Frameworks for Energy Efficiency Maximization in HetNets With Millimeter Wave Backhaul Links
Bibliographic record
Abstract
Heterogeneous networks (HetNets) and millimeter wave (mmWave) communications have been recognized as two of the most promising techniques for future cellular networks. HetNets possess the ability to significantly increase network capacity and coverage, while the mmWave bands have an abundant spectrum to support gigabit-per-second data transmission for backhauling. Due to the extreme pathloss and the unreliable transmission of mmWave signals over longer distances, multi-hop mmWave transmissions have been identified as a backhaul (BH) solution in HetNets. On the other hand, energy efficiency (EE) has been identified as a prime design factor for cellular networks because of their rising energy costs. In this paper, two optimization frameworks for maximizing the EE of HetNets with multi-hop mmWave BH links are explored. The first framework, referred to as joint EE, power, and flow control (JEEPF), considers enforcing a strict throughput requirement on all user equipment (UEs) and maximizing the network EE via the joint optimization of power and BH flows. The second framework, referred to as joint EE, power, flow, and throughput (JEEPFT), allows an acceptable range of throughput requirements for each UE and maximizes the network EE via the joint optimization of power, BH flows, and UEs' achievable throughputs. It is observed that this little change (i.e., strict vs. an acceptable range of throughput requirements) causes a drastic difference in the formulations of both problems. The JEEPF simplifies to power minimization problem (which is convex), while the JEEPFT is a ratio of throughput to power (which is fractional and non-convex). Two solution techniques that obtain the optimal solution are proposed for the JEEPFT optimization framework. Simulation results are used to demonstrate the superiority of the JEEPFT framework over the JEEPF and other simple benchmark schemes. The computational complexity of the JEEPFT solution techniques is discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".