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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".