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In this paper, the problem of minimizing energy and time consumption for task computation and transmission is studied in a mobile edge computing (MEC)-enabled balloon network.
“A joint learning and communications framework for federated learning over wireless networks,”
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, · 1909
Earlier work this paper cites.
“Energy efficient federated learning over wireless communication networks,”
Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, · 1911
Earlier work this paper cites.
“Deep learning for optimal deployment of UAVs with visible light communications,” 2019,
Y. Wang, M. Chen, T. Luo Z. Yang, and W. Saad, · 1912
Earlier work this paper cites.
A Statistical Study of On-line Learning: Online Learning and Neural Networks
N. Murata, · 1998
Earlier work this paper cites.
International Telecommunications Union
Recommendation ITU-R M.1456, · 2000
Earlier work this paper cites.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
B. Scholkopf and A. J. Smola, · 2001
Earlier work this paper cites.
“Integrated services from high-altitude platforms: A flexible communication system,”
E. Falletti, M. Laddomada, M. Mondin, and F. Sellone, · 2006
Earlier work this paper cites.
“Exploiting platform diversity for GoS improvement for users with different high altitude platform availability,”
Y. Liu, D. Grace, and P. D. Mitchell, · 2009
Earlier work this paper cites.
“Ballooning: An agent-based search strategy in wireless sensor and actor networks,”
Z. Xu, C. Chen, Y. Guo, and X. Guan, · 2011
Earlier work this paper cites.
“Using Lagrangian relaxation for radio resource allocation in high altitude platforms,”
A. Ibrahim and A. S. Alfa, · 2015
Earlier work this paper cites.
“CVX: Matlab software for disciplined convex programming, version 3.0,”
M. Grant and S. Boyd, · 2015
Earlier work this paper cites.
“Brute: Energy-efficient user association in cellular networks from population game perspective,”
S. Moon, H. Kim, and Y. Yi, · 2016
Earlier work this paper cites.
“Energy-efficient resource allocation for mobile-edge computation offloading,”
C. You, K. Huang, H. Chae, and B. Kim, · 2017
Earlier work this paper cites.
“Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations,”
H. Zhang, S. Huang, C. Jiang, K. Long, V. C. M. Leung, and H. V. Poor, · 2017
Cited alongside, same era.
“Joint millimeter wave and microwave resources allocation in cellular networks with dual-mode base stations,”
O. Semiari, W. Saad, and M. Bennis, · 2017
Cited alongside, same era.
“Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,”
M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and C. S. Hong, · 2017
Cited alongside, same era.
“Federated multi-task learning,”
V. Smith, C. K. Chiang, M. Sanjabi, and A. Talwalkar, · 2017
Cited alongside, same era.
“Airborne communication networks: A survey,”
X. Cao, P. Yang, M. Alzenad, X. Xi, D. Wu, and H. Yanikomeroglu, · 2018
Cited alongside, same era.
“Beyond 5G with UAVs: Foundations of a 3D wireless cellular network,”
M. Mozaffari, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, · 2019
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“Deep learning for hybrid 5G services in mobile edge computing systems: Learn from a digital twin,”
R. Dong, C. She, W. Hardjawana, Y. Li, and B. Vucetic, · 2019
Later among the works it cites.
“Online proactive caching in mobile edge computing using bidirectional deep recurrent neural network,”
L. Ale, N. Zhang, H. Wu, D. Chen, and T. Han, · 2019
Later among the works it cites.
“Artificial neural networks-based machine learning for wireless networks: A tutorial,”
M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, · 2019
Later among the works it cites.
“Client selection for federated learning with heterogeneous resources in mobile edge,”
T. Nishio and R. Yonetani, · 2019
Later among the works it cites.
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“Sum-rate analysis for high altitude platform (HAP) drones with tethered balloon relay,”
P. G. Sudheesh, M. Mozaffari, M. Magarini, W. Saad, and P. Muthuchidambaranathan, · 2018
Cited alongside, same era.
“Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning,”
S. Jeong, O. Simeone, and J. Kang, · 2018
Cited alongside, same era.
“3-D placement of an unmanned aerial vehicle base station for maximum coverage of users with different QoS requirements,”
M. Alzenad, A. El-Keyi, and H. Yanikomeroglu, · 2018
Cited alongside, same era.
“Air-ground integrated vehicular network slicing with content pushing and caching,”
S. Zhang, W. Quan, J. Li, W. Shi, P. Yang, and X. Shen, · 2018
Cited alongside, same era.
“Computation scheduling for distributed machine learning with straggling workers,”
M. M. Amiri and D. Gunduz, · 2018
Cited alongside, same era.
“A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,”
M. Mozaffari, W. Saad, M. Bennis, Y. Nam, and M. Debbah, · 2019
Cited alongside, same era.
“UAV communications based on non-orthogonal multiple access,”
Y. Liu, Z. Qin, Y. Cai, Y. Gao, G. Y. Li, and A. Nallanathan, · 2019
Cited alongside, same era.
“High-resolution ITU-R cloud attenuation model for satellite communications in tropical region,”
F. Yuan, Y. H. Lee, Y. S. Meng, S. Manandhar, and J. T. Ong, · 2019
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“Federated learning over wireless networks: Optimization model design and analysis,”
N. H. Tran, W. Bao, A. Zomaya, M. N. H. Nguyen, and C. S. Hong, · 2019
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“Federated learning for energy-efficient task computing in wireless networks,”
S. Wang, M. Chen, W. Saad, and C. Yin, · 2020
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Wireless Communications and Networking for Unmanned Aerial Vehicles
W. Saad, M. Bennis, M. Mozaffari, and X. Lin, · 2020
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“A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,”
W. Saad, M. Bennis, and M. Chen, · 2020
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“Adaptive online decision method for initial congestion window in 5G mobile edge computing using deep reinforcement learning,”
R. Xie, X. Jia, and K. Wu, · 2020
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“Distributed federated learning for ultra-reliable low-latency vehicular communications,”
S. Samarakoon, M. Bennis, W. Saad, and M. Debbah, · 2020
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