User Association and Resource Allocation Optimization in LTE Cellular Networks

Trabelsi, Nessrine; Chen, Chung Shue; Azouzi, Rachid El; Roullet, Laurent; Altman, Eitan

doi:10.1109/tnsm.2017.2677778

Cited by 38 publications

(28 citation statements)

References 16 publications

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“…Atualmente, diversos trabalhos com propostas de mitigação de interferências baseados em Game Theory para redes LTE/LTE-A/5G têm sido publicados. Soluções em jogos potenciais para otimização eICIC distribuída são consideradas nos estudos em [Trabelsi et al 2017, Liu et al 2015a, Liu et al 2015b.…”

Section: Revisão Da Literaturaunclassified

“…A densificação da rede apresenta-se como um elemento chave para aumentar a cobertura do sistema e atenderà crescente demanda por dados móveis de maneira econômica devido a sua capacidade de reutilização do espectro de frequência e a propriedade de preservação da SINR [Dhillon et al 2012]. Além disso, a técnica reduz as distâncias médias entre um UE e a BS (do inglês, base station) mais próxima e, assim, diminui a perda de percurso do sinal transmitido e melhora o ganho e a capacidade do canal [Trabelsi et al 2017]. Com isso, obtém-se uma capacidade do sistema (volume de tráfego por unidade deárea) e taxas de dados elevadas.…”

Section: Introductionunclassified

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Técnicas de Coordenação de Interferência Inter-Celular para Redes Heterogêneas no Downlink LTE-A

Moreira

Silva

Guardieiro

2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

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Future generation cellular networks are expected to provide broad-band access to a continuously growing number of mobile users. The densification of cellular networks is necessary to increase their capacity and improve spectral efficiency, however, in these systems, ICI still represents a real challenge that limits system performance, especially for users located at the edge of the cell. This paper briefly reviews the ICIC techniques in the downlink LTE-A of mobile cellular networks as an approach to alleviate the impact of interference and improve performance. The simulation results show the advantages and limitations of the eICIC and FeICIC techniques compared to the model without interference control.

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Section: Revisão Da Literaturaunclassified

Section: Introductionunclassified

Técnicas de Coordenação de Interferência Inter-Celular para Redes Heterogêneas no Downlink LTE-A

Moreira

Silva

Guardieiro

2019

Anais Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2019)

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“…Liu et al,in [12], propose to optimize the probability that a macro BS offers almost blank resource blocks on both time and frequency dimensions. Potential game based solutions for distributed eICIC optimization are considered in [13]- [15].…”

Section: A Related Workmentioning

confidence: 99%

A Game Theoretic Distributed Algorithm for FeICIC Optimization in LTE-A HetNets

Liu

Chen

Sung

et al. 2017

IEEE/ACM Trans. Networking

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“…Although current approximation methods demand low computational complexity, they are usually assumed to know the perfect knowledge about the channel state information (CSI) [10,11], which may be impractical for a dynamic wireless environment due to the huge overhead for feedback channel. In addition, the discrete optimization assisted approaches mainly utilize game theory or graph theory to solve the joint optimization problem effectively with acceptable amount of complexity [12,13]. Nevertheless, they are hardly able to solve the multi-objective optimization problems.…”

mentioning

confidence: 99%

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

Liao

Shi

et al. 2020

IEEE Trans. Veh. Technol.

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A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks. IEEE Transactions on Vehicular Technology, 69(1), pp. 983-997.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.Abstract-Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness.

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User Association and Resource Allocation Optimization in LTE Cellular Networks

Cited by 38 publications

References 16 publications

Técnicas de Coordenação de Interferência Inter-Celular para Redes Heterogêneas no Downlink LTE-A

Técnicas de Coordenação de Interferência Inter-Celular para Redes Heterogêneas no Downlink LTE-A

A Game Theoretic Distributed Algorithm for FeICIC Optimization in LTE-A HetNets

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

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