Improved transmit null steering for MIMO-OFDM downlinks with distributed base station antenna arrays
Bibliographic record
Abstract
Space-division multiple-access (SDMA) is a communication technique that enables a base station to communicate with several mobile users simultaneously. The ability of the base station to spatially separate several users depends on the pairwise cross correlations between the channel matrices of the users (the inter-user correlation). In this paper, we propose an improved null steering downlink multiple-input-multiple-output-orthogonal frequency-division multiplexing (OFDM) system that reduces both the inter-user correlation and the near-far problem resulting in a significant enhancement in system performance. In this system, several base station multiantenna arrays are distributed in a given area. Each array communicates with the base station via optical fiber links, and all transmitter signal processing is performed at the base station. Multiantenna users are spatially separated such that only a subset of the users is served by each tone of the OFDM symbol. The served users are selected based on an algorithm that reduces the inter-user correlations. Distributing the arrays around the users also balances the channel matrix leading to significant reduction in the effect of the near-far problem. The channel matrix of each user is assumed correlated and Ricean distributed. Several data symbols can be spatially multiplexed to each user over each OFDM tone with high reliability and with good total system capacity.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".