Joint MSE-based hybrid precoder and equalizer design for full-duplex massive MIMO systems
Bibliographic record
Abstract
In this paper, we study joint design of linear hybrid precoding and equalization for full-duplex (FD) massive multiple-input multiple-output (MIMO) systems such that the sum mean squared error is minimized across all mobile stations. To better resolve practical issues such as hardware complexity, power consumption, and overhead of channel estimation, hybrid processing, which consists of digital processing in the baseband and radio frequency (RF) analog processing, is employed at the base station for simultaneous transmission and reception. In particular, baseband processing is adjusted according to instantaneous channel variation while RF processing is only updated based on such long-term channel statistics as transmit and receive correlation. In the presence of self-interference (SI) and co-channel interference, joint power optimization is carried out in order to achieve balanced performance for both the uplink and the downlink. As demonstrated by numerical results, FD is able to outperform half-duplex under realistic SI. Furthermore, the employment of the proposed hybrid processing structure is justified by its near optimal performance when equipped with even only a small number of RF chains.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".