Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/32448
Title: | POWER ALLOCATIONS FOR DELAY-CONSTRAINED TASK OFFLOADING IN CELL-FREE WIRELESS NETWORKS |
Authors: | Mahmuda, Subaha |
Advisor: | Zhao, Dongmei |
Department: | Electrical and Computer Engineering |
Keywords: | power allocations, task offloading, Cell-Free wireless network |
Publication Date: | Nov-2025 |
Abstract: | This thesis addresses the uplink resource allocation problem in a cell-free (CF) environment, where mobile devices (MDs) periodically offload application data for processing to a shared edge server (ES) co-located at the central unit (CU). These user applications require timely processing at the ES, while the uncertain and time-varying wireless transmission conditions, combined with the limited battery energy of the MDs, make this difficult. To tackle this challenge, we propose a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced with Transformer encoders in both the actor and critic networks. The resulting Transformer-based DDPG (T-DDPG) framework captures spatial-temporal dependencies in dynamic wireless conditions to make more informed decisions. Simulations are conducted under varying numbers of MDs and task sizes. The proposed T-DDPG consistently outperforms conventional DDPG, achieving both lower task completion time and energy consumption, while improving the rate of task completion within deadlines. These results highlight the effectiveness of spatial-temporal policy learning for real-time uplink scheduling in CF edge computing systems. |
URI: | http://hdl.handle.net/11375/32448 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
mahmuda_subaha_finalsubmission_202509_masc.pdf | 748.59 kB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.