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). I...
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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.
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 periodica...
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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.
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, t...
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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.
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 ...
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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.
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 framewo...
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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.
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 ...
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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.
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