-FNN, we analyze cell-edge performance and cell-center performance indicators jointly across neighboring cells and specifically consider the difference in load distribution in a given region. The simulation results show that RL-FNN performs significantly better than the best fixed configuration proposed in the literature. Furthermore, this is achieved with significantly lower energy consumption. Finally, since each self-optimization round completes in less than a minute, RL-FNN can meet the need of practical applications of self-optimization in a dynamic environment.

关键词： LTE Self-organizing network coverage and capacity Fuzzy neural network Reinforcement learning

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coverage and capacity optimization in LTE network based on non-cooperative games

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The Journal of China Universities of Posts and Telecommunications 2012年第4期19卷 14-21,42页

作者： XU Sen HOU Meng NIU Kai HE Zhi-qiang WU Wei-ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic Engineering and Automation Shandong Polytechnic University Jinan 250353 China

expenditures and operational expenditures （OPEX）（CAPEX） for operator, the coverage and capacity optimization （CCO） is one of the key use cases in long term evolution （LTE） self-organization network （SON）. In LTE system, some factors （e.g. load, traffic type, user distribution, uplink power setting, inter-cell interference, etc.） limit the coverage and capacity performance. From the view of single cell, it always pursuits maximize performance of coverage and capacity by optimizing the uplink power setting and intra-cell resource allocation, but it may result in decreasing the performance of its neighbor cells. Therefore, the benefit of every cell conflicts each other. In order to tradeoff the benefit of every cell and maximize the performance of the whole network, this paper proposes a multi-cell uplink power allocation scheme based on non-cooperative games. The scheme aims to make the performance of coverage and capacity balanced by the negotiation of the uplink power parameters among multi-cells. So the performance of every cell can reach the Nash equilibrium, making it feasible to reduce the inter-cell interference by setting an appropriate uplink power parameter. Finally, the simulation result shows the proposed algorithm can effectively enhance the performance of coverage and capacity in LTE network.

关键词： coverage and capacity power control non-cooperative game Nash equilibrium

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A Tractable Approach To Small Cell Dimensioning

A Tractable Approach To Small Cell Dimensioning

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Wu, Yanyan Xian Jiatong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Jiangsu Peoples R China

ISBN: (纸本)9781538617342

Dynamic dimensioning adapting both base station (BS) density and maximum BS transmission power for varying traffic demands requires a tractable approach to two competing objectives of cell planning: coverage and rate capacity. This paper introduces a mathematical modeling of spatial distributions of other cell interference (OCIF) and interference to own-cell power ratio (IOPR) for down-link reception with respect to the distance between a mobile and its serving BS following the best-cell configuration. This allows a more realistic evaluation of the relation between coverage and capacity than nearest-cell configuration, based on which analytical results of maximum BS transmission power vs. cell size, and (admitted) user density vs. cell size are derived for small-cell dimensioning purpose.

关键词： Cell Dimensioning coverage and capacity

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A Tractable Approach To Small Cell Dimensioning

A Tractable Approach To Small Cell Dimensioning

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IEEE Wireless Communications and Networking Conference

作者： Yanyan Wu Department of Electrical and Electronics Engineering Xi'an Jiatong-Liverpool University Suzhou Jiangsu P R China

关键词： Cell Dimensioning coverage and capacity

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Improved Fuzzy Reinforcement Learning for Self-Optimisation of Heterogeneous Wireless Networks

Improved Fuzzy Reinforcement Learning for Self-Optimisation ...

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20th International Conference on Telecommunications (ICT)

作者： Razavi, Rouzbeh Claussen, Holger Bell Labs Alcatel Lucent Dublin 15 Ireland

ISBN: (纸本)9781467364263

In this paper, a novel scheme to improve learning mechanism for future self-organising networks' functionalities is presented using a combination of fuzzy logic and reinforcement learning. Although the two frameworks compliment each other well, an efficient reward distribution mechanism needs to be deployed or otherwise the learning performance may be degraded. This study introduces an improved reward distribution (IRD) scheme in that the action space is abstracted to represent only the actions that are most relevant to the final crisp executed action after defuzzification. As a case study, coverage and capacity optimisation of heterogeneous networks consisting of dense deployment of small cells is considered. Using the proposed method, simulation results confirm considerable performance enhancment in terms of learning efficiency and convergence time.

关键词： Self-Organising Networks Self-optimisation reinforcement learning Fuzzy logic Small cells Heterogeneous Networks metrocell Femtocells coverage and capacity

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