Gram-Schmidt precoding for two-tier cellular networks with massive MIMO
Bibliographic record
Abstract
The uplink in a cellular network where the base station (BS) and an advanced user equipment (UE) that is not the traditional mobile device is investigated. This is proposed as a method of antenna offloading for the typical UE which suffers from size constraints. The Gram-Schmidt (GS) precoding algorithm is used to design novel precoding solutions when the receiver and transmitter can have a large antenna array (e.g. Nr= 64, NT= 32), as expected to be the case for deploying wireless backhaul links. Using the QR-decomposition on the Zero-Forcing (ZF) receiver, the proposed scheme is shown to essentially perform channel inversion with the help of the transmitter and receiver. With a moderate number of receive antenna elements, the matched filter (MF) was observed to incur inter-stream interference, while the ZF and minimum mean squared error (MMSE) receiver were more susceptible to transmitter side correlation compared to the GS precoding algorithm. When there is only channel correlation information (CCI) at the transmitter side, the GS precoding matrix is shown to be approximated by inverting the Cholesky decomposition of the CCI transmit matrix and outperforms the MF. Finally a block successive interference cancellation (SIC) detection scheme for the K-User MIMO channel is presented. It is shown for NT= 2 the GS precoder has a higher sum rate than the ZF receiver in the high signal to noise ratio (SNR) regime.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| 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".