In this paper, we investigate the reliability in an unmanned aerial vehicle (UAV) assisted caching-based downlink network where non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes are adopted. In this network, the ground user equipments (GUEs) request contents from a distant base station (BS) but there are no direct links from the BS to the GUEs. A UAV with limited cache size is employed to assist the BS to complete the communication by either first requesting the uncached contents from the BS and then serving the GUEs or directly sending the cached contents to the GUEs. In this setting, we first introduce the decoding error rate in the FBL regime as well as the caching policy at the UAV, and subsequently we construct an optimization problem aiming to minimize the maximum end-to-end decoding error rate among all GUEs under both coding length and maximum UAV transmission power constraints. A two-step alternating algorithm is proposed to solve the problem and numerical results demonstrate that our algorithm can solve the optimization problem efficiently. More specifically, loosening the FBL constraint, enlarging the cache size and having a higher transmission power budget at the UAV lead to an improved performance.
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This content will become publicly available on January 1, 2025
Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions
In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.
more » « less- Award ID(s):
- 2221875
- PAR ID:
- 10545148
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Drones
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2504-446X
- Page Range / eLocation ID:
- 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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