Full Duplex Transmit and Receive Beamforming With Block-Sparse Antenna Selection for Multi-User Massive MIMO
Bibliographic record
Abstract
Through simultaneous downlink and uplink transmission on the same frequency slot, in-band full duplex has the potential to double the spectral efficiency of communication systems, however the potential is difficult to realize due to the strong self interference (SI). The great number of antenna elements in massive MIMO has made spatial SI suppression a promising solution to SI suppression but this approach is challenged by the coupling of the transmit and receive beamforming problems. This paper applies a combined beamforming and reduced connectivity antenna selection approach to suppress SI while maintaining user directivity and reduce switching complexity. To solve the non-convex beamforming problem, Regularized Joint Linearly Constrained Minimum Variance (RJLCMV) is proposed which leverages disappearing regularization to provide deep SI nulling while avoiding the self-nulling problem. To solve the non-convex joint group antenna selection, we pose the problem as a block-sparse recovery problem and propose Hard-Thresholding Pursuit-based Joint Group Antenna Selection (HTPJGAS), an iterative method based on compressed sensing. Using measured SI channel data, RJLCMV decreases the probability of deep self-nulling by 49% compared to a standard alternating approach. By leveraging HTP-JGAS with RJLCMV, the probability of deep nulling is nearly eliminated compared to a sub-connected approach while the run-time is over two orders of magnitude faster than existing nature inspired approaches. Furthermore, it is demonstrated that the proposed partial switching connectivity does not substantially reduce performance while providing a great reduction in hardware complexity.
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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 | 0.000 | 0.001 |
| 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".