AAV Deployment in IoT Networks: A Codebook-Based Reinforcement Learning Approach
Bibliographic record
Abstract
This study explores a multiuser massive multiple-input-multiple-output (MU-mMIMO) system that incorporates an autonomous aerial vehicle (AAV) as a decode-and-forward (DF) relay between the base station (BS) and multiple Internet of Things (IoT) devices. The primary goal is to maximize the overall achievable rate (AR) by introducing a novel framework that integrates joint hybrid beamforming (HBF) with AAV deployment in dynamic MU-mMIMO IoT systems. Specifically, considering the geometry-based millimeter-wave (mmWave) channel model for both links, the radio frequency (RF) stages are configured to minimize the number of RF chains by utilizing slow time-varying angular information, while the baseband (BB) stages are developed using reduced-dimension effective channel matrices. Subsequently, deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm with continuous action space, is developed to train the AAV for its deployment. By employing a customized reward function, the RL agent learns an optimal AAV deployment policy capable of adapting to both static and dynamic environments. Then, a novel low-complexity DDPG codebook-based AAV deployment (DDPG-C-AD) is proposed, consisting of an offline agent training phase, and an online AAV deployment prediction to achieve maximum AR. The illustrative results show that the proposed DDPG-C-AD can attain a performance close to the DDPG-based solution in both static and dynamic environments while reducing the runtime by 99%. This makes the DDPG codebook-based solution a promising implementation for real-time online applications in AAV-assisted MU-mMIMO IoT systems.
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".