GFDM-Modulated Full-Duplex Cognitive Radio Networks in the Presence of RF Impairments
Bibliographic record
Abstract
This paper investigates the problem of sum rate maximization for a full-duplex (FD) generalized frequency division multiplexing (GFDM) based secondary user (SU) link, operating in a spectrum hole. The right and left adjacent channels of the spectrum hole have two active primary users (PUs), and thus adjacent channel interference (ACI) on them must be below a threshold. For SU link, radio frequency (RF) impairments including phase noise, in-phase (I) and quadrature (Q) imbalance, and carrier frequency offset (CFO) are considered and analog domain and digital domain self-interference (SI) cancellation techniques are applied. We consider two cases: (1) two independent oscillators for local transmitter and receiver, (2) single shared oscillator between them. We derive the powers of residual SI, desired signal and noise and signal-to-interference-plus noise ratio (SINR). Furthermore, power spectral density (PSD) of FD transmitter is calculated and ACI is formulated. By using successive convex approximation, sum rate maximization problem subject to ACI limits on adjacent PUs is defined and solved. Finally, we show that FD GFDM for the SU link can achieve twice higher sum rate than FD orthogonal frequency division multiplexing (OFDM).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".