Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems

Liu, Ying; He, Chunlong; Li, Xingquan; Zhang, Ce; Tian, Chen

doi:10.1109/access.2019.2896134

Cited by 17 publications

(12 citation statements)

References 27 publications

(31 reference statements)

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“…Lacking of appropriate and adequate dataset is a bottleneck of applicating ML in communication networks. A promising solution is to obtain the dataset through simulation [21]- [23]. We use system-level simulation to generate and collect data.…”

Section: B Data Generation and Collectionmentioning

confidence: 99%

Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario

Zhang

et al. 2019

IEEE Access

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The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC) are the two main scenarios of 5 th generation (5G) mobile communication system networks. There is an obvious difference in service requirements between different scenarios. When multi-scenario services coexist in the 5G networks, exploring optimized resource scheduling and allocation strategies become a critical issue. The 5G New Radio (NR) and numerology technologies have been standardized, which lay the foundation for flexible frame structure and adaptive scheduling. In this paper, we propose the self-adaptive flexible transmission time interval (TTI) scheduling (SAFE-TS) strategy in the eMBB and uRLLC coexistence scenario. Machine learning (ML) is applied to achieve flexible TTI scheduling. Moreover, we design the random forest-based ensemble TTI decision algorithm (RF-ETDA) to accomplish the TTI selection for each service. Compared with the existing ML methods, the proposed algorithm has a performance improvement in selecting TTI, especially for the uRLLC services. Then, the TTI selection results will be the basis of system resource scheduling and allocation. The simulation results prove that the proposed SAFE-TS effectively reduce the delay and packet loss rate of the uRLLC services while guaranteeing the eMBB requirements. Therefore, it is highly recommended that flexible TTI scheduling should be applied in the construction of the 5G networks to achieve superior network performances. INDEX TERMS Flexible TTI scheduling, machine learning, delay, control overhead, eMBB, uRLLC.

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Section: B Data Generation and Collectionmentioning

confidence: 99%

Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario

Zhang

et al. 2019

IEEE Access

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“…In [ 17 ], the authors maximized the network-centric EE and user-centric EE in DAS with WPT. In [ 18 ], the authors maximized the EE in DAS with SWIPT [ 19 , 20 , 21 ], where the power splitting (PS) receiver architecture was employed to fulfill EH and information decoding. In [ 22 ], the system goal was to maximize secrecy EE by jointly optimizing the confidential signal’s power, the artificial noise power, and the power-splitting ratio.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this paper aims to maximize the system EE, to achieve the green communication system design. Although some existing works investigated the EE for DAS with RF EH, only the constraint of power control was considered, where the user’s information rate requirement was not involved; see, e.g., [ 17 , 18 ], while in our work, the system EE is maximized with the user’s minimal information rate requirement, which is much closer to the users’ demands. Different from the most similar existing works [ 22 , 23 ] where the linear EH model was used, in this work, the nonlinear EH model is adopted, as it was reported that the nonlinear EH one was obtained with real measurement data [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although some existing works investigated the EE for DAS with RF EH, only the constraint of power control was considered, where the user’s information rate requirement was not involved; see, e.g., [ 17 , 18 ], while in our work, the system EE is maximized with the user’s minimal information rate requirement, which is much closer to the users’ demands.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficiency in RF Energy Harvesting-Powered Distributed Antenna Systems for the Internet of Things

Xiong

Cao

et al. 2020

Sensors

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This paper studies a distributed antenna system (DAS) network with radio frequency (RF) energy harvesting (EH) technology where the distributed antenna ports (DAPs) transmit energy and information to multiple users simultaneously. The time division multiple access (TDMA) protocol is adopted, so for each time slot is allowed to receive information, while the rest of the users harvest energy. In order to maximize the system energy efficiency (EE), subject to the EH requirements and data rate requirements of the users, the transmission time and power assignment are jointly optimized. In order to deal with this non-convex problem, based on Dinkelbach theory and the block-coordinate descent (BCD) scheme, an efficient algorithm is designed to obtain the global optimal solution. Then, simulation results are presented to show that the proposed method achieves much higher system EE compared with benchmark methods. With the increase of the user’s minimum information rate, the system EE decreases rapidly.

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“…The gaussian mixture model-based (GMM) algorithm contributes to clustering the users for interest estimate in social networks [27]. The authors applied the k-NN algorithm to classify the cellular users for getting the power allocation schemes of DAS according to the historical data constructing by the traditional method [28]. The Q-learning was introduced to offer an alternative option to solve the high complexity problem of the traditional resource allocation solution in multi-cell, multi-user system, which is possibly applied to DAS [22].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

Zhou

Qian

et al. 2019

IEEE Access

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In this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture modelbased (GMM) algorithm. Under the communication scenario of DAS with ML generated clusters model, we investigate two different power allocation optimization problems with the interference of maximizing spectral efficiency (SE) and energy efficiency (EE) in DAS, respectively. We compare the SE and EE of DAS with ML generated clusters model and the conventional model. The simulation results verify the effectiveness of DAS with ML generated clusters model, which can obtain the much better performance of SE and EE compared with the conventional communication model in DAS.INDEX TERMS Machine learning, spectral efficiency, energy efficiency, k-means, mixture of Gaussian model, distributed antenna system.

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Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems

Cited by 17 publications

References 27 publications

Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario

Machine Learning Based Flexible Transmission Time Interval Scheduling for eMBB and uRLLC Coexistence Scenario

Energy Efficiency in RF Energy Harvesting-Powered Distributed Antenna Systems for the Internet of Things

Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

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