Sum rate maximization of D2D networks with energy constrained UAVs through deep unsupervised learning

Lea, Benjamin; Shome, Debaditya; Waqar, Omer; Tomal, Jabed H.

doi:10.1109/uemcon53757.2021.9666500

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Leveraging Deep Unsupervised Learning for Joint Passive Beamforming and Power Control of RIS-Aided D2D Networks with Energy Constrained Nodes

Madhan-Sohini,

Waqar

2024

2024 Wireless Telecommunications Symposium (WTS)

View full text Add to dashboard Cite

Leveraging Deep Unsupervised Learning for Joint Passive Beamforming and Power Control of RIS-Aided D2D Networks with Energy Constrained Nodes

Madhan-Sohini,

Waqar

2024

2024 Wireless Telecommunications Symposium (WTS)

View full text Add to dashboard Cite

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Zhou,

2024

Electronics

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.

show abstract

Sum rate maximization of D2D networks with energy constrained UAVs through deep unsupervised learning

Cited by 2 publications

References 15 publications

Leveraging Deep Unsupervised Learning for Joint Passive Beamforming and Power Control of RIS-Aided D2D Networks with Energy Constrained Nodes

Leveraging Deep Unsupervised Learning for Joint Passive Beamforming and Power Control of RIS-Aided D2D Networks with Energy Constrained Nodes

Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure

Contact Info

Product

Resources

About