Full-Duplex Cell-Free Massive MIMO
Bibliographic record
Abstract
This work studies a novel full-duplex (FD) cell-free massive multiple-input multiple-output (MIMO) network, where a very large number of multiple-antenna access points (APs) simultaneously serve many single-antenna uplink and downlink users in the same frequency band. The APs operate in the FD mode while the users in the half-duplex (HD) mode. The APs apply a simple conjugate beamforming/matched filtering scheme with the channel state information acquired via the uplink training with orthogonal pilots transmitted from the users. By an analysis with a large number of APs, residual self-interference (RI) is proved to be the main limitation of the cell-free massive MIMO systems. A simple power control method to mitigate this limitation is also proposed. The closedform expressions of uplink and downlink achievable rates are derived with a finite number of APs and the channel estimation error taken into account. Under considered parameter settings, numerical results show that when the RI is sufficiently low, the FD mode can achieve a spectral efficiency gain of 140% over the HD mode in the cell-free massive MIMO system. They also confirm that the FD cell-free massive MIMO systems outperform the FD collocated massive MIMO systems in terms of spectral efficiency.
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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 | 0.000 | 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".