Spectral-Energy Efficiency and Power Allocation in Full-Duplex Networks: The Effects of Hardware Impairment and Nakagami-$m$ Fading Channels
Bibliographic record
Abstract
Full-duplex (FD) systems have emerged as game-changers for the future of wireless communication thanks to their ability to increase spectral efficiency (SE) and energy efficiency (EE). In this article, we study the effect of hardware impairments (HWIs) on Multiple-input Multiple-output (MIMO) FD systems. We derived closed-form expressions for the lower bounds of the average UL and DL achievable rates. We formulate different power allocation optimization problems to maximize the average FD SE and EE, while satisfying the quality of service (QoS) and power budget constraints. Moreover, we consider the max-min objective functions to assure fairness between users. These problems are solved using different optimization techniques, including the Dinkelbach approach, transformation, and the Karush–Kuhn–Tucker (KKT) conditions. We also refine the SE algorithm and present a simpler solution. Finally, we assume that all fading channels follow Nakagami-$m$distributions, where other scenarios can be considered special cases. Extensive simulations were performed to validate the presented analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".