On the study of fundamental trade-offs between QoE and energy efficiency in wireless networks

Light causes damage when it is absorbed by sensitive artwork, such as oil paintings. However, light is needed to initiate vision and display artwork. The dilemma between visibility and damage, coupled with the inverse relationship between color quality and energy efficiency, poses a challenge for curators, conservators, and lighting designers in identifying optimal light sources. Multi-primary LEDs can provide great flexibility in terms of color quality, damage reduction, and energy efficiency for artwork illumination. However, there are no established metrics that quantify the output variability or highlight the trade-offs between different metrics. Here, various metrics related to museum lighting (damage, the color quality of paintings, illuminance, luminous efficacy of radiation) are analyzed using a voxelated 3-D volume. The continuous data in each dimension of the 3-D volume are converted to discrete data by identifying a significant minimum value (unit voxel). Resulting discretized 3-D volumes display the trade-offs between selected measures. It is possible to quantify the volume of the graph by summing unique voxels, which enables comparison of the performance of different light sources. The proposed representation model can be used for individual pigments or paintings with numerous pigments. The proposed method can be the foundation of a damage appearance model (DAM).

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Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽

10.3390/en12224300 ◽

2019 ◽

Vol 12 (22) ◽

pp. 4300 ◽

Cited By ~ 1

Author(s):

Hoon Lee ◽

Han Seung Jang ◽

Bang Chul Jung

Keyword(s):

Energy Efficiency ◽

Wireless Networks ◽

Deep Learning ◽

Power Control ◽

Control Method ◽

Optimal Solution ◽

Control Policy ◽

Maximization Problem ◽

Optimization Approach ◽

Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

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