Deep Reinforcement Learning for Joint UAV Trajectory and Communication Design in Cache-Enabled Cellular Networks Article Swipe
Bathula Prasanna Kumar
,
U. S. B. K. Mahalaxmi
,
V. Nagagopiraju
,
Ashok Kumar Manda
,
K Siri Chandana
,
Suresh Betam
,
Akurathi Gangadhar
,
Sarala Patchala
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14419/djn77m90
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14419/djn77m90
Unmanned Aerial Vehicles (UAVs) are now widely used in communication networks. They help in delivering data in areas where the demand is high. This paper studies how UAVs work with cellular networks to provide better content transmission. The main goal is to reduce the time users wait to get the content they need. The researchers suggest using edge caching with UAVs. This means UAVs store popular data before users request it. The UAVs move based on an optimized path to deliver data efficiently. We also adjust transmission power. This reduces delays and improves the user experience. The challenge is that users request data randomly. UAVs move dynamically, which adds uncertainty. Solving this problem with normal optimization methods is difficult. Instead, we use deep reinforcement learning (DRL). We model the problem as a game where UAVs and a base station act as agents. These agents observe the environment and make decisions accordingly. The paper introduces a new method based on Proximal Policy Optimization (PPO). It is called Dual-Clip PPO. This method helps UAVs explore the environment efficiently. It also ensures that actions are optimal over time. A new reward system is introduced to guide UAV movement. The base station agent gets rewards from the environment, while UAVs receive an extra reward when they explore new areas. Simulations show that this new approach works better than existing methods. The proposed model reduces the time needed for users to receive content. It also performs better than standard PPO-based learning methods. This paper concludes that combining UAVs with caching and DRL improves communication networks. The method allows UAVs to move sensibly, place content efficiently, and adjust transmission power.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14419/djn77m90
- https://www.sciencepubco.com/index.php/IJBAS/article/download/33978/18535
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412786321
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412786321Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.14419/djn77m90Digital Object Identifier
- Title
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Deep Reinforcement Learning for Joint UAV Trajectory and Communication Design in Cache-Enabled Cellular NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-30Full publication date if available
- Authors
-
Bathula Prasanna Kumar, U. S. B. K. Mahalaxmi, V. Nagagopiraju, Ashok Kumar Manda, K Siri Chandana, Suresh Betam, Akurathi Gangadhar, Sarala PatchalaList of authors in order
- Landing page
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https://doi.org/10.14419/djn77m90Publisher landing page
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https://www.sciencepubco.com/index.php/IJBAS/article/download/33978/18535Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://www.sciencepubco.com/index.php/IJBAS/article/download/33978/18535Direct OA link when available
- Concepts
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Cache, Reinforcement learning, Computer science, Joint (building), Computer architecture, Computer network, Artificial intelligence, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Basic and Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.14419/djn77m90 |
| publication_date | 2025-07-30 |
| publication_year | 2025 |
| referenced_works_count | 0 |
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