Underlaid FD D2D Communications in Massive MIMO Systems via Joint\n Beamforming and Power Allocation
Bibliographic record
Abstract
This paper studies the benefits of incorporating underlaid full-duplex (FD)\ndevice-to-device (D2D) communications into massive\nmultiple-input-multiple-output (MIMO) downlink systems. Due to the nature of\ncellular downlink and FD D2D transmission, the performances of cellular and D2D\nservices are severely impaired due to high interference caused by base-stations\n(BSs) and D2D transceivers. As a consequence, integrating a large number of D2D\nlinks into exiting cellular networks might degrade the system performances. To\novercome this challenge, utilizing the large uniform linear array (ULA)\nequipped at BSs, we propose a joint beamforming and power allocation design for\naverage sum-rate maximization while considering the effects of interference to\nboth cellular and D2D transmission. The problem formulation leads to a\nnonconvex vector-variable optimization problem, where we develop an efficient\nsolution using a fractional programming (FP) based approach. Numerical results\nshow that, at sufficiently high self-interference cancellation (SIC) levels and\nnumbers of active D2D links, the FD D2D transmission provides a significant\nsum-rate improvement as compared to the half-duplex (HD) counterpart and pure\ncellular systems in absence of D2D.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".