UAV-Assisted Enhanced Coverage and Capacity in Dynamic MU-mMIMO IoT Systems: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
This study focuses on a multi-user massive multiple-input multiple-output (MU-mMIMO) system by incorporating an unmanned aerial vehicle (UAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet-of-Things (IoT) devices. Our primary objective is to maximize the overall achievable rate (AR) by introducing a novel framework that integrates joint hybrid beamforming (HBF) and UAV localization in dynamic MU-mMIMO IoT systems. Particularly, HBF stages for BS and UAV are designed by leveraging slow time-varying angular information, whereas a deep reinforcement learning (RL) algorithm, namely deep deterministic policy gradient (DDPG) with continuous action space, is developed to train the UAV for its deployment. By using a customized reward function, the RL agent learns an optimal UAV deployment policy capable of adapting to both static and dynamic environments. The illustrative results show that the proposed DDPG-based UAV deployment (DDPG-UD) can achieve approximately 99.5% of the sum-rate capacity achieved by particle swarm optimization (PSO)-based UAV deployment (PSO-UD), while requiring a significantly reduced runtime at approximately 68.50% of that needed by PSO-UD, offering an efficient solution in dynamic MU-mMIMO environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".