Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks
This paper proposes two energy-efficient reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) communications toward beyond-5G (B5G). This can be especially useful in ad-hoc communication scenarios within a neighborhood with main-network conn...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10517756/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850051366085459968 |
|---|---|
| author | Ahmad Gendia Osamu Muta Sherief Hashima Kohei Hatano |
| author_facet | Ahmad Gendia Osamu Muta Sherief Hashima Kohei Hatano |
| author_sort | Ahmad Gendia |
| collection | DOAJ |
| description | This paper proposes two energy-efficient reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) communications toward beyond-5G (B5G). This can be especially useful in ad-hoc communication scenarios within a neighborhood with main-network connectivity problems such as in areas affected by natural disasters. To improve the system’s overall sum-rate performance, the UAV-operated mobile base station (UAV-MBS) can harness non-orthogonal multiple access (NOMA) as an efficient protocol to grant ground devices access to fast downlink connections. Dynamic selection of suitable hovering spots within the target zone where the battery-constrained UAV needs to be positioned as well as calibrated NOMA power control with proper device pairing are critical for optimized performance. We propose cost-subsidized multiarmed bandit (CS-MAB) and double deep Q-network (DDQN)-based solutions to jointly address the problems of dynamic UAV path design, device pairing, and power splitting for downlink data transmission in NOMA-based systems. To verify that the proposed RL-based solutions support high sum-rates, numerical simulations are presented. In addition, exhaustive and random search benchmarks are provided as baselines for the achievable upper and lower sum-rate levels, respectively. The proposed DDQN agent achieves 96% of the sum-rate provided by the optimal exhaustive scanning whereas CS-MAB reaches 91.5%. By contrast, a conventional channel state sorting pairing (CSSP) solver achieves about 89.3%. |
| format | Article |
| id | doaj-art-e391cf98ee8e43658baf4e4a504cf543 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-e391cf98ee8e43658baf4e4a504cf5432025-08-20T02:53:09ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01261763210.1109/TMLCN.2024.339643810517756Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA NetworksAhmad Gendia0https://orcid.org/0000-0003-4197-5288Osamu Muta1https://orcid.org/0000-0001-5100-9855Sherief Hashima2https://orcid.org/0000-0002-4443-7066Kohei Hatano3https://orcid.org/0000-0002-1536-1269Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, JapanFaculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, JapanComputational Learning Theory Team, RIKEN-AIP, Fukuoka, JapanComputational Learning Theory Team, RIKEN-AIP, Fukuoka, JapanThis paper proposes two energy-efficient reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) communications toward beyond-5G (B5G). This can be especially useful in ad-hoc communication scenarios within a neighborhood with main-network connectivity problems such as in areas affected by natural disasters. To improve the system’s overall sum-rate performance, the UAV-operated mobile base station (UAV-MBS) can harness non-orthogonal multiple access (NOMA) as an efficient protocol to grant ground devices access to fast downlink connections. Dynamic selection of suitable hovering spots within the target zone where the battery-constrained UAV needs to be positioned as well as calibrated NOMA power control with proper device pairing are critical for optimized performance. We propose cost-subsidized multiarmed bandit (CS-MAB) and double deep Q-network (DDQN)-based solutions to jointly address the problems of dynamic UAV path design, device pairing, and power splitting for downlink data transmission in NOMA-based systems. To verify that the proposed RL-based solutions support high sum-rates, numerical simulations are presented. In addition, exhaustive and random search benchmarks are provided as baselines for the achievable upper and lower sum-rate levels, respectively. The proposed DDQN agent achieves 96% of the sum-rate provided by the optimal exhaustive scanning whereas CS-MAB reaches 91.5%. By contrast, a conventional channel state sorting pairing (CSSP) solver achieves about 89.3%.https://ieeexplore.ieee.org/document/10517756/NOMA resource controlreinforcement learningUAV emergency communications |
| spellingShingle | Ahmad Gendia Osamu Muta Sherief Hashima Kohei Hatano Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks IEEE Transactions on Machine Learning in Communications and Networking NOMA resource control reinforcement learning UAV emergency communications |
| title | Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks |
| title_full | Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks |
| title_fullStr | Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks |
| title_full_unstemmed | Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks |
| title_short | Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks |
| title_sort | energy efficient trajectory planning with joint device selection and power splitting for mmwaves enabled uav noma networks |
| topic | NOMA resource control reinforcement learning UAV emergency communications |
| url | https://ieeexplore.ieee.org/document/10517756/ |
| work_keys_str_mv | AT ahmadgendia energyefficienttrajectoryplanningwithjointdeviceselectionandpowersplittingformmwavesenableduavnomanetworks AT osamumuta energyefficienttrajectoryplanningwithjointdeviceselectionandpowersplittingformmwavesenableduavnomanetworks AT sheriefhashima energyefficienttrajectoryplanningwithjointdeviceselectionandpowersplittingformmwavesenableduavnomanetworks AT koheihatano energyefficienttrajectoryplanningwithjointdeviceselectionandpowersplittingformmwavesenableduavnomanetworks |