Spatial Reuse Precoding for Scalable Downlink Networks
Bibliographic record
Abstract
In this paper, we develop linear precoding schemes for MIMO downlink networks with quasi-static channels that are scalable, in the sense that they can be implemented with moderate complexity in networks with increasing numbers of cells and users. The principle that underlies the proposed precoding scheme is to exploit the decomposable structure of the equivalent channel matrix by designing the precoders at the base stations to be decomposable as well. Thus, the channel matrices and the designed precoders can be expressed as the Kronecker product of constituent matrices. For networks with a finite number of cells, the proposed structured precoding schemes enable inter-cell interference cancellation without the need for inter-cell feedback and provide more degrees of freedom than conventional interference avoidance schemes, while incurring a latency that grows only linearly in the number of cells. The proposed structured precoding schemes also enable a scaling approach for unbounded networks that we have called “spatial reuse precoding” (SRP). SRP is based on the observation that at each receiver, the signals from interfering sources that employ the same precoder arrive in the same subspace, regardless of the particular channel matrices between the interfering sources and the receiver. In some typical cellular architectures we show how an SRP scheme based on the proposed structured precoders can be designed to eliminate the dominant sources of interference without requiring cooperation between cells. In addition, we show that SRP can provide substantial performance gains in certain heterogeneous networks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".