Spectral efficiency evaluation of full‐duplex mode of communications based on SLNR approach
Bibliographic record
Abstract
Summary Full‐duplex (FD) mode of communication with efficient transmission scheme is a promising approach for 5G wireless systems by improving the spectral efficiency. This can be attained by making use of various precoding approaches. We propose a new co‐channel interference (CCI)‐aware improvement to signal‐to‐leakage‐and‐noise ratio (SLNR) technique and a suppression filter at the receiver to whiten the interference for the downlink channel. As well, for the uplink (UL) communication, we propose a self‐interference (SI)‐aware enhancement to SLNR scheme and designing a precoder using self‐interference plus noise covariance matrix. The total spectral efficiency is obtained from the sum‐rates of both downlink and uplink communication systems. Simulation results verify that the spectral efficiency (SE) of FD using the proposed scheme performs well relative to the half‐duplex system for all Rician factor and for small powers at the base station (BS) and UL communication channel users. Moreover, as the number of users grows, which entails that as the number of receiving antennas greater than the number of antennas at the BS the SLNR scheme still works, nonetheless, zero‐forcing (ZF) and block‐diagonalization (BD) precoding schemes failed. This is due to the fact that designing a precoder based on SLNR scheme supports multiple numbers of antennas at the base station and users compared with ZF and BD by compromising the interference and noise. However, for the cases of ZF and BD approaches failed due to both schemes require the number of transmit antennas at the BS to be larger than the sum of the receiving antennas at all users.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".