Energy-efficient downlink transmission in two-tier network MIMO OFDMA networks
Bibliographic record
Abstract
We propose an energy-efficient resource allocation scheme for downlink transmission in two-tier Network MIMO OFDMA-based macrocell-femtocell networks where the femto-cells form clusters of equal size. The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission at each network tier to perform allocation of subcarrier and precoding coefficients. Then, power allocation is optimized in order to maximize the total system energy efficiency (i.e., average number of successfully transmitted bits per energy unit [bit/Joule], or equivalently, the average data rate per unit power [bps/Watt]). The macro base stations (MBSs) and the femto base stations (FBSs) in a cluster maximize their energy efficiency in a distributed manner while considering the cross-tier interference and the capacity limitations of backhaul links. The problem of maximizing energy efficiency is formulated as a fractional program and solved by using the Dinkelbach iterative algorithm. Numerical results show that the proposed scheme outperforms the scheme that maximizes the system average capacity, in terms of energy efficiency, and also improves the total system performance in terms of energy efficiency and average system capacity when compared to a single-tier system.
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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 | 0.000 | 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".