Achieving Capacity Gains in Practical Full-Duplex Massive MIMO Systems: A Multi-Objective Optimization Approach Using Hybrid Beamforming
Bibliographic record
Abstract
This paper presents a novel approach to full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems using hybrid beamforming (HBF) architecture, enabling simultaneous uplink (UL) and downlink (DL) transmission within the same frequency band. The proposed solution aims to mitigate strong self-interference (SI) and maximize the total achievable rate based on over-the-air (OTA) measurements of the SI channel. Our objective is to leverage the spatial degrees of freedom (DoF) in mMIMO systems to enhance FD capacity without the need for expensive analog SI-cancellation circuitry. To address this challenging issue, we employ a sub-array configuration for transmit and receive antennas at the base station (BS) and design the RF stages using non-orthogonal beamforming (NOBF) in both UL and DL user directions. Additionally, sub-array selection (SAS) is utilized to identify the optimal Tx-Rx antenna pair. To solve the non-convex multi-objective optimization problem (MOOP), we propose a swarm intelligence-based algorithmic solution to determine the optimal perturbations in user directions jointly with Tx-Rx sub-array indices while satisfying directivity degradation constraints. The illustrative results show that the proposed NOBF scheme with SAS can achieve an SI suppression of -78 dB. Furthermore, in FD mMIMO systems, this approach can effectively double the capacity compared to half-duplex (HD) transmissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".