Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands
In this paper, the drone base-station (DBS) dispatching problem in a multi-cell B5G/6G network is investigated. The main objective is to achieve the highest system profit by serving the largest possible number of users with the least possible cost while considering the uncertain time-dependent fluct...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9911648/ |
| _version_ | 1798021838812479488 |
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| author | Haythem Bany Salameh Ala'eddin Masadeh Ghaleb El Refae |
| author_facet | Haythem Bany Salameh Ala'eddin Masadeh Ghaleb El Refae |
| author_sort | Haythem Bany Salameh |
| collection | DOAJ |
| description | In this paper, the drone base-station (DBS) dispatching problem in a multi-cell B5G/6G network is investigated. The main objective is to achieve the highest system profit by serving the largest possible number of users with the least possible cost while considering the uncertain time-dependent fluctuated user’s (service) demand in the different cells, the cost of dispatched drones, and the possible profit loss due to un-served users. The problem is formulated as a profit-maximization discount return problem. Due to the uncertainty in the demand (users) in each cell, the problem cannot be solved using conventional optimization methods. Hence, the problem is reformulated as a Markov decision problem (MDP). Due to the exponential complexity of finding the solution and the unavailability of statistical knowledge about user availability (demand) in the considered regions for such optimization, we adopt a reinforcement learning (RL) approach based on the state-action-reward-state-action (SARSA) algorithm to efficiently solve the MDP. Simulation results reveal that our RL-based approach significantly increases the overall operator profit by continuously adapting its DBS dispatching strategy based on the learned users’ behavior in the network, which enables serving a larger number of users (highest revenue) with least number of DBSs (least cost). |
| first_indexed | 2024-04-11T17:19:56Z |
| format | Article |
| id | doaj.art-3a835983936a4d0cb423c0d0830e3e36 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T17:19:56Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a835983936a4d0cb423c0d0830e3e362022-12-22T04:12:31ZengIEEEIEEE Access2169-35362022-01-011010674010674910.1109/ACCESS.2022.32121499911648Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated DemandsHaythem Bany Salameh0https://orcid.org/0000-0003-3429-7212Ala'eddin Masadeh1Ghaleb El Refae2Department of Network and Communications Engineering, Al Ain University, Al Ain, United Arab EmiratesElectrical Engineering Department, Al-Huson University College, Al-Balqa Applied University, Al-Salt, JordanCollege of Business, Al Ain University, Al Ain, United Arab EmiratesIn this paper, the drone base-station (DBS) dispatching problem in a multi-cell B5G/6G network is investigated. The main objective is to achieve the highest system profit by serving the largest possible number of users with the least possible cost while considering the uncertain time-dependent fluctuated user’s (service) demand in the different cells, the cost of dispatched drones, and the possible profit loss due to un-served users. The problem is formulated as a profit-maximization discount return problem. Due to the uncertainty in the demand (users) in each cell, the problem cannot be solved using conventional optimization methods. Hence, the problem is reformulated as a Markov decision problem (MDP). Due to the exponential complexity of finding the solution and the unavailability of statistical knowledge about user availability (demand) in the considered regions for such optimization, we adopt a reinforcement learning (RL) approach based on the state-action-reward-state-action (SARSA) algorithm to efficiently solve the MDP. Simulation results reveal that our RL-based approach significantly increases the overall operator profit by continuously adapting its DBS dispatching strategy based on the learned users’ behavior in the network, which enables serving a larger number of users (highest revenue) with least number of DBSs (least cost).https://ieeexplore.ieee.org/document/9911648/Reinforcement learningrevenueon-demand dispatchinguncertain demanddrone base-station |
| spellingShingle | Haythem Bany Salameh Ala'eddin Masadeh Ghaleb El Refae Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands IEEE Access Reinforcement learning revenue on-demand dispatching uncertain demand drone base-station |
| title | Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands |
| title_full | Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands |
| title_fullStr | Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands |
| title_full_unstemmed | Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands |
| title_short | Intelligent Drone-Base-Station Placement for Improved Revenue in B5G/6G Systems Under Uncertain Fluctuated Demands |
| title_sort | intelligent drone base station placement for improved revenue in b5g 6g systems under uncertain fluctuated demands |
| topic | Reinforcement learning revenue on-demand dispatching uncertain demand drone base-station |
| url | https://ieeexplore.ieee.org/document/9911648/ |
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