Energy-Efficient 5G Integrated Access and Backhaul Open RAN-Based Fixed Wireless Access Provisioning in Rural Areas
Bibliographic record
Abstract
Fixed Wireless Access (FWA) has emerged as a promising solution for providing Internet access to rural areas where fiber deployment is not economically feasible. Recently, 5G-based FWA has been demonstrated to offer high coverage and capacity using mid and high-frequency bands. However, these bands require more radio units to increase capacity and coverage. Additionally, 5G-based FWA necessitates the deployment of edge clouds for proximity computation and hosting network functions. However, deploying more radios and leveraging edge cloud infrastructure can increase the network’s energy consumption. Therefore, such 5G-based FWA should be designed and powered by renewable energy to be sustainable and cost-efficient in rural areas. To address these challenges, we propose energy-efficient 5G Integrated Access and Backhaul Open Radio Access Network-based FWA (5G IAB and Open RAN-based FWA) serving rural areas, where Open RAN supports the interoperability of 5G-based FWA elements. We design an energy model that leverages the power grid and renewable energy to serve IAB and Open RAN-based FWA.We join the communication model with the energy model. Then, we formulate a joint optimization problem to minimize energy consumption while maximizing communication utility. We propose concave programming and disciplined convex programming as solutions. The results show that our approach can increase network and energy utility by leveraging renewable energy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".