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2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, C., see Li, M  ...  ., TWC Jan. 2020 650-664 Huang, A., see He, H., TWC Dec. 2020 7881-7896 Huang, C., Molisch, A.F., He, R., Wang, R., Tang, P., Ai, B., and Zhong, Z., Machine Learning-Enabled LOS/NLOS Identification  ...  ., +, TWC Oct. 2020 6520-6534 Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Subject index

2021 Journal of Systems Engineering and Electronics  
and multi-decision evolutionary game model based on multi-agent reinforcement learning •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 3-  ...  multi-user massive MIMO system •••••••••••••••••••••••••••••••••••••• 2-399 A criterion based adaptive RSIC scheme in underwater communication •••••••••••••••••••••••••••••••••••••••••• 2-408 Integrating  ...  control for fractional-order linear multi-agent systems with state-delays Constrained voting extreme learning machine and its application Enhanced two-loop model predictive control design for linear uncertain  ... 
doi:10.23919/jsee.2021.9679721 fatcat:x76rw4j6bjbcnamwooh37hozhq

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Chen Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Pollin Graph Attention Network-Based Multi-Agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3114973 fatcat:zozra4xapraxfc2smyvlx7pnh4

2021 Index IEEE Transactions on Vehicular Technology Vol. 70

2021 IEEE Transactions on Vehicular Technology  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TVT April 2021 3978-3983 Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems.  ...  ., +, TVT April 2021 3978-3983 Orchestrated Scheduling and Multi-Agent Deep Reinforcement Learning for Cloud-Assisted Multi-UAV Charging Systems.  ... 
doi:10.1109/tvt.2022.3151213 fatcat:vzuzqu54irebpibzp3ykgy5nca

SAM 2020 Author Index

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Point Cloud Map Generation Based on FMCW Radar Yang, Luxi SS08.1 Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning Yang, Minglei R11.1  ...  DOA Estimation of Quasi- Stationary Signals in the Presence of Malfunctioning Sensors Xu, Chunmei SS08.1 Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement  ... 
doi:10.1109/sam48682.2020.9104397 fatcat:cfp5gsikrzabhhcnkalahjkxze

Table of Contents

2022 IEEE Transactions on Vehicular Technology  
Yu 4277 Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference .  ...  Dynamic Beam Pattern and Bandwidth Allocation Based on Multi-Agent Deep Reinforcement Learning for Beam Hopping Satellite Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2022.3164386 fatcat:g3v2t2enmbe3nfqkcuebwc4rte

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TWC Feb. 2021 1363-1378 Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks.  ...  ., +, TWC June 2021 3723-3733 Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq

Table of Contents

2022 IEEE Transactions on Vehicular Technology  
Bozorgchenani 391 Learning to Schedule Joint Radar-Communication With Deep Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . .  ...  Chiang 503 Vision-Based Localization in Multi-Agent Networks With Communication Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3139860 fatcat:heyeekeuvbb7rohazhzmtwoswy

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Monteiro 13112 Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning . 13424 Performance Analysis of Cellular Downlink With Fluctuating Two-Ray  ...  Shoji 13849 Scalable Parallel Task Scheduling for Autonomous Driving Using Multi-Task Deep Reinforcement Learning .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2020.3031771 fatcat:fnby2geqgzggrenxk5blu6sn5y

[SAM 2020 Title Page]

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning K 90 1570620846 Kernel interpolation of acoustic transfer function between regions considering reciprocity 91 1570620972 Knowledge-Aided  ...  78 1570618233 Hybrid Transceiver Design for Dual-Functional Radar-Joint Transmit Waveforms and Receive Filters Design for Large-Scale MIMO Beampattern Synthesis 89 1570617112 Joint User Scheduling and  ... 
doi:10.1109/sam48682.2020.9104267 fatcat:erntqdmhdrdspcrkvjowtplyyq

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Ansari Multi-Agent Deep Reinforcement Learning for Computation Offloading and Interference Coordination in Small Cell Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Gui A Multi-Agent Reinforcement Learning Approach for Capacity Sharing in Multi-Tenant Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3108593 fatcat:feewwd6epzgjvg7v3g3dpmxopa

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Misra 4993 Sustainable Task Offloading in UAV Networks via Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Niyato 4924 Joint Network Control and Resource Allocation for Space-Terrestrial Integrated Network Through Hierarchal Deep Actor-Critic Reinforcement Learning . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3080800 fatcat:kek3g7wqlzeufhxs26h4qxe5fm

Multi-Radar Tracking Optimization for Collaborative Combat [article]

Nouredine Nour, Reda Belhaj-Soullami, Cédric Buron, Alain Peres, Frédéric Barbaresco
2020 arXiv   pre-print
In this paper, we propose two novel reward-based learning approaches to decentralized netted radar coordination based on black-box optimization and Reinforcement Learning (RL).  ...  We apply these techniques on a simulation where radars can follow multiple targets at the same time and show they can learn implicit cooperation by comparing them to a greedy baseline.  ...  Recent successes in multi-agent RL were obtained by adapting mono-agent deep RL methods to the multiagent case, most of them based on policy gradient approaches [7] with a centralized learning and decentralized  ... 
arXiv:2010.11733v1 fatcat:yfrqehf63fholk4m3oqlwklbne

Research on Efficient Reinforcement Learning for Adaptive Frequency-Agility Radar

Xinzhi Li, Shengbo Dong
2021 Sensors  
In order to improve the adaptability of frequency-agile radar under complex environmental conditions, reinforcement learning (RL) is introduced into the radar anti-jamming research.  ...  There are two aspects of the radar system that do not obey with the Markov decision process (MDP), which is the basic theory of RL: Firstly, the radar cannot confirm the interference rules of the jammer  ...  Acknowledgments: The authors would like to thank Xiangyang Cui for their help in this work. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21237931 pmid:34883935 fatcat:yoj3gyjsuzekthqwfoebkpwf54

Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles [article]

Umut Demirhan, Ahmed Alkhateeb
2022 arXiv   pre-print
This article focuses on ten key machine learning roles for joint sensing and communication, sensing-aided communication, and communication-aided sensing systems, explains why and how machine learning can  ...  Realizing these gains in practice, however, is subject to several challenges where leveraging machine learning can provide a potential solution.  ...  For example, distributed reinforcement learning based solutions can enable network level management with minimal coordination, thanks to its network level learning capability through the distributed agents  ... 
arXiv:2208.02157v2 fatcat:obypfcwomzdxfbrw6w64pq3xsm
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