On the Resource Allocation in HetNets with Massive MIMO Wireless Backhaul
Bibliographic record
Abstract
This paper proposes a new transmission technique for heterogeneous networks with massive MIMO wireless back-haul with the objective of minimizing the power consumption cost. We assume that transmissions occur during two phases. During the first phase, the multi-antenna small-cell base stations (SBSs) receive signals from their associated users and from the macro-cell base station (MBS) thanks to MIMO spatial multiplexing. In the second phase, the SBSs transmit signals to the MBS and to the users. We study the problem of minimizing the sum SBS transmit power under minimum-rate constraints required at the users. We solve the formulated problem by deriving analytically the optimal time splitting parameter and the allocated transmit powers. Compared with the well-known reverse time division duplex (RTDD) with bandwidth splitting, considered as a benchmark, simulations show that the proposed transmission technique allows the SBSs to reduce considerably the power consumption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".