Adaptive Bandwidth Allocation in Multiuser MIMO THz Systems with Graph-Transformer Networks
Bibliographic record
Abstract
Terahertz (THz) wireless systems aim to support content-rich applications with ultra-high data rate. Due to high molecular absorption, THz signals experience severe path loss. Adaptive sub-band bandwidth (ASB) allocation can mitigate absorption attenuation by allocating THz sub-bands with variable bandwidth to the users. However, in ASB allocation, since the bandwidth of sub-bands may not be known a priori, accurate channel estimation is challenging. To overcome this issue, in this paper, we propose a heterogeneous graph-transformer network (HGTN) to bypass the channel estimation phase. We formulate a sum-rate maximization problem with quality-of-service (QoS) constraints in a multiuser multiple-input multiple-output (MU-MIMO) THz system to optimize the precoding and ASB allocation. The proposed HGTN parameterizes the mapping from input features (e.g., location information, users' minimum data rate) to the optimized system parameters via unsupervised learning. The proposed HGTN can be applied to systems with different number of users once it is trained. Simulation results show that our proposed HGTN achieves a higher system sum-rate with faster convergence when compared with the unsupervised deep neural network learning algorithm.
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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 | 0.001 |
| 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".