THz Network Placement and Mobility-Aware Resource Allocation for Indoor Hybrid THz/VLC Wireless Networks
Bibliographic record
Abstract
This paper focuses on the energy and spectral efficient design of an indoor communication system that leverages terahertz (THz) and visible light communication (VLC). We first optimize THz access points (APs) deployment in an indoor environment equipped with VLC APs such that uniform data rate can be guaranteed for all arbitrarily located users in the room. We discretize the placement problem and then apply fractional programming techniques and a Majorization-Minimization (MM) approach to solve it. Then, we develop a novel mobility-aware resource allocation framework to optimize user-AP assignment, subchannel allocation (SA), and power allocation (PA) to maximize the handoff (HO)-aware sum rate and energy efficiency (EE) of hybrid THz/VLC networks. The HO-aware sum rate and EE adapts according to the HOs experienced by users. The proposed framework constrains users’ quality-of-service demands, transmit power budgets, molecular absorption loss thresholds, illumination requirements, and minimum electromagnetic field exposure. This joint problem is a mixed integer nonlinear programming problem which is generally intractable and mostly solved by decomposing into multiple sub-problems via alternating optimization. Different from the traditional approach, we cast this problem as a multi-objective optimization problem and obtain a solution that jointly optimizes all variables using quadratic optimization and MM approach. Computational complexity analysis is presented for both solutions. The proposed placement solution is much faster than the optimal solution. Moreover, the time complexity does not increase with augmenting the number of THz APs. Also, the proposed mobility-aware joint resource allocation solution significantly outperforms the existing benchmarks.
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".