Iterative Successive Nonlinear Self-Interference Cancellation for In-Band Full-Duplex Communications
Bibliographic record
Abstract
In-band full-duplex (IBFD) communications have recently been considered for wireless backhaul in the ultra-high frequency (UHF) band terrestrial broadcast systems since they can double the spectral efficiency compared to conventional half-duplex communications. The inherent challenge of IBFD communication is self-interference (SI), the power leakage from the co-located transmitter to the receiver. For wireless backhaul among transmitter towers, it’s desirable to employ high-order modulation, e.g., 1024QAM or 4096QAM, for higher spectral efficiency. Furthermore, the transmitter emission power is much higher in low radio frequency (RF) bands. Due to the nonlinearity of the transmitter high-power amplifier (HPA), the nonlinear distorted SI becomes a performance-limiting impairment and must be effectively mitigated. Moreover, the SI channel exhibits a large multipath delay spread in these applications, especially in UHF-band terrestrial broadcasting systems where the transmission frequency is lower and the antenna directivity is limited. In this paper, a novel iterative successive nonlinear SI cancellation scheme, based on the previously proposed frequency-domain RF self-interference cancellation (RF-SIC) technique, is presented. The baseline RF-SIC is capable of cancelling SI with a large multipath delay spread. However, the RF-SIC performance is limited by the presence of the remote signal of interest (SOI) in the received signal. This SOI presence, referred to as “intrinsic noise” in the SI channel estimation process, occurs due to foregoing the training phase requirement. The proposed approach in this paper iteratively cancels the “intrinsic noise” and can suppress the nonlinear SI to the receiver noise floor. The proposed technique can be applied to 3rd Generation Partnership Project (3GPP) Integrated Access and Backhaul (IAB) technology to make it more feasible for deployment at the lower frequency band.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".