Joint Beamforming and Sub-Array Selection for MU-FD-mMIMO
Bibliographic record
Abstract
This work considers a novel joint self-interference (SI) and multi-user interference (MUI) suppression with a subarray selection scheme in a full-duplex (FD) multi-user (MU) massive multiple-input multiple-output (mMIMO) system using sub-connected hybrid beamforming (SC-HBF) architecture, which offers lower complexity and cost compared to fullyconnected architectures. The main goal is to reduce the strong SI by formulating RF beamforming stages for both uplink (UL) and downlink (DL). These stages leverage the spatial degrees of freedom offered by large array structures while also mitigating multi-user interference (MUI) in a FD environment. We consider multiple uplink and multiple downlink UE’s and formulate a joint optimization problem, which considers SI suppression by using a particle swarm optimization (PSO)-based algorithmic solution that introduces beam perturbation (BP) jointly with null space projection (NSP) and Tx/Rx sub-array selection (SAS). The illustrative results show that the joint BP, SAS, and null space projection scheme can suppress SI by around 91 dB in the FD-MU-mMIMO system.
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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 | 0.000 |
| 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".