A Globally Optimal Energy-Efficient Power Control Framework and Its Efficient Implementation in Wireless Interference Networks

Matthiesen, Bho; Zappone, Alessio; Besser, Karl-Ludwig; Jorswieck, Eduard A.; Debbah, Mérouane

doi:10.1109/tsp.2020.3000328

Cited by 65 publications

(61 citation statements)

References 32 publications

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“…Many researchers investigated the use of deep learning theory to minimize the energy consumption in 5G wireless networks [33]- [36]. For instance, the authors of [33] proposed a deep reinforcement learning-based Small cell base stations (SBSs) activation strategy to lower the energy consumption without comprising the quality of service.…”

Section: E Energy Efficiency Maximizationmentioning

confidence: 99%

“…Machine learning/ deep learning thus can help in building intelligent wireless networks that proactively predict the traffic and mobility of users and delivery services only when requested -subsequently reducing the power consumption in radio access networks. The authors of [36] developed a deep learning power control framework for energy efficiency maximization in wireless interference networks. Throughout the above-mentioned examples, deep learning can reduce energy consumption in 5G radio access networks.…”

Section: E Energy Efficiency Maximizationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Arjoune

Faruque

2020

2020 10th Annual Computing and Communication Workshop and Conference (CCWC)

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The advent of the wireless communications systems augurs new cutting-edge technologies, including self-driving vehicles, unmanned aerial systems, autonomous robots, the Internet of-Things, and virtual reality. These technologies require high data rates, ultra-low latency, and high reliability, all of which are promised by the fifth generation of wireless communication systems (5G). Many research groups state that 5G cannot meet its demands without artificial intelligence (AI) integration as 5G wireless networks are expected to generate unprecedented traffic giving wireless research designers access to big data that can help in predicting the demands and adjust cell designs to meet the users' requirements. Subsequently, many researchers applied AI in many aspects of 5G wireless communication design including radio resource allocation, network management, and cyber-security. In this paper, we provide an in-depth review of AI for 5G wireless communication systems. In this respect, the aim of this paper is to survey AI in 5G wireless communication systems by discussing many case studies and the associated challenges, and shedding new light on future research directions for leveraging AI in 5G wireless communications.

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Section: E Energy Efficiency Maximizationmentioning

confidence: 99%

Section: E Energy Efficiency Maximizationmentioning

confidence: 99%

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Arjoune

Faruque

2020

2020 10th Annual Computing and Communication Workshop and Conference (CCWC)

View full text Add to dashboard Cite

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“…The power control framework for wireless interference networks in Ref. [20] applies a branchand-bound procedure to find the bounds for the energyefficient maximization problem.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning based energy-efficient internet-of-things video transmission

Xiao

Niu

Xiao

et al. 2020

Intell. and Converged Netw.

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“…However, the unsupervised learning cannot be generalized due to that the loss function in discrete allocation such as subchannel allocation and user association may be non-differential and may not converge in training phase. Rather than maximizing network throughput, Matthiesen et al developed a deep learning power control framework for energy efficiency maximization in wireless interference networks [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based User Association in Heterogeneous Wireless Networks

Zhang

Xiong

Yu³

2020

IEEE Access

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Due to the high splitting-gain of dense small cells, Ultra-Dense Network (UDN) is widely regarded as a promising networking technology to achieve high data rate and low latency in 5G mobile communications. In UDNs, user association is an open NP-hard problem due to the high computational complexity. In this paper, we study the user association problem from a deep learning perspective. We propose a U-Net based deep learning scheme aimed at intelligently associating user equipments(UE) to the competing Macro Base Stations (MBS) and small Base Stations (SBS). We formulate the user association problem as a constrained combinatorial optimization problem and employ a cross-entropy algorithm to obtain its asymptotically optimal solutions for labelling in supervised learning. We define a differentiable loss function by combining the Mean Squared Errors(MSE) criterion and the fairness and load balancing constraints for the supervised deep learning framework. We first train the U-Net based learning model and then evaluate the accuracy of the proposed scheme. Simulation results show that the proposed U-Net scheme approaches the asymptotically optimum Genetic Algorithm (GA) scheme in terms of minimum rate gain and sum rate gain, whereas outperforms the latter with significantly reduced computation time and robustness to network scales.

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A Globally Optimal Energy-Efficient Power Control Framework and Its Efficient Implementation in Wireless Interference Networks

Cited by 65 publications

References 32 publications

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Direction

Reinforcement learning based energy-efficient internet-of-things video transmission

Deep Learning Based User Association in Heterogeneous Wireless Networks

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