Energy Efficiency and Throughput Maximization Using Millimeter Waves–Microwaves HetNets
Bibliographic record
Abstract
The deployment of millimeter waves can fulfil the stringent requirements of high bandwidth and high energy efficiency in fifth generation (5G) networks. Still, millimeter waves communication is challenging because it requires line of sight (LOS). The heterogeneous network (HetNet) of millimeter waves and microwaves solves this problem. This paper proposes a millimeter -microwaves heterogeneous HetNet deployed in an indoor factory (InF). In InF, the manufacturing and production are performed inside big and small halls. We consider non standalone dual-mode base stations (DMBS) working on millimeter waves and microwaves. We analyze the network in terms of throughput and energy efficiency (EE). We formulate mixed-integer-non-linear-programming (MINLP) to maximize the throughput and EE of the network. The formulated problem is a complex optimization problem and hard to solve with exhaustive search. We propose a novel outer approximation algorithm (OAA) to solve this problem, and the proposed algorithm OAA achieves optimal solution at β = 10−3. At this β, the average throughput value obtained is approximately 50 Mbps, whereas the value of EE is 4.4 Mbits/J. We also compare the performance of OAA with the mesh-adaptive-direct-search-algorithm (NOMAD), and the experimental results verify that OAA outperforms NOMAD in terms of throughput and EE maximization. We also compare the performance of OAA with particle swarm optimization (PSO), genetic algorithm (GA), and many others optimization algorithms. Experimental results verify that OAA outperforms all other algorithms.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".