MIMO Full-Duplex Precoding: A Joint Beamforming and Self-Interference Cancellation Structure
Bibliographic record
Abstract
This paper presents a single- or multi-user Multiple-Input-Multiple-Output (MIMO) Full-Duplex (FD) precoding transceiver structure applicable for single-carrier and Orthogonal Frequency Division Multiplexing (OFDM) systems. The structure increases the dimensionality at the transmitter, which allow for the cancellation of self-interference and forward beamforming to be jointly processed using precoding at the transmitter. The FD Precoding (FDP) structure allows for various joint precoding algorithms and different optimization objectives. We present separate and joint precoding designs for sum-rate maximization and a theoretical analysis of when the separate design is optimal. The joint designs make use of Sequential Convex Programming (SCP). Extensive simulation results using both channel models and measured data show that the FDP structure can provide very significant performance gains over existing techniques for both SU- and MU-MIMO systems. In particular, the FDP structure provides between 1.6 and 1.8 times the spectral efficiency of optimized half-duplex for many of the tested SU-MIMO scenarios.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".