A distributed solution for proportional fairness optimization in load coupled OFDMA networks

Gutierrez-Estevez, M. A.; Cavalcante, Renato L. G.; Stańczak, Sławomir; Zhang, J.; Zhuang, Hongcheng

doi:10.1109/globalsip.2016.7905902

Cited by 6 publications

(2 citation statements)

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“…) is the elevation angle. Based on (17) and (18), the average pathloss of a link can be expressed as…”

Section: B Data Transmissionmentioning

confidence: 99%

Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks

Zeng

Zhang

et al. 2021

Preprint

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In this paper, we consider a single-cell multi-user orthogonal frequency division multiple access (OFDMA) network with one unmanned aerial vehicle (UAV), which works as an amplify-andforward relay to improve the quality-of-service (QoS) of the user equipments (UEs) in the cell edge.Aiming to improve the throughput while guaranteeing the user fairness, we jointly optimize the communication mode, subchannel allocation, power allocation, and UAV trajectory, which is an NP-hard problem. To design the UAV trajectory and resource allocation efficiently, we first decompose the problem into three subproblems, i.e., mode selection and subchannel allocation, trajectory optimization, and power allocation, and then solve these subproblems iteratively. Simulation results show that the proposed algorithm outperforms the random algorithm and the cellular scheme.

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“…) is the elevation angle. Based on (17) and (18), the average pathloss of a link can be expressed as…”

Section: B Data Transmissionmentioning

confidence: 99%

Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks

Zeng

Zhang

et al. 2021

Preprint

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“…The load-coupling model (see, e.g., [1,2] and [3]) is widely used when designing networks according to the long-term evolution (LTE) standard and has also attracted attention in the context of fifth-generation (5G) networks. More specifically, the load-coupling model has been used in various optimization frameworks dealing with different aspects of network design including, but not limited to, data offloading [4], proportional fairness [5], energy optimization [6,7], and load balancing [8].…”

Section: Introductionmentioning

confidence: 99%

A Robust Machine Learning Method for Cell-Load Approximation in Wireless Networks

Awan

Cavalcante

Stańczak

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a learning algorithm for the problem of cell-load estimation in dense wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty due to a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales due to fast varying communication environment. The first part of this work studies the set of achievable rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the corresponding unique fixed point of the non-linear load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework which achieves the best worst-case performance. Moreover, the approximation preserves the monotonicity and continuity properties. Simulation results show that the proposed method exhibits better robustness and accuracy for small training sets when compared to standard approximation techniques for multivariate data.

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