Joint Beam Steering and Nulling Angle Perturbation for Tx-Rx Isolation Enhancement in FD mMIMO
Bibliographic record
Abstract
Full-duplex (FD) communication can potentially double spectral efficiency by allowing simultaneous transmission and reception over the same frequency. However, strong self-interference (SI) between the co-located transmitter (Tx) and receiver (Rx) frequently undermines the system performance. This paper introduces a two-stage beamformer optimization strategy to improve Tx-Rx beam-level isolation. First, a neural network model, trained on a dataset generated by particle swarm optimization, predicts optimal nulling angles for both downlink and uplink beamformers based on user locations. Next, the beam steering angle is perturbed within a predefined search window to further minimize SI. Validation using measured SI channels from an FD mMIMO testbed demonstrates an average Tx-Rx beam-level isolation of 95.7 dB, representing a 29.5 dB improvement, with less than a 3 dB trade-off in desired signal gain. Across all tested user pairs, beam-level mutual coupling remains better than −66.8 dB, and 95.9% of tested beam pairs achieve over 15 dB of additional isolation. The relationship between the allowable degradation in desired gain and the achievable Tx-Rx isolation enhancement is thoroughly examined.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".