File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/GCWkshps50303.2020.9367389
- Scopus: eid_2-s2.0-85102939595
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: NOMA in UAV-aided cellular offloading: A machine learning approach
Title | NOMA in UAV-aided cellular offloading: A machine learning approach |
---|---|
Authors | |
Issue Date | 2020 |
Citation | 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings, 2020, article no. 367389 How to Cite? |
Abstract | A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the aid of the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than that of invoking the circular trajectory and the 2D trajectory, respectively. |
Persistent Identifier | http://hdl.handle.net/10722/349543 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhong, Ruikang | - |
dc.contributor.author | Liu, Xiao | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Chen, Yue | - |
dc.date.accessioned | 2024-10-17T06:59:14Z | - |
dc.date.available | 2024-10-17T06:59:14Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings, 2020, article no. 367389 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349543 | - |
dc.description.abstract | A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional deep Q-network (DQN) algorithm, the MDQN algorithm enables the experience of multi-agent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the aid of the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than that of invoking the circular trajectory and the 2D trajectory, respectively. | - |
dc.language | eng | - |
dc.relation.ispartof | 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings | - |
dc.title | NOMA in UAV-aided cellular offloading: A machine learning approach | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/GCWkshps50303.2020.9367389 | - |
dc.identifier.scopus | eid_2-s2.0-85102939595 | - |
dc.identifier.spage | article no. 367389 | - |
dc.identifier.epage | article no. 367389 | - |