| UAVs as mobile infrastructure: Addressing battery lifetime B Galkin, J Kibilda, LA DaSilva IEEE Communications Magazine 57 (6), 132-137, 2019 | 163 | 2019 |
| Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks B Galkin, J Kibilda, LA DaSilva 2016 Wireless Days (WD), 1-6, 2016 | 154 | 2016 |
| Coverage analysis for low-altitude UAV networks in urban environments B Galkin, J Kibilda, LA DaSilva GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, 2017 | 111 | 2017 |
| A stochastic model for UAV networks positioned above demand hotspots in urban environments B Galkin, J Kibiłda, LA DaSilva IEEE Transactions on Vehicular Technology 68 (7), 6985-6996, 2019 | 96 | 2019 |
| Backhaul for low-altitude UAVs in urban environments B Galkin, J Kibilda, LA DaSilva 2018 IEEE International Conference on Communications (ICC), 1-6, 2018 | 84 | 2018 |
| Performance analysis of mobile cellular-connected drones under practical antenna configurations R Amer, W Saad, B Galkin, N Marchetti ICC 2020-2020 IEEE international conference on communications (ICC), 1-7, 2020 | 48 | 2020 |
| Modelling multi-operator base station deployment patterns in cellular networks J Kibiłda, B Galkin, LA DaSilva IEEE Transactions on Mobile Computing 15 (12), 3087-3099, 2015 | 44 | 2015 |
| A stochastic geometry model of backhaul and user coverage in urban UAV networks B Galkin, J Kibiłda, LA DaSilva arXiv preprint arXiv:1710.03701, 2017 | 37 | 2017 |
| REQIBA: Regression and deep Q-learning for intelligent UAV cellular user to base station association B Galkin, E Fonseca, R Amer, LA DaSilva, I Dusparic IEEE Transactions on Vehicular Technology 71 (1), 5-20, 2021 | 27 | 2021 |
| Optimizing energy efficiency in UAV-assisted networks using deep reinforcement learning B Omoniwa, B Galkin, I Dusparic IEEE Wireless Communications Letters 11 (8), 1590-1594, 2022 | 20 | 2022 |
| Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning B Omoniwa, B Galkin, I Dusparic 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC …, 2022 | 16* | 2022 |
| Intelligent Base Station Association for UAV Cellular Users: A Supervised Learning Approach B Galkin, R Amer, E Fonseca, LA DaSilva IEEE 5G World Forum, 2020 | 16 | 2020 |
| Mobility for cellular-connected UAVs: Challenges for the network provider E Fonseca, B Galkin, M Kelly, LA DaSilva, I Dusparic 2021 Joint European Conference on Networks and Communications & 6G Summit …, 2021 | 13 | 2021 |
| Multi-agent deep reinforcement learning for optimising energy efficiency of fixed-wing UAV cellular access points B Galkin, B Omoniwa, I Dusparic ICC 2022-IEEE International Conference on Communications, 1-6, 2022 | 10 | 2022 |
| Experimental evaluation of a UAV user QoS from a two-tier 3.6 GHz spectrum network B Galkin, E Fonseca, G Lee, C Duff, M Kelly, E Emmanuel, I Dusparic 2021 IEEE International Conference on Communications Workshops (ICC …, 2021 | 9 | 2021 |
| Impact of UAV antenna configuration on wireless connectivity in urban environments B Galkin, J Kibiłda, LA DaSilva arXiv preprint arXiv:1807.00696, 2018 | 6 | 2018 |
| Communication-enabled deep reinforcement learning to optimise energy-efficiency in UAV-assisted networks B Omoniwa, B Galkin, I Dusparic Vehicular Communications 43, 100640, 2023 | 5 | 2023 |
| Adaptive height optimization for cellular-connected UAVs: A deep reinforcement learning approach E Fonseca, B Galkin, R Amer, LA DaSilva, I Dusparic IEEE Access 11, 5966-5980, 2023 | 5 | 2023 |
| Adaptive height optimisation for cellular-connected UAVs using reinforcement learning E Fonseca, B Galkin, R Amer, LA DaSilva, I Dusparic arXiv preprint arXiv:2007.13695, 2020 | 5 | 2020 |
| Deep reinforcement learning for combined coverage and resource allocation in uav-aided ran-slicing L Bellone, B Galkin, E Traversi, E Natalizio 2023 19th International Conference on Distributed Computing in Smart Systems …, 2023 | 4 | 2023 |
