Joint Optimization of IRS and THz Resource Allocation in 6G IoT Networks: An Adaptive Online MADDPG Approach
Bibliographic record
Abstract
The convergence of Intelligent Reflecting Surfaces (IRS) and Terahertz (THz) communications represents a transformative advancement for sixth-generation (6G) wireless networks, yet presents unprecedented challenges in system optimization. This paper addresses the critical challenge of joint optimization between IRS phase shifts and THz resource allocation in dynamic Internet of Things (IoT) environments, focusing on real-time adaptation to rapidly changing channel conditions. We propose a novel Adaptive Online Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that leverages dynamic experience weighting to automatically adjust learning based on detected environmental changes. Our approach incorporates a multi-resolution buffer structure that balances recent observations with historical patterns, enabling both rapid adaptation and long-term optimization while considering the unique characteristics of THz-band propagation and IRS reflection patterns. The framework employs explicit coordination protocols between IRS controllers and resource managers, significantly improving convergence in non-stationary environments. Comprehensive simulations using realistic THz channel models and practical IRS configurations demonstrate that our proposed framework achieves a 45% improvement in system throughput, a 38% reduction in end-to-end latency, and a 30% enhancement in energy efficiency compared to conventional optimization approaches. More significantly, our solution demonstrates unprecedented adaptation capabilities, recovering 90% of optimal performance within 5 ms after abrupt environmental changes a critical requirement for future 6G networks. The framework maintains robust performance under diverse conditions, including high user mobility scenarios and adverse atmospheric conditions, while exhibiting linear computational scaling with increasing IRS elements (tested up to 512 elements). These results establish the viability of Adaptive Online MADDPG-based joint IRS-THz optimization for practical 6G deployments, particularly in dynamic IoT environments where traditional communication approaches face significant limitations.
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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".