Joint Transmit and Receive Beamforming Design for Joint Multi-User Interference and Self-Interference Rejection for Full-Duplex Sub-Connected MU-mMIMO
Bibliographic record
Abstract
Full-duplex (FD) radios have the potential to double the spectral efficiency of communication systems by transmitting and receiving on the same time and frequency slot. However, realizing this potential gain is challenging due to the strong selfinterference (SI) between the transmit and receive arrays. In massive MIMO (mMIMO), traditional cancellation techniques such as analog cancellation become unfeasible due to the required hardware complexity. The large number of antenna elements in mMIMO enables spatial processing techniques, making spatial SI suppression a promising approach for FD communications. This paper proposes Regularized Joint Linearly Constrained Minimum Variance Beamforming (RJLCMV), a novel alternating method for spatial SI cancellation for FD subconnected multi-user mMIMO based on regularized LCMV. The beamformer design problem is not jointly-convex between the transmit and receive array beamformers and is susceptible high levels of self-nulling with standard alternating optimization algorithms. The proposed algorithm uses diminishing regularization to mitigate the self-nulling effect while still providing total SI isolation. We study the cases in which total SI rejection can be achieved and demonstrate that with a measured SI channel, a duplexing gain of 1.93 can be achieved with RJLCMV in the fully constrained case in which both multi-user interference and SI are nulled, supporting as many users as there are per antenna element in each subarray.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".